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In order to address the challenges of identifying, detecting, and tracking moving objects in video surveillance, this paper emphasizes image-based dynamic entity detection. It delves into the complexities of numerous moving objects, dense targets, and intricate backgrounds. Leveraging the You Only Look Once (YOLOv3) algorithm framework, this paper proposes improvements in image segmentation and data filtering to address these challenges. These enhancements form a novel multi-object detection algorithm based on an improved YOLOv3 framework, specifically designed for video applications. Experimental validation demonstrates the feasibility of this algorithm, with success rates exceeding 60% for videos such as "jogging", "subway", "video 1", and "video 2". Notably, the detection success rates for "jogging" and "video 1" consistently surpass 80%, indicating outstanding detection performance. Although the accuracy slightly decreases for "Bolt" and "Walking2", success rates still hover around 70%. Comparative analysis with other algorithms reveals that this method's tracking accuracy surpasses that of particle filters, Discriminative Scale Space Tracker (DSST), and Scale Adaptive Multiple Features (SAMF) algorithms, with an accuracy of 0.822. This indicates superior overall performance in target tracking. Therefore, the improved YOLOv3-based multi-object detection and tracking algorithm demonstrates robust filtering and detection capabilities in noise-resistant experiments, making it highly suitable for various detection tasks in practical applications. It can address inherent limitations such as missed detections, false positives, and imprecise localization. These improvements significantly enhance the efficiency and accuracy of target detection, providing valuable insights for researchers in the field of object detection, tracking, and recognition in video surveillance. [ABSTRACT FROM AUTHOR]

A novel algorithm has been developed and validated to isolate individual papermaking fibres in micro-computed tomographic images of paper handsheets as a first step to characterize the structure of the paper. The three-step fibre segmentation algorithm segments the papermaking fibres by (i) tracking the hollow inside the fibres via a modified connected component methodology, (ii) extracting the fibre walls using a distance transform and (iii) labelling the fibres through collapsed sections by a final refinement step. Furthermore, postprocessing algorithms have been developed to calculate the length and coarseness of the segmented fibres. The fibre segmentation algorithm is the first ever reported method for the automated segmentation of the tortuous three-dimensional morphology of papermaking fibres within microstructural images of paper handsheets. The method is not limited to papermaking fibres, but can be applied to any material consisting of tortuous and hollow fibres. [ABSTRACT FROM AUTHOR]

Swarm intelligence optimization algorithm has been proved to perform well in the field of parameter optimization. In order to further improve the performance of intelligent optimization algorithm, this paper proposes an improved and adaptive tunicate swarm algorithm (IMATSA) based on tunicate swarm algorithm (TSA). IMATSA improves TSA in the following four aspects: population diversity, local search convergence speed, jumping out of local optimal position, and balancing global and local search. Firstly, IMATSA adopts Tent map and quadratic interpolation to initialize population and enhance the diversity. Secondly, IMATSA uses Golden-Sine algorithm to accelerate the convergence of local search. Thirdly, in the process of global development, IMATSA adopts Levy flight and the improved Gauss disturbance method to adaptively improves and coordinates the ability of global development and local search. Then, this paper verifies the performance of IMATSA based on 14 benchmark functions experiment, ablation experiment, parameter optimization experiments of Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT), Wilcoxon signed rank test and image multi-threshold segmentation experiment with the performance metrics are convergence speed, convergence value, significance level P-value, Peak Signal-to-Noise Ratio (PSNR) and Standard Deviation (STD). Experimental results show that IMATSA performs better in three kinds of benchmark functions; each component of IMATSA has a positive effect on the performance; IMATSA performs better in parameter optimization experiments of SVM experiment and GBDT; there is significant difference between IMATSA and other algorithms by Wilcoxon signed rank test; in image segmentation, the performance is directly proportional to the number of thresholds, and compared with other algorithms, IMATSA has better comprehensive performance. [ABSTRACT FROM AUTHOR]

In response to the low efficiency and poor quality of current seed optimization algorithms for multi-threshold image segmentation, this paper proposes the utilization of the normal distribution in the cluster distribution mathematical model, the Levy flight mechanism, and the differential evolution algorithm to address the deficiencies of the seed optimization algorithm. The main innovation lies in applying the BBO algorithm to image multi threshold segmentation, providing a new perspective and method for image segmentation tasks. The second significant progress is the combination of Levy flight dynamics and differential evolution algorithm (DEA) to improve the BBO algorithm, thereby enhancing its performance and image segmentation quality. Therefore, a multi-threshold image segmentation model based on the optimized seed optimization algorithm is developed. The experimental results showed that on the function f1, the iteration of the improved seed optimization algorithm was 53, the Generational Distance value was 0.0020, the Inverted Generational Distance value was 0.098, and the Spacing value was 0.051. Compared with the other two algorithms, the improved seed optimization algorithm has better image segmentation performance and clearer image segmentation details. In summary, compared with existing multi-threshold image segmentation methods, the proposed multi-threshold image segmentation model based on the improved seed optimization algorithm has a better image segmentation effect and higher efficiency, can significantly improve the quality of image segmentation, has positive significance for the development of image processing technology, and also provides references for the improvement and application of optimization algorithms. [ABSTRACT FROM AUTHOR]

Aquila Optimization (AO) is a recently proposed meta-heuristic algorithm, which has been proved to be more competitive than other meta-heuristic algorithms in function optimization and practical applications. However, when solving more complex optimization problems, AO still has the shortcomings of local optimal stagnation and low solving accuracy. To overcome these shortcomings, an improved Aquila Optimization algorithm (IAO) is proposed in this paper. During the initialization of IAO population, a hybrid chaotic mapping mechanism was introduced to initialize the population, improving both the population diversity and the uniformity of the population distribution. The elite dimensional lens imaging learning strategy is introduced for elite individual to improve the optimization quality of the algorithm as elite individual has more useful information than ordinary individuals. Then the probabilistic jump mechanism of simulated annealing algorithm is used to select the position update mode to balance local development and global search. The experimental results on the CEC2005 test function verify the viability and effectiveness of IAO. IAO is used to the multi-threshold segmentation problem based on symmetric cross entropy to demonstrate its capacity to resolve practical optimization problems. The segmentation performance on different reference images shows that IAO has good segmentation performance in most cases. [ABSTRACT FROM AUTHOR]

In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies. [ABSTRACT FROM AUTHOR]

To solve the shortcomings of the Otsu image segmentation algorithm based on traditional Moth–Flame Optimization (MFO), such as its poor segmentation accuracy, slow convergence, and tendency to fall into local optimum, this paper proposes fractional order moth–flame optimization with the Otsu image segmentation algorithm. Utilizing the advantages of memorability and heritability in fractional order differentiation, the position updating of moths is controlled by fractional order. Using the adaptive fractional order, the positions of moths are used to adjust the fractional order adaptively to improve the convergence speed. Combining the improved MFO algorithm with the two-dimensional Otsu algorithm, the optimization objective function is achieved by using its dispersion matrix. The experimental results indicate that, compared with traditional MFO, the convergence rate of the proposed algorithm is improved by about 74.62%. Furthermore, it has better segmentation accuracy and a higher fitness value than traditional MFO. [ABSTRACT FROM AUTHOR]

In recent years, UNet and its derivative networks have gained widespread recognition as major methods of medical image segmentation. However, networks like UNet often struggle with Point-of-Care (POC) healthcare applications due to their high number of parameters and computational complexity. To tackle these challenges, this paper introduces an efficient network designed for medical image segmentation called MCU-Net, which leverages ConvNeXt to enhance UNet. 1) Based on ConvNeXt, MCU-Net proposes the MCU Block, which employs techniques such as large kernel convolution, depth-wise separable convolution, and an inverted bottleneck design. To ensure stable segmentation performance, it also integrates global response normalization (GRN) layers and Gaussian Error Linear Unit (GELU) activation functions. 2) Additionally, MCU-Net introduces an enhanced Multi-Scale Convolution Attention (MSCA) module after the original UNet's skip connections, emphasizing medical image features and capturing semantic insights across multiple scales. 3)The downsampling process replaces pooling layers with convolutions, and both upsampling and downsampling stages incorporate batch normalization (BN) layers to enhance model stability during training. The experimental results demonstrate that MCU-Net, with a parameter count of 2.19 million and computational complexity of 19.73 FLOPs, outperforms other segmentation models. The overall performance of MCU-Net in medical image segmentation surpasses that of other models, achieving a Dice score of 91.8% and mIoU of 84.7% on the GlaS dataset. When compared to UNet on the BUSI dataset, MCU-Net shows an improvement of 2% in Dice and 2.9% in mIoU. [ABSTRACT FROM AUTHOR]

In order to realize the unsupervised segmentation of subtle defect images on the surface of small magnetic rings and improve the segmentation accuracy and computational efficiency, here, an adaptive threshold segmentation method is proposed based on the improved multi-scale and multi-directional 2D-Gabor filter bank. Firstly, the improved multi-scale and multi-directional 2D-Gabor filter bank was used to filter and reduce the noise on the defect image, suppress the noise pollution inside the target area and the background area, and enhance the difference between the magnetic ring defect and the background. Secondly, this study analyzed the grayscale statistical characteristics of the processed image; the segmentation threshold was constructed according to the gray statistical law of the image; and the adaptive segmentation of subtle defect images on the surface of small magnetic rings was realized. Finally, a classifier based on a BP neural network is designed to classify the scar images and crack images determined by different threshold segmentation methods. The classification accuracies of the iterative method, the OTSU method, the maximum entropy method, and the adaptive threshold segmentation method are, respectively, 85%, 87.5%, 95%, and 97.5%. The adaptive threshold segmentation method proposed in this paper has the highest classification accuracy. Through verification and comparison, the proposed algorithm can segment defects quickly and accurately and suppress noise interference effectively. It is better than other traditional image threshold segmentation methods, validated by both segmentation accuracy and computational efficiency. At the same time, the real-time performance of our algorithm was performed on the advanced SEED-DVS8168 platform. [ABSTRACT FROM AUTHOR]

At present, there are many semantic segmentation algorithms with excellent performance for intelligent driving vehicles, but most of them only work well on scenes with good illumination. In order to solve the problem of scene segmentation under low illumination, this paper proposes a novel semantic segmentation algorithm that combines visible and infrared images. In this algorithm, two parallel encoders are designed as the input of the images, and the decoder divides the fused images output from the encoder. The model is based on ResNet algorithm, and the residual attention module is used in each branch to mine and enhance the spatial features of multilevel channels to extract images information. Experiments are carried out on publicly available thermal infrared and visible datasets. The results show that the algorithm proposed in this paper is superior to the algorithm using only visible images in semantic segmentation of traffic environment. [ABSTRACT FROM AUTHOR]

With the sustainable development of intelligent fisheries, accurate underwater fish segmentation is a key step toward intelligently obtaining fish morphology data. However, the blurred, distorted and low-contrast features of fish images in underwater scenes affect the improvement in fish segmentation accuracy. To solve these problems, this paper proposes a method of underwater fish segmentation based on an improved PSPNet network (IST-PSPNet). First, in the feature extraction stage, to fully perceive features and context information of different scales, we propose an iterative attention feature fusion mechanism, which realizes the depth mining of fish features of different scales and the full perception of context information. Then, a SoftPool pooling method based on fast index weighted activation is used to reduce the numbers of parameters and computations while retaining more feature information, which improves segmentation accuracy and efficiency. Finally, a triad attention mechanism module, triplet attention (TA), is added to the different scale features in the golden tower pool module so that the space attention can focus more on the specific position of the fish body features in the channel through cross-dimensional interaction to suppress the fuzzy distortion caused by background interference in underwater scenes. Additionally, the parameter-sharing strategy is used in this process to make different scale features share the same learning weight parameters and further reduce the numbers of parameters and calculations. The experimental results show that the method presented in this paper yielded better results for the DeepFish underwater fish image dataset than other methods, with 91.56% for the Miou, 46.68 M for Params and 40.27 G for GFLOPS. In the underwater fish segmentation task, the method improved the segmentation accuracy of fish with similar colors and water quality backgrounds, improved fuzziness and small size and made the edge location of fish clearer. [ABSTRACT FROM AUTHOR]

This paper proposes a road crack detection algorithm based on an improved SparseInst network, called the SparseInst-CDSM algorithm, aimed at solving the problems of low recognition accuracy and poor real-time detection of existing algorithms. The algorithm introduces the CBAM module, DCNv2 convolution, SPM strip pooling module, MPM mixed pooling module, etc., effectively improving the integrity and accuracy of crack recognition. At the same time, the central axis skeleton of the crack is extracted using the central axis method, and the length and maximum width of the crack are calculated. In the experimental comparison under the self-built crack dataset, SparseInst-CDSM has an accuracy of 93.66%, a precision of 67.35%, a recall of 66.72%, and an IoU of 84.74%, all higher than mainstream segmentation models such as Mask-RCNN and SOLO that were compared, reflecting the superiority of the algorithm proposed in this paper. The comparison results of actual measurements show that the algorithm error is within 10%, indicating that it has high effectiveness and practicality. [ABSTRACT FROM AUTHOR]

The key step in the intelligence of tongue diagnosis is the segmentation of the tongue image, and the accuracy of the segmented edges has a significant impact on the subsequent medical judgment. Deep learning can predict the class of pixel points to achieve pixel-level segmentation of images, so it can be used to handle tongue segmentation tasks. However, different models have different segmentation effects, and they did not learn the connection between space and channels, resulting in inaccurate tongue segmentation. This paper first discussed the choice of model and loss function and then compared the results of different options to find the better model. Associating the red feature of the tongue is very conducive to segmentation as a feature, this paper tested many methods to try to get the color features of the original image to be paid attention to. Finally, this paper proposed an improved Encoder-Decoder network model to solve the problem based on the results. Start with Resnet as the backbone network, then introduce the U-Net model, and then we fused the attention layer, obtained from the source image through convolution and CBAM attention mechanism, and the feature layer obtained from the last upsampling in U-Net. Experimental results show that: The new, improved algorithm results are 2-3 percentage points higher than the popular algorithm, making it more suitable for tongue segmentation tasks. [ABSTRACT FROM AUTHOR]

With the continuous progress of Internet technology, so there is a higher demand for English translation. In this context, this paper realizes the design and improvement of the online English translation system by combining the image segmentation algorithm. After the experimental test of the algorithm of this system, if there is a lot of salt-and-pepper noise in the target image in the actual system application process, the method designed in this paper can reflect a better processing effect. Based on the analysis of performance parameters, this method can effectively process Gaussian noise images with high precision. After the completion of the system design, this paper tests the system. From the test and operation results, the software has reached the design objectives and completed the work tasks. The English translation online teaching system can form a safe, stable and high-bandwidth English translation teaching network and can realize the connection of the whole network. Users can share various software and hardware resources, completed the design of a translation platform that can realize rapid English communication, share experience and discuss, to effectively improve the learner's initiative. In this paper, an effective online teaching system is designed by introducing image segmentation algorithm into the field of English translation. [ABSTRACT FROM AUTHOR]

For solving the problem of quality detection in the production and processing of stuffed food, this paper suggests a small neighborhood clustering algorithm to segment the frozen dumpling image on the conveyor belt, which can effectively improve the qualified rate of food quality. This method builds feature vectors by obtaining the image's attribute parameters. The image is segmented by a distance function between categories using a small neighborhood clustering algorithm based on sample feature vectors to calculate the cluster centers. Moreover, this paper gives the selection of optimal segmentation points and sampling rate, calculates the optimal sampling rate, suggests a search method for optimal sampling rate, as well as a validity judgment function for segmentation. Optimized small neighborhood clustering (OSNC) algorithm uses the fast frozen dumpling image as a sample for continuous image target segmentation experiments. The experimental results show the accuracy of defect detection of OSNC algorithm is 95.9%. Compared with other existing segmentation algorithms, OSNC algorithm has stronger anti-interference ability, faster segmentation speed as well as more efficiently saves key information ability. It can effectively improve some disadvantages of other segmentation algorithms. [ABSTRACT FROM AUTHOR]

The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining. [ABSTRACT FROM AUTHOR]

To solve the drawbacks of the Otsu image segmentation algorithm based on traditional butterfly optimization, such as slow convergence speed and poor segmentation accuracy, this paper proposes hybrid fractional-order butterfly optimization with the Otsu image segmentation algorithm. G-L-type fractional-order differentiation is combined with the algorithm's global search to improve the position-updating method, which enhances the algorithm's convergence speed and prevents it from falling into local optima. The sine-cosine algorithm is introduced in the local search step, and Caputo-type fractional-order differentiation is used to avoid the disadvantages of the sine-cosine algorithm and to improve the optimization accuracy of the algorithm. By dynamically converting the probability, the ratio of global search to local search is changed to attain high-efficiency and high-accuracy optimization. Based on the 2-D grayscale gradient distribution histogram, the trace of discrete matrices between classes is chosen as the fitness function, the best segmentation threshold is searched for, image segmentation is processed, and three categories of images are chosen to proceed with the segmentation test. The experimental results show that, compared with traditional butterfly optimization, the convergence rate of hybrid fractional-order butterfly optimization with the Otsu image segmentation algorithm is improved by about 73.38%; meanwhile, it has better segmentation accuracy than traditional butterfly optimization. [ABSTRACT FROM AUTHOR]

Aquila optimizer (AO) is an efficient meta-heuristic optimization method, which mimics the hunting style of Aquila in nature. However, the AO algorithm may suffer from immature convergence during the exploitation stage. In this paper, two strategies are elegantly employed into conventional AO, such as random opposition-based learning and nonlinear flexible jumping factor, which can efficiently enhance the performance of conventional AO. Experiments on 1 7 benchmark functions and image segmentation demonstrate the effectiveness of the proposed algorithm. Comparison with several state-of-the-art meta-heuristic optimization techniques indicates the efficacy of the developed method. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of the fuzzy c-means (FCM) algorithm sensitivity to noise when clustering image pixels. We propose in this regard an improved FCM algorithm that incorporates contextual information at the membership degrees updating stage. For that aim, we introduce two novel parameters: the contextual similarity degree and the intrinsic similarity degree which are used to estimate each pixel's nature (normal or noisy), according respectively to its context and to its specific features. Based on this estimation, we propose a modified membership degrees updating strategy that proceeds by adaptively reinforcing the assignment of a pixel to its context's cluster when this pixel is detected as noisy. Experiments performed on synthetic and real-world images proved that our approach achieves competitive performance compared to state-of-the-art FCM-based methods. [ABSTRACT FROM AUTHOR]

The main purpose of identifying and locating the retina vessels is to specify from the fundus image the various tissues of the vascular structure, which can be wide or tight. The classification of vessels in the retinal image often confronts several challenges, such as the low contrast accompanying the fundus image, the inhomogeneity of the background lighting, and the noise. Moreover, fuzzy c-means (FCM) is one of the most frequently used algorithms for medical image segmentation due to its effectiveness. Hence, many FCM method derivatives have been developed to improve their noise robustness and time-consuming. This paper aims to analyze the performance of some improved FCM algorithms to recommend the best ones for the segmentation of retinal blood vessels. Eight derivatives of FCM algorithm are detained in this study: FCM, EnFCM, SFCM, FGFCM, FRFCM, DSFCM_N, FCM_SICM and SSFCA. The performance analysis is conducted from three viewpoints: noise robustness, blood vessels segmentation performance, and time-consuming. At first, the noise robustness of improved FCM clustering algorithms is evaluated using a synthetic image degraded by various types and levels of noise. Then, the ability of the selected algorithms to segment retinal blood vessels is assessed based on images from the DRIVE and STARE databases after a pre-processing phase. Finally, the time consumption of each algorithm is measured. The experiments demonstrate that the FRFCM and DSFCM_N algorithms achieve better results in terms of noise robustness and blood vessels segmentation. Regarding the running time, the FRFCM algorithm requires less time than other algorithms in the segmentation of retinal images. The results of this study are extensively discussed, and some suggestions are proposed at the end of this paper. [ABSTRACT FROM AUTHOR]

Whale optimization algorithm is a new swarm intelligence optimization algorithm inspired by humpback whale hunting behavior, and its performance is far better than the traditional algorithms. In order to track the latest research results and promote the research in the field of swarm intelligence optimization algorithm, this paper firstly introduced the basic principle of whale optimization algorithm, briefly described its implementation steps, and compared and analyzed its pe1formance characteristics. In view of its shortcomings of slow convergence speed and low convergence accuracy, this paper summarized and analyzed the improved methods including other intelligent optimization algorithms, Levy flight strategy, chaos strategy, as well as their advantages and limitations. Then, it described its applications and effect in combinatorial optimization, image segmentation, data prediction, and so on. Finally, this paper summarized and prospected the future development trend. [ABSTRACT FROM AUTHOR]

The transformer-based U-Net network structure has gained popularity in the field of medical image segmentation. However, most networks overlook the impact of the distance between each patch on the encoding process. This paper proposes a novel GC-TransUnet for medical image segmentation. The key innovation is that it takes into account the relationships between patch blocks based on their distances, optimizing the encoding process in traditional transformer networks. This optimization results in improved encoding efficiency and reduced computational costs. Moreover, the proposed GC-TransUnet is combined with U-Net to accomplish the segmentation task. In the encoder part, the traditional vision transformer is replaced by the global context vision transformer (GC-VIT), eliminating the need for the CNN network while retaining skip connections for subsequent decoders. Experimental results demonstrate that the proposed algorithm achieves superior segmentation results compared to other algorithms when applied to medical images. [ABSTRACT FROM AUTHOR]

Multi-level thresholding image segmentation divides an image into multiple regions of interest and is a key step in image processing and image analysis. Aiming toward the problems of the low segmentation accuracy and slow convergence speed of traditional multi-level threshold image segmentation methods, in this paper, we present multi-level thresholding image segmentation based on an improved slime mould algorithm (ISMA) and symmetric cross-entropy for global optimization and image segmentation tasks. First, elite opposition-based learning (EOBL) was used to improve the quality and diversity of the initial population and accelerate the convergence speed. The adaptive probability threshold was used to adjust the selection probability of the slime mould to enhance the ability of the algorithm to jump out of the local optimum. The historical leader strategy, which selects the optimal historical information as the leader for the position update, was found to improve the convergence accuracy. Subsequently, 14 benchmark functions were used to evaluate the performance of ISMA, comparing it with other well-known algorithms in terms of the optimization accuracy, convergence speed, and significant differences. Subsequently, we tested the segmentation quality of the method proposed in this paper on eight grayscale images and compared it with other image segmentation criteria and well-known algorithms. The experimental metrics include the average fitness (mean), standard deviation (std), peak signal to noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM), which we utilized to evaluate the quality of the segmentation. The experimental results demonstrated that the improved slime mould algorithm is superior to the other compared algorithms, and multi-level thresholding image segmentation based on the improved slime mould algorithm and symmetric cross-entropy can be effectively applied to the task of multi-level threshold image segmentation. [ABSTRACT FROM AUTHOR]

Farming is an essential sustenance for the progressive population. The development of our country depends on the farmers. Plants endure by many diseases due to environmental factors. So, the farmers need to detect plant diseases at an early stage for appreciable yield. In the beginning, the observing and examining plant disease are examined physically by the expertise in the farming field, which requires a considerable measure of work/ and requires over the top handling time. Now, machine learning concepts eliminate conventional protruding and time-consuming techniques. This paper focuses on a novel method for detecting and identifying paddy leaf diseases at the early stages in Thanjavur region using radial basis function neural network (RBFNN) classifier. Further, it is optimized with salp swarm algorithm (SSA) technique. The proposed method utilizes the data from the TNAU agritech portal, IRRI knowledge bank, UCI machine learning repository databases, which have healthy and diseased images. This work illustrates four categories (Bacterial Blast, Bacterial Blight, Leaf Tungro and Brown Spot) of infected paddy images along with the normal set of images. Initially the preprocessing is performed for the acquired images then K-means segmentation algorithm segregates the image. Gray level co-occurrence matrix extracts the Texture features from the segmented image and the RBFNN classifier performs the disease classification and improves the detection accuracy by optimizing the data using SSA. The investigational results of the proposed methodology exhibit the performance in terms of accuracy of disease detection is 98.47%. However, radial basis function neural network (RBFNN) achieves the diseases detection accuracy of 97.85% and support-vector machine (SVM) classifier achieves a disease detection accuracy of 97.07%. This paper proposes a method of paddy leaf disease recognition and classification using RBFNN and salp swarm algorithm. It also suggests and identifies an image analysis by framing a set of conditions for disease affected plants. The results show that the most satisfactory outcome can be gained to verify the yield of proposed methods with least effort. [ABSTRACT FROM AUTHOR]

As we know, image colorization is widely used in computer graphics and has become a research hotspot in the field of image processing. Current image colorization technology has the phenomenon of single coloring effect and unreal color, which is too complicated to be implemented and struggled to gain popularity. In this paper, a new method based on a convolution neural network is proposed to study the reasonable coloring of human images and ensures the realism of the coloring effect and the diversity of coloring at the same time. First, this paper selects about 5000 pictures of people and plants from the Imagenet dataset and makes a small dataset containing only people and backgrounds. Secondly, in order to obtain the image segmentation results, this paper improves the U-net network and carries out three times of down sampling and three times of up-sampling. Finally, we add the expanded convolution, and use the sigmoid activation function to replace the ReLU (The Rectified Linear Unit) activation function and put the BN (Batch Normalization) before the activation function. Experimental results show that our proposed image colorization algorithm based on the deep learning scheme can reduce the training time of the network and achieve higher quality segmentation results. [ABSTRACT FROM AUTHOR]

Among the congenital coronary heart diseases, atrial septal defect constitutes the 1/3 most common type. In many cases, the affected person stays asymptomatic for the duration of the youth even having big shunts. Methodologies that may be hired for figuring out the defects are: echocardiogram, chest X-ray, electrocardiogram, cardiac catheterization, MRI, CT scan, phonocardiogram. Deep learning may be correctly utilised for the automatic estimation of the illness from the test result. The purpose of this review paper is to offer an perception into ASD, the strategies for figuring out it and the application of deep learning models for distinguishing the illness. The paper opinions diverse algorithms used for identity of defects also points out the restrictions of every algorithm. [ABSTRACT FROM AUTHOR]

Based on the theory and application, this paper discusses the optimization of art image segmentation algorithm based on FFNN (Feed Forward Neural Network). In this paper, residual units are used in the corresponding stages of encoder and decoder, and feature information of several convolution layers in each convolution stage of encoder is extracted at the same time. And the feature pyramid module is used to extract multiscale features from the feature map of the last convolution stage in the encoder. Finally, pixel by pixel additions combine the previously mentioned feature information into the corresponding layer of the decoder. Additionally, an improved weight adaptive algorithm based on feature preservation is suggested in this paper, which addresses the issue that the conventional image segmentation algorithm is noise-sensitive. The adaptive connection weight mechanism is also introduced. The accuracy and recall rates of this optimization algorithm can both reach 96.574%, according to the results of 50% cross-validation. All the segmentation performance evaluation indexes of this algorithm are higher than the existing main algorithms. Moreover, the algorithm takes a short time, does not need too much manual intervention, and can effectively segment artistic images. The optimization algorithm in this paper has certain reference significance for the related research of artistic image segmentation. [ABSTRACT FROM AUTHOR]

With the development of deep learning theory and the decrease of the cost of acquiring massive data, the image semantic segmentation algorithm based on Convolutional Neural Networks (CNNs) is gradually replacing the conventional segmentation algorithm by its high accuracy segmentation performance. By increasing the amount of training data and stacking more convolutional layers to form Deep Convolutional Neural Networks (DCNNs), a neural network model with higher segmentation accuracy can be obtained, but it faces the problems of serious memory consumption and long latency. For some special application scenarios, such as augmented reality and mobile interaction, real-time processing cannot be performed. To improve the speed of semantic segmentation while obtaining the most accurate segmentation results as possible, this paper proposes a semantic segmentation algorithm based on lightweight convolutional neural networks. Taking the computational complexity and segmentation accuracy into account, the algorithm starts from the perspective of extracting high-level semantic features and introduces a position-attention mechanism with richer contextual information to model the relationship between different pixels, avoiding the convolutional local perceptual field to be too small. To recover clearer target boundaries, a channel attention mechanism is introduced in the decoding part of the model to mine more useful feature channel information and effectively improve the fusion of low-level features with high-level features. By verifying the effectiveness of the above model on a publicly available dataset and comparing it with the more popular semantic segmentation methods, the model proposed in this paper has higher semantic segmentation accuracy and reflects certain advantages in objective evaluation. [ABSTRACT FROM AUTHOR]

The intrinsic high parallelism and entanglement characteristics of quantum computing have made quantum image processing techniques a focus of great interest. One of the most widely used techniques in image processing is segmentation, which in one of their most basic forms can be carried out using thresholding algorithms. In this paper, a fault-tolerant quantum dual-threshold algorithm has been proposed. This algorithm has been built using only Clifford+T gates for compatibility with error detection and correction codes. Because fault-tolerant implementation of T gates has a much higher cost than other quantum gates, our focus has been on reducing the number of these gates. This has allowed adding noise tolerance, computational cost reduction, and fault tolerance to the state-of-the-art dual-threshold segmentation circuits. Since the dual-threshold image segmentation involves the comparison operation, as part of this work we have implemented two full comparator circuits. These circuits optimize the metrics T-count and T-depth with respect to the best circuit comparators currently available in the literature. [ABSTRACT FROM AUTHOR]

Interactive image segmentation refers to bringing in a small number of user guidelines to segment the target object during the segmentation process and is one of the most fundamental tasks in image processing. Existing methods usually require the construction of non-quadratic energy functions and generally suffer from the lack of unique solutions and low segmentation accuracy. To further improve the segmentation quality, this paper proposed an interactive image segmentation algorithm combining local linear embedding and seed information（SILLE). The algorithm toke into account the local and a priori information of the pixel points and incorporated the information of the labeled seed points into the newly constructed energy function to obtain a unique and optimal solution of the energy function with an efficient and fast minimization scheme, thus obtaining more accurate segmentation results. Finally, comparison of different methods on different datasets verifies the effectiveness and feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm's search diversity, local pioneering ability, and convergence accuracy. A levy flight strategy further improves the algorithm's ability to jump out of local minima. In this paper, 12 benchmark test functions and 8 other newer swarm intelligence algorithms are compared in terms of convergence speed and convergence accuracy to evaluate the performance of the MSIPOA algorithm. By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. According to different performance metrics and Fridman test, MSIPOA algorithm outperforms similar algorithms in global optimization and image segmentation, and the symmetric cross entropy of MSIPOA algorithm for multilevel thresholding image segmentation method can be effectively applied to multilevel thresholding image segmentation tasks. [ABSTRACT FROM AUTHOR]

The traditional fuzzy C-means clustering technology only considers one performance Angle of image segmentation process when processing data, resulting in low accuracy of image segmentation. In this paper, the traditional FCM algorithm is analyzed, and the low clustering accuracy, noise interference and lack of flexibility and other problems are fully considered from the relationship between parameter components, non-local spatial information elements and noise sensitivity. Firstly, a distance calculation method based on robust statistics theory is proposed, which can deal with abnormal noise stably. Secondly, based on the extreme learning machine theory, the non-local spatial information coefficient is introduced to improve the identification ability of the influence factors. This method not only guarantees the anti-noise performance of the algorithm, but also preserves the image data, improving the iteration efficiency and segmentation accuracy of the algorithm. The test results show that the accuracy of the improved C-means clustering algorithm for image segmentation is 95.5%, which is compared with the traditional C-means clustering technique and other optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a new variant of the metaheuristic algorithm based on the naked mole rat (NMR) algorithm, called the Q-learning naked mole rat algorithm (QL-NMR), for substitution box construction and optimization. Unlike most competing works (which typically integrate a single chaotic map into a particular metaheuristic algorithm), QL-NMR assembles five chaotic maps (i.e., Chebyshev, logistic, circle, Singer, and sinusoidal) as part of the algorithm itself. Using a Q-learning table, QL-NMR remembers the historical performance of each chaotic map during the S-box construction process allowing just-in-time adaptive selection based on its current performance. Experimental results for 8 × 8 S-box generation demonstrate that the proposed QL-NMR gives competitive performance against other existing works, particularly in terms of nonlinearity and strict avalanche criteria. To further demonstrate the effectiveness of our proposed work, we have subjected the QL-NMR for image segmentation using multilevel thresholding. The results confirm that QL-NMR gives better performance than its predecessor NMR. Finally, QL-NMR S-box also outperformed NMR S-box in image encryption. [ABSTRACT FROM AUTHOR]

Line detection is a fundamental technique in image processing. It can extract the required information, while the information that does not need attention can be ignored, thus reducing the amount of data. At the same time, line detection is also the basis of image segmentation and plays an important role in this process. In this paper, we implement a quantum algorithm based on a line detection mask for novel enhanced quantum representation (NEQR). We build a quantum algorithm for line detection in different directions and design a quantum circuit for line detection. The detailed module designed is also provided. On a classical computer, we simulate the quantum method, and the simulation results prove the feasibility of the quantum method. By analyzing the complexity of quantum line detection, we find that the computation complexity of the proposed method is improved compared to some similar edge detection algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid and unsupervised approach to flame front detection for low signal-to-noise planar laser-induced fluorescence (PLIF) images. The algorithm combines segmentation and edge detection techniques to achieve low-cost and accurate flame front detection in the presence of noise and variability in the flame structure. The method first uses an adaptive contrast enhancement scheme to improve the quality of the image prior to segmentation. The general shape of the flame front is then highlighted using segmentation, while the edge detection method is used to refine the results and highlight the flame front more accurately. The performance of the algorithm is tested on a dataset of high-speed PLIF images and is shown to achieve high accuracy in finely wrinkled turbulent hydrogen-enriched flames with order of magnitude improvements in computation speed. This new algorithm has potential applications in the experimental study of turbulent flames subject to intense wrinkling and low signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low efficiency and insufficient accuracy of threshold solution in multithreshold sculpture image segmentation, this paper proposes a threshold segmentation algorithm for sculpture images based on sparse decomposition. In this paper, sparse decomposition is introduced to optimize the model to reduce the impact of local noise on segmentation accuracy, and an energy functional based on pixel coconstraint is built to make up for the defect that pixels cannot retain local details. At the same time, the weighted sum of elite solution sets is used to determine Neighborhood centers increase communication between groups. Experiments show that compared with other algorithms, the above method has significant advantages in convergence efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Interactive image segmentation uses a priori information to guide the acquisition of the people ' s interesting parts of an image, but existing algorithms cannot balance efficiency and accuracy. To solve this problem, this paper proposed a fast interactive segmentation algorithm based on superpixel and random walk (random walk on superpixel, SPRW) . Firstly, it presegmented the image into superpixel regions with local similarity and used pixel colour averages to represent the superpixel regions. Secondly, it built the F-B graph structure based on manually labelled a priori information and extended the range of the random walk, obtained the hard segmentation results using the random walk solution. Finally, this paper proposed the improved matting algorithm (fast robust matting, FRB ) for the unsmooth boundaries of segmentation results and performed the secondary process to obtain soft segmentation results. Experiments on the BSDSOO and MSRC datasets confirm that the proposed hard method is superior to other algorithms in terms of time and average intersection ratio, while experiments on the Alpha Matting dataset demonstrate that the proposed soft algorithm improves efficiency and accuracy. In addition, the experiment of replacing the background of a life photograph demonstrates the application value of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the research and application of images, people are often only interested in the foreground or specific area of the image, so it is necessary to extract the specific area from the image, and image segmentation technology is the key to solving this problem. Aiming at the complex background and the color image with unclear target contour as the target image to be segmented, this paper first uses the texture and color of the image as the feature vector, and proposes an image segmentation algorithm based on SVM. The experimental results show that the segmentation accuracy is 91.23%. Secondly, in order to improve the accuracy of segmentation, the SVM algorithm is improved. The improved SVM algorithm is based on the grid search method to optimize the parameters C and g in the SVM. At the same time, the HIS color channel is added to the feature vector to obtain more Excellent SVM image segmentation model. Finally, the color image segmentation is verified and compared with the standard SVM algorithm. The experimental results show that the accuracy rate of the improved SVM algorithm reaches 97.263%, which improves the segmentation efficiency. It is verified that the improved model proposed in this paper can effectively segment complex color images. [ABSTRACT FROM AUTHOR]

The traditional algorithms have the low performance when applied to remote sensing images segmentation. Thus, this paper proposed a segmentation algorithm based on adaptive feature-reduction. Firstly, this algorithm divided the source image into super-pixels as the basic operation object. Then, it extracted the color, texture, edge and spatial features of images and used weighted pixel values to calculate the features of super-pixels. Furthermore, it added the fuzzy separation measure into the FRFCM model to construct own segmentation model. This model can automatically select useful features. Finally, this paper got the final segmentation result based on optimizing the segmentation model. Experiments on remote sensing images prove that this proposed algorithm has superior performance in terms of segmentation accuracy, running time and eliminate noise effects. [ABSTRACT FROM AUTHOR]

Aiming at the problem that dynamic objects will lead to excessive pose estimation error, this paper proposed a RGB-D SLAM algorithm for dynamic scenes. Firstly, the method divides the object detection results into dynamic objects and potential dynamic objects according to whether the object has the ability of autonomous movement, and uses Otsu method to complete the front and background segmentation of the area in the detection frame in the corresponding depth map. Then, combined with the position coordinates and depth information of the detection frame, determined whether the potential dynamic object was related to the dynamic object in three-dimensional space, and screened out the potential dynamic object with a certain motion probability. Finally, used the motion consistency detection algorithm to determine whether to treat potential dynamic objects as dynamic objects. The experiment part selects the open TUM data set, and the results show that the algorithm has high positioning accuracy and robustness while maintaining real-time. [ABSTRACT FROM AUTHOR]

In this paper, an efficient interactive image segmentation method based on maximization of submodular function under user's scribble constraint is presented. The problem of interactive image segmentation is formulated as the maximum entropy rate under user's constraints. The objective function is submodular, and we solve the constrained submodular function maximization by incorporating a new data structure and some aggregating rules into the greedy algorithm. The main steps of our algorithm are as follows. First, the pixels scribbled by the user are clustered separately as target foreground and background clusters. Second, in the process of greedy algorithm, unscribbled pixels are aggregated to the corresponding target cluster according to the proposed aggregating rules. Finally, the segmentation result is presented by the two target clusters. The experiments and comparisons on three standard benchmarks show that our method has good performance. Our method is straightforward and efficient, and the time complexity of our method is between linear and polynomial. Furthermore, we analyze the influence of different scribbles, and propose some optimal scribble strategies. [ABSTRACT FROM AUTHOR]

The number of wheat ears is one of the most important factors in wheat yield composition. Rapid and accurate assessment of wheat ear number is of great importance for predicting grain yield and food security-related early warning signal generation. The current wheat ear counting methods rely on manual surveys, which are time-consuming, laborious, inefficient and inaccurate. Existing non-destructive wheat ear detection techniques are mostly applied to near-ground images and are difficult to apply to large-scale monitoring. In this study, we proposed a sampling survey method based on the unmanned aerial vehicle (UAV). Firstly, a small number of UAV images were acquired based on the five-point sampling mode. Secondly, an adaptive Gaussian kernel size was used to generate the ground truth density map. Thirdly, a density map regression network (DM-Net) was constructed and optimized. Finally, we designed an overlapping area of sub-images to solve the repeated counting caused by image segmentation. The MAE and MSE of the proposed model were 9.01 and 11.85, respectively. We compared the sampling survey method based on UAV images in this paper with the manual survey method. The results showed that the RMSE and MAPE of NM13 were 18.95 × 104/hm2 and 3.37%, respectively, and for YFM4, 13.65 × 104/hm2 and 2.94%, respectively. This study enables the investigation of the number of wheat ears in a large area, which can provide favorable support for wheat yield estimation. [ABSTRACT FROM AUTHOR]

Taking Uyghur character recognition as an example, this paper use a method of image segmentation which combines traditional methods with CNN, and transforms and implements the Uyghur character on intelligent devices. Firstly, after analyzing the application of several general segmentation methods, this paper finds some shortcomings but also some ideas for Uighur segmentation. Then, starting from the characteristics of Uyghur language, such as structure, word formation and input habits, the author studies the idea of Uyghur adhesive language segmentation from the perspective of language characteristics, puts forward the basic algorithm of Uyghur symbol segmentation, and applies the Uyghur character adhesion segmentation based on minimum spanning tree and multi-queue primitive merging model to improve the segmentation efficiency. In addition, in order to solve the limitations of the traditional handwriting recognition framework of "preprocessing + feature extraction + classifier", this paper puts forward a new solution of Uyghur handwriting recognition technology combining prior domain knowledge with CNN, constructs a Uyghur handwriting recognition model on the basis of CNN and random elastic deformation, and also improves the recognition rate of the system by combining domain knowledge with CNN. According to the experimental results, we can conclude that the mixed method proposed in this paper can effectively break the technical bottleneck of traditional methods, thus improving the efficiency of segmentation and recognition. [ABSTRACT FROM AUTHOR]

The mainstream image matching method for recovering the motion of the camera is based on local feature matching, which faces the challenges of rotation, illumination, and the presence of dynamic objects. In addition, local feature matching relies on the distance between descriptors, which easily leads to lots of mismatches. In this paper, we propose a new robust image matching method for camera pose estimation, called IM_CPE. It is a novel descriptor matching method combined with 3-D point clouds for image matching. Specifically, we propose to extract feature points based on a pair of matched point cloud planes, which are generated and segmented based on depth images. Then, the feature points are matched based on the distance between their corresponding 3-D points on the point cloud planes and the distance between their descriptors. Moreover, the robustness of the matching can be guaranteed by the centroid distance of the matched point cloud planes. We evaluate the performance of IM_CPE using four well-known key point extraction algorithms, namely Scale-Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF), Features from Accelerated Segment Test (FAST), and Oriented FAST and Rotated Brief (ORB), with four sequences from the TUM RGBD dataset. According to the experimental results, compared to the original SIFT, SURF, FAST, and ORB algorithms, the NN_mAP performance of the four key point algorithms has been improved by 11.25%, 13.98%, 16.63%, and 10.53% on average, respectively, and the M.Score has also been improved by 25.15%, 23.05%, 22.28%, and 11.05% on average, respectively. The results show that the IM_CPE can be combined with the existing key points extraction algorithms and the IM_CPE can significantly improve the performance of these key points algorithms. [ABSTRACT FROM AUTHOR]

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. Lévy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images. [ABSTRACT FROM AUTHOR]

Aiming at the difficult problem of coal gangue image segmentation in complex backgrounds, this paper proposes an image segmentation method based on the edge detection theory of the star algorithm. The pixel matrix is extracted one by one in the X and Y directions of the coal gangue image, and the central pixel of the matrix satisfying the monotonic change condition is assigned as 0. They are mapped to single-value images with equal size in turn, to realize the detection of coal and gangue edges in the images. The response strategy of adjusting matrix length n and assignment factor β in real-time by using the feedback result of the illuminance meter in changing illumination environment is given. Combining the edge detection method of the star algorithm with the morphological method, the fine segmentation of the coal gangue image is completed. The segmentation results are based on the segmentation results obtained by the AI algorithm, and the error rates of the pixel area and centroid coordinates of coal gangue are within 0.29%. This study provides a novel, precise and efficient solution to the problem of image edge detection and segmentation in complex backgrounds. A new method of coal gangue edge detection. Adapt to the complex background and illumination change conditions. The area and position information of coal gangue can be extracted quickly and accurately. It provides a new scheme to solve the background segmentation problem in coal gangue recognition. [ABSTRACT FROM AUTHOR]

There is a constant rise in the amount of data being copied or plagiarized because of the abundance of content and information freely available across the internet. Even though the systems try to check documents for the plagiarism, there have been trials to overcome these system checks. In this paper, the concept of character injection is used to trick plagiarism checker is presented. It is also showcased that how does the similarity check algorithms based on k-grams fail to detect the character injection. In order to eradicate the problem or error in similarity rates caused due to the problem of character injection, image processing based approach of multiple histogram projections are used. An application is developed to detect the character injection in the document and produce the accurate similarity rate. The results are shown with some test documents and the proposed method eliminates any kind of character injected in the document that tricks plagiarism. The proposed method has addressed the problem of character injection with image processing based changes in the existing methods of document-similarity check algorithms using k-grams. The proposed method can detect 100% injected character be it any alphabet of any language, The processing time for conversion, histogram projections and applying winnowing algorithm takes 1.2 sec per page on average when experimented on multiple types of document varying in size from 2 KB to 10 MB. [ABSTRACT FROM AUTHOR]

In long-term use, cracks will show up on the road, delivering monetary losses and security hazards. However, the road surface with a complex background has various disturbances, so it is challenging to segment the cracks accurately. Therefore, we propose a pavement cracks segmentation method based on a conditional generative adversarial network in this paper. U-net3+ with the attention module is used in the generator to generate segmented images for pavement cracks. The attention module highlights crack features and suppresses noise features from two dimensions of channel and space, then fuses the features generated by these two dimensions to obtain more complementary crack features. The original image is stitched with the manual annotation of cracks and the generated segmented image as the input of the discriminator. The PatchGAN method is used in the discriminator. Moreover, we propose a weighted hybrid loss function to improve the segmentation accuracy by exploiting the difference between the generated and annotated images. Through alternating gaming training of the generator and the discriminator, the segmentation image of cracks generated by the generator is very close to the actual segmentation image, thus achieving the effect of crack detection. Our experimental results using the Crack500 datasets show that the proposed method can eliminate various disturbances and achieve superior performance in pavement crack detection with complex backgrounds. [ABSTRACT FROM AUTHOR]

In the special sports camera, there are subframes. A lens is composed of multiple frames. It will be unclear if a frame is cut out. The definition of video screenshots lies in the quality of video. To get clear screenshots, we need to find clear video. The purpose of this paper is to analyze and evaluate the quality of sports video images. Through the semantic analysis and program design of video using computer language, the video images are matched with the data model constructed by research, and the real-time analysis of sports video images is formed, so as to achieve the real-time analysis effect of sports techniques and tactics. In view of the defects of rough image segmentation and high spatial distortion rate in current sports video image evaluation methods, this paper proposes a sports video image evaluation method based on BP neural network perception. The results show that the optimized algorithm can overcome the slow convergence of weights of traditional algorithm and the oscillation in error convergence of variable step size algorithm. The optimized algorithm will significantly reduce the learning error of neural network and the overall error of network quality classification and greatly improve the accuracy of evaluation. Sanda motion video image quality evaluation method based on BP (back propagation) neural network perception has high spatial accuracy, good noise iteration performance, and low spatial distortion rate, so it can accurately evaluate Sanda motion video image quality. [ABSTRACT FROM AUTHOR]

Due to the haze weather, the outdoor image quality is degraded, which reduces the image contrast, thereby reducing the efficiency of computer vision systems such as target recognition. There are two aspects of the traditional algorithm based on the principle of dark channel to be improved. First, the restored images obviously contain color distortion in the sky region. Second, the white regions in the scene easily affect the atmospheric light estimated. To solve the above problems, this paper proposes a single-image dehazing and image segmentation method via dark channel prior (DCP) and adaptive threshold. The sky region of hazing image is relatively bright, so sky region does not meet the DCP. The sky part is separated by the adaptive threshold, then the scenery and the sky area are dehazed, respectively. In order to avoid the interference caused by white objects to the estimation of atmospheric light, we estimate the value of atmospheric light using the separated area of the sky. The algorithm in this paper makes up for the shortcoming that the algorithm based on the DCP cannot effectively process the hazing image with sky region, avoiding the effect of white objects on estimating atmospheric light. Experimental results show the feasibility and effectiveness of the improved algorithm. [ABSTRACT FROM AUTHOR]