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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]

In order to solve the problem of noise sensitivity and low precision in image segmentation, a new image segmentation method based on super-pixel with neighborhood constrained and multi-level FCM clustering is proposed. Firstly, an improved SLIC algorithm of FCM is designed based on the local pixel region of the image to get the refined super-pixel. Then, the improved FCM clustering algorithm is combined with the spatial relationship of neighborhood super-pixel to mark the super-pixel categories. In order to verify the effectiveness of the algorithm, the artificial synthetic image with noise and natural image without noise are used for comparison experiments. The experimental result show the algorithm proposed in this paper can not only suppress the noise, but also improve the processing accuracy. [ABSTRACT FROM AUTHOR]

Superpixel-based segmentation has been widely used as a primary prepossessing step to simplify the subsequent image processing tasks. Since determining the number of clusters is subjective and varies based on the type of image, the segmentation algorithm may provide over-segmented or under-segmented superpixels. This paper proposes an image segmentation method to improve the SLIC superpixel by region merging. It aims to improve the segmentation accuracy without defining a precise number of superls. The color difference between superpixels is employed as a homogeneity criterion for the merging process. The Berkeley dataset is used with different quantitative performance metrics to evaluate the proposed model's performance. Results obtained from probabilistic rand index (PRI), boundary recall, and under-segmentation error proved the ability of the proposed algorithm to provide comparable segmentation with a reduced number of clusters. [ABSTRACT FROM AUTHOR]

In order to improve the security effect of image information, this paper studies the reversible information hiding of the digital images combined with the pixel combination algorithm and proposes an improved simulated annealing algorithm using the incremental calculation method of statistical functions. Moreover, according to the gray gradient information contained in the image, this paper proposes an automatic threshold determination algorithm suitable for the unimodal gray distribution images and uses this algorithm to complete the threshold determination and binary segmentation of all images. In addition, this paper optimizes the model by transforming the isolated pores inside the model and removing the isolated matrix area. Through comparative experiments, it can be seen that the reversible information hiding method for digital images based on the pixel combination algorithm proposed in this paper has a good image information confidentiality effect. [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]

Medical image segmentation can effectively overcome human perception with strong personal limitations. The superpixel/voxel segmentation method has strong adaptability and computational efficiency. It can effectively separate the diseased tissue from normal cells or bone and muscle, which is widely used. This paper proposes a superpixel/voxel medical image segmentation algorithm based on regional interlinked value and block (region) merging, which can segment the two-dimensional bone image and three-dimensional brain image. By computing the regional interlinked value, the proposed method can overcome the problem of the initial setting block size in the traditional superpixel/voxel segmentation method. Next, the blocks with the same features are merged. To segment the superpixel/voxel medical image, the final distance with the intensity feature, the location feature, and the gradient feature is considered. Compared with most state-of-the-art algorithms, the proposed method has strong robustness and efficiency, which provides a solid foundation for further image segmentation. [ABSTRACT FROM AUTHOR]

With the increase of the camera resolution, the number of pixels contained in froth image is increased, which brings many challenges to image segmentation. Froth size and distribution are the important index in froth flotation. The segmentation of froth images is always a problem in building flotation model. In segmenting froth images, Otsu method is usually used to get a binary image for classification of froth images, this method can get a satisfactory segmentation result. However, each gray level is required to calculate each of the between-class variance, it takes a longer time in froth images with a large number of pixels. To solve this problem, an improved method is proposed in this paper. Most froth images have the pixel distribution characteristic that the gray histogram curve is a sawtooth shape. The proposed method uses polynomial to fit the curve of gray histogram and takes the characteristic of gray histogram's valley into consideration in Otsu method. Two performance comparison methods are introduced and used. Experimental comparison between Otsu method and the proposed method shows that the proposed method has a satisfactory image segmentation with a low computing time. [ABSTRACT FROM AUTHOR]

Milano Retinex is a family of spatial color algorithms inspired by Retinex and mainly devoted to the image enhancement. In the so-called point-based sampling Milano Retinex algorithms, this task is accomplished by processing the color of each image pixel based on a set of colors sampled in its surround. This paper presents STAR, a segmentation based approximation of the point-based sampling Milano Retinex approaches: it replaces the pixel-wise image sampling by a novel, computationally efficient procedure that detects once for all the color and spatial information relevant to image enhancement from clusters of pixels output by a segmentation. The experiments reported here show that STAR performs similarly to previous point-based sampling Milano Retinex approaches and that STAR enhancement improves the accuracy of the well-known algorithm scale-invariant feature transform on the description and matching of photographs captured under difficult light conditions. [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 conventional fuzzy c-means clustering (FCM) algorithm is sensitive to noise because no spatial information is taken into account. Many related algorithms reduce the influence of noise by adding local information to the objective function. However, there are still many problems, such as poor edge-preserving and anti-noise performance. This paper proposes an FCM-based method for image refinement segmentation to address the above problems effectively. We first take advantage of the pre-classification results of image sub-blocks as a new metric to measure the similarity of pixels and then combine the grayscale and spatial features of the local windows to vote and refine on these initial clustering results, which optimize the classification of pixels. Compared with existing algorithms, our algorithm can correct the misclassified pixels in the global segmentation and reserve image edge better. In addition, it is efficient for noisy image segmentation, which can maximize the recognition of noise and eliminate outliers. Experiments on both synthetic images and real-world images demonstrate the effectiveness and accuracy of the proposed method. [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]

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]

Each pixel can be classified in the image by the semantic segmentation. The segmentation detection results of pixel level can be got which are similar to the contour of the target object. However, the results of semantic segmentation trained by Fully convolutional networks often lead to the loss of detail information. This paper proposes a CRF-FCN model based on CRF optimization. Firstly, the original image is detected based on feature pyramid networks, and the target area information is extracted, which is used to train the high-order potential function of CRF. Then, the high-order CRF is used as the back-end of the complete convolution network to optimize the semantic image segmentation. The algorithm comparison experiment shows that our algorithm makes the target details more obvious, and improves the accuracy and efficiency of semantic segmentation. [ABSTRACT FROM AUTHOR]

This paper proposes an image enhancement algorithm based on the theory of image segmentation and image frequency. First, a mathematical model corresponding to the pixel frequency is established by using the difference of pigment values between the pixels in the image and the surrounding pixels (i.e., pixel receptive field). Then, the image is divided into the low frequency region (background area), low-medium frequency region (foreground area), medium-high frequency region (target area) and high frequency region (detail area) by a pixel frequency characteristic graph. Gamma correction, MSRCR, MSR, top hat + bottom hat are used for image enhancement for each area, and then the parts are merged. Three indicators of PSNR, SSIM, and MSE are introduced to evaluate the quality of the enhanced image. The results show that the image enhanced by this algorithm has the highest PSNR and SSIM values and the lowest MSE value, indicating that the enhancement effect of this algorithm is better. Compared with traditional algorithms, the image enhancement algorithm in this paper produces higher image quality and richer details. [ABSTRACT FROM AUTHOR]

Fuzzy clustering is widely used in image segmentation because of its ability to describe the uncertain information presented in images. However, traditional fuzzy clustering ignores the spatial contextual information in the image, which makes it poor in processing images corrupted by high noise. In this paper, we present a novel fuzzy clustering algorithm called PFLWCM-CIM algorithm for noise image segmentation by introducing the image patches, the correntropy induced metric (CIM), and a fuzzy local weighted factor. Firstly, we use image patches as samples in clustering. Compared with individual pixels, image patches can preserve the local geometry of images by considering comprehensive features. Secondly, a new distance measure is developed on the basis of image patches and CIM to describe the relationship between samples and cluster centers. The CIM is more robust to noise than the traditional L 2 -norm. Next, a novel weighting method to characterize the similarity between two pixels named local weights is proposed. The local weights combine the spatial location relationship and the pixel value relationship of two pixels simultaneously, which describes the relationship between pixels from a more reasonable perspective. Furthermore, a new fuzzy local weighted factor is put forward by integrating the new distance measure and the local weights, then the PFLWCM-CIM algorithm is proposed based on the new factor and the idea of image patches. Several commonly used fuzzy clustering algorithms are incorporated into the experiments in segmenting images polluted by various types of noise. Experimental results demonstrate that our algorithm has reached state-of-the-art results in several metrics. [ABSTRACT FROM AUTHOR]

Generating a sufficient number of regions with high accuracy is an important objective in the region proposal generation techniques. This paper presents a new, robust, and effective approach, which is based on the bottom-up segmentation, to produce a pool of well-quality regions. After image segmentation, the segmented candidates are expanded into the surrounding regions. The suggested algorithm produces some enlarged regions, which better cover objects and stuff. The proposed process can be applied in three different modes, namely fixed_mode, all_mode, and efficient_mode. The fixed_mode extends each region into parts of all the adjacent regions using an extension controller, which considers adjacent sequential pixels for each point on the region boundary. In all_mode, the current region is merged with all the adjacent regions to generate a larger region. The efficient_mode is then implemented using the accumulation of the results from both the fixed_mode and all_mode. Besides, the algorithm can be repeated in the fixed_mode and all_mode by considering a variety of values for the extension controller factor. No features are required to be extracted in the proposed algorithm, except for the image segmentation stage. In this study, four challenging datasets known as MSRC, VOC2007, VOC2012, and COCO 2017 are used to compare the proposed algorithm with other segmentation and region proposal algorithms. As a significant advantage compared to well-known region proposal algorithms, our approach achieves a greater Recall with the desirable number of regions. Furthermore, the algorithm shows a good improvementin extraction of small, medium, and large objects. [ABSTRACT FROM AUTHOR]

The interactive image segmentation algorithm based on pixel-level is sensitive to initial seed position and noise . However, the image segmentation method only oriented super-pixels is unable to retain image details so that under-segmentation could produce. Aiming at above problems, this paper proposed the segmentation model based on superpixel/pixel constraint and sparse representation. The method firstly defined two mutual constraint cost functions utilized the Gaussian model for super-pixels and pixels respectively. Meanwhile, it introduced constraint condition oriented on sparse decomposition to optimize the proposed model to avoid over-segmentation. Finally, this paper utilized joint optimization method to obtain the solution of the proposed model and estimated the object and background labels to realize object extraction. Experiment results demonstrate that the proposed method can obtain better segmentation results compared with the state-of-the-art methods and is more robust to noise. [ABSTRACT FROM AUTHOR]

Traditional saliency detection algorithms lack object semantic character, and the segmentation algorithms cannot highlight the saliency of the segmentation regions. In order to compensate for the defects of these two algorithms, the salient object segmentation model, which is a novel combination of two algorithms, is established in this paper. With the help of a priori knowledge of image boundary background traits, the K-means++ algorithm is used to cluster the pixels for each region; in line with the sensitivity of the human eye to color and with its attention mechanism, the joint probability distribution of the regional contrast ratio and spatial saliency is established. The selection of the salient area is based on the probabilities, for which the region boundary is taken as the initial curve, and the level-set algorithm is used to perform the salient object segmentation of the image. The curve convergence condition is established according to the confidence level for the segmented region, thus avoiding over-convergence of the segmentation curve. With this method, the salient region boundary is adjacent to the object contour, so the curve evolution time is shorter, and compared with the traditional Li algorithm, the proposed algorithm has higher segmentation evaluation scores, with the additional benefit of emphasizing the importance of the object. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of performing multiple objects, interactive image segmentation. Given a small number of pixels (seeds) with predefined labels, we can quickly and accurately determine the closest seed from each unlabeled pixel. By assigning each pixel to the label same as its closest seed, a rapid image segmentation result can be obtained. Since the shortest distance is considered in this paper which directs our attention of segmentation into path planning problem, Dijkstra occurs to mind. Modifying the classical single-source algorithm, a simple yet rapid multi-source Dijkstra (MSD) algorithm is put forward. From both theoretical and experimental aspects, the proposed algorithm performs quite well in resisting noise, and preserving the objects details. Moreover, under the situation of multiple sources, instead of performing Dijkstra several times to obtain the distance from each pixel to each seed and choose the closest seed, the proposed multi-source image segmentation algorithm could determine the closest seed by running Dijkstra only once. Its efficiency, which will not be affected by the number of initial seed settings, maintains the same as the Dijkstra. [ABSTRACT FROM AUTHOR]

The existing infrared image processing technology mainly relies on the traditional segmentation algorithm, which is not only inefficient, but also has problems such as blurred edges, poor segmentation accuracy, and insufficient extraction of key power equipment features for the infrared image defect segmentation of power equipment. A CS_DeeplabV3+ network for the accurate segmentation of the infrared image defect segmentation of power equipment is designed for the situation of leakage and false detection after segmentation by traditional algorithms. The ASPP module is improved in the encoder part to enable the network to obtain a denser pixel sampling, an improved attention mechanism is introduced to enhance the sensitivity and accuracy of the network for feature extraction, and a semantic segmentation feature enhancement module—the structured feature enhancement module (SFEM)—is introduced in the decoder part to enhance the feature processing to improve the segmentation accuracy. The CS_DeeplabV3+ network is validated using the dataset, and the experimental comparison proves that the improved model has finer contours compared with other models for segmenting infrared images of power equipment defects, and MPA is improved by 5.6% and MIOU is improved by 7.3% compared with the DeeplabV3+ network. [ABSTRACT FROM AUTHOR]

Video segmentation is a task of partitioning pixels that exhibit homogeneous appearance and motion into coherent spatial–temporal groups, which is still challenging for aerial applications. In this paper, a principled combination of superpixel labeling priors and Markov random field (S-MRF) is proposed for aerial video segmentation. The proposed approach has several contributions: 1) we develop a metadata-based global projection model with coordinate transformation to estimate motion information between frames; 2) the superpixel labeling priors from previous frames are incorporated into the segmentation of the current frame, leading to a highly efficient probabilistic label propagation algorithm; and 3) we perform an MRF optimization on the initial segments with propagated labeling priors to improve the temporal coherency. In addition, a new video dataset is collected and will be made publicly available to evaluate the performance of aerial video segmentation algorithms. The experimental results show that the proposed approach outperforms the state-of-the-art video segmentation methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new framework for hierarchical image segmentation based on iterative contraction and merging. In the proposed framework, we treat the hierarchical image segmentation problem as a sequel of optimization problems, with each optimization process being realized by a contraction-and-merging process to identify and merge the most similar data pairs at the current resolution. At the beginning, we perform pixel-based contraction and merging to quickly combine image pixels into initial region-elements with visually indistinguishable intra-region color difference. After that, we iteratively perform region-based contraction and merging to group adjacent regions into larger ones to progressively form a segmentation dendrogram for hierarchical segmentation. Comparing with the state-of-the-art techniques, the proposed algorithm can not only produce high-quality segmentation results in a more efficient way, but also keep a lot of boundary details in the segmentation results. [ABSTRACT FROM PUBLISHER]

Among the Markov random field methods, conditional random fields (CRF) model has shown good results in Synthetic Aperture Radar (SAR) image segmentation. CRF not only directly models the posterior distribution of label field conditioned on images but also allows the interactions between observations. In this paper, we propose a Fast, Weighted CRF (FWCRF) algorithm based on a two-step superpixel generation method. In our method, heterogeneity of intensity in SAR images due to speckle noise and backscattering is considered. The first step is a preprocessing task for suppressing speckle noise which is done by the L0 smoothing algorithm. It is followed by Sobel edge detector to highlight heterogeneous regions and edges. Then SAR image is partitioned into homogeneous and heterogeneous regions by using fast robust fuzzy c-means clustering (FRFCM). Simultaneously, the simple linear iterative clustering (SLIC) algorithm is applied to split the image into superpixels. Next, the superpixels belonging to the same class are merged by using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. Finally, the SAR image is labelled using the FWCRF algorithm which consists of weighted pairwise potential based on adaptive features and applying FRFCM as an initial segmentation method. The proposed algorithm is evaluated in comparison with other conditional random field schemes. The results of our proposed method demonstrate accuracy improvement in segmentation. [ABSTRACT FROM AUTHOR]

Summary: Image segmentation method based on region growing has the advantages of simple segmentation method and complete segmentation target. Because the color discrimination and gray gradient of smoke are not obvious, the traditional region growing segmentation method is difficult to separate it from the image, resulting in an unsatisfactory segmentation effect. To solve this problem, this paper partially improved the region growing method and proposed a new smoke segmentation algorithm based on the improved intelligent seeded region growing (IISRG) method. First, smoke images obtained from experimental videos were converted from the RGB color space to the HSV color space, and image binarization was achieved using background subtraction with an adaptive threshold in the V channel. Then, a pixel in the binary image was selected intelligently as the seed point, which was used for the regional growth. The final smoke segmentation images were obtained by the morphological processing of region growing images. Experimental smoke segmentation results show that the proposed algorithm has a higher overlap rate and a lower overflow rate, and performs a better smoke segmentation effect compared with the other two approaches. In addition, this algorithm can also effectively solve the problems of under‐segmentation and over‐segmentation of smoke images. [ABSTRACT FROM AUTHOR]

In this paper, a context-sensitive energy curve based cross-entropy method for multilevel color image segmentation is proposed. In thresholding approaches, pixels are arranged in various regions based on their intensity level. The main challenge generally faced in multilevel thresholding is the selection of best threshold values for the pixel division. However, the combination of the energy curve and the minimum cross entropy (Energy-MCE) scheme provides appropriate thresholds for a multilevel approach, but the computational cost for selecting optimal thresholds is high. Therefore, the selection of meta-heuristic optimization algorithms reduces this cost and generates optimal thresholds. A multi-verse optimizer (MVO) algorithm based on Energy-MCE thresholding approach is proposed to search the accurate and near-optimal thresholds for segmentation. Tests on natural images showed that the proposed method achieves better performance than the well-known optimization techniques in many challenging cases or images, such as identifying weak objects and revealing fine structures of complex objects while the added computational cost is minimal. [ABSTRACT FROM AUTHOR]

Superpixel as a fundamental processing unit can significantly reduce the computational complexity of subsequent computer vision tasks. In this paper, an Adaptive High-Precision (AHP) superpixel segmentation algorithm is proposed. Three major schemes are proposed in this algorithm. First, a scheme of generating the initial number of superpixels adaptively is proposed by calculating the histogram peak value of L-component corresponding to the smallest salient region. In addition, we show that the isolated pixels are not only existent but also abundant. Instead of using distance-measurement of color and position space, we reclassify each isolated pixel just relying on the LAB color space. Furthermore, the detection and re-segmentation scheme of the under-segmentation superpixels is adopted according to the standard deviation and mean value of the L-component, which can re-segment each under-segmentation superpixel directly without measuring the distance between each under-segmentation pixel and new centroid. The proposed algorithm has linear computational complexity and the merits of adaptivity and high precision. Experiments on the Berkeley segmentation dataset demonstrate the effectiveness and feasibility of the proposed schemes, and they also prove AHP can achieve high-precision superpixel segmentation results in comparison with the state-of-the-art algorithms on standard metrics. [ABSTRACT FROM AUTHOR]

Fuzzy C-means has been adopted for image segmentation, but it is sensitive to noise and other image artifacts due to not considering neighbor information. In order to solve this problem, many improved algorithms have been proposed, such as fuzzy local information C-means clustering algorithm (FLICM). However, the segmentation results of FLICM are unsatisfactory when performed on complex images. To overcome this, a novel fuzzy clustering algorithm is proposed in this paper, and more information is utilized to guide the procedure of image segmentation. In the proposed algorithm, pixel relevance based on patch similarity will be investigated firstly, by which all information over the whole image can be considered, not limited to local context. Compared with Zhang et al. (Multimed Tools Appl 76(6):7869–7895, 2017a. 10.1007/s11042-016-3399-x) pixel relevance is unnecessary to be normalized, and much more information can play positive role in image segmentation. Experimental results show that the proposed algorithm outperforms current fuzzy algorithms, especially in enhancing the robustness of corresponding fuzzy clustering algorithms. [ABSTRACT FROM AUTHOR]

WT (Wavelet Transform) is considered as landmark for image compression because it represents a signal in terms of functions which are localized both in frequency and time domain. Wavelet sub-band coding exploits the self-similarity of pixels in images and arranges resulting coefficients in different sub-bands. A much simpler and fully embedded codec algorithm SPIHT (Set Partitioning in Hierarchical Trees) is widely used for the compression of wavelet transformed images. It encodes the transformed coefficients depending upon their significance comparative to the given threshold. Statistical analysis reveals that the output bit-stream of SPIHT comprises of long trail of zeroes that can be further compressed, therefore SPIHT is not advocated to be used as sole mean of compression. In this paper, wavelet transformed images have been initially compressed by using SPIHT technique and to attain more compression, the output bit streams of SPIHT are then fed to entropy encoders; Huffman and Arithmetic encoders, for further de-correlation. The comparison of two concatenations has been carried out by evaluating few factors like Bit Saving Capability, PSNR (Peak Signal to Noise Ratio), Compression Ratio and Elapsed Time. The experimental results of these cascading demonstrate that SPIHT combined with Arithmetic coding yields better compression ratio as compared to SPIHT cascaded with Huffman coding. Whereas, SPIHT once combined with Huffman coding is proved to be comparatively efficient. [ABSTRACT FROM AUTHOR]

To enhance the performance of image classification and improve the accuracy and efficiency of copy detection, this paper proposed an image hash algorithm based on QBFM moments and three-dimensional structure. Firstly, it used normalization to process the color image, and obtained the Gaussian fusion image and Laplace fusion image through multi-scale fusion, then extracted the QBFM features of two fusion images. At the same time, it directly extracted gradient information of the Gaus-sian fusion image in the RGB color space and constructed a three-dimensional model, used the concave convex point information of peak and valley curve of gradient from different perspectives to obtain the three-dimensional local structure features. Then, it disposed the three-dimensional model of gradient image by equidistant segmentation, Counted the number of pixels and variance of each section as the three-dimensional global structure features. Finally, it combined the QBFM features and three-dimensional features of the image and scrambled to form the final hash sequence. Experimental results show that the algorithm has a better balance between robustness and discrimination. Compared with the existing hash algorithms, it has good image classification performance. In the copy detection experiment, the algorithm has the best recall and precision. [ABSTRACT FROM AUTHOR]

Existing stereo matching algorithms suffer from issues such as susceptibility to distortion, weak noise resistance, and a high rate of mismatches in regions with weak textures and discontinuous disparities. To address these challenges, this paper proposes a stereo matching algorithm based on an improved census transform and minimum spanning tree (MST) cost aggregation. In the cost calculation phase, we employ a Gaussian-weighted transformation window and incorporate gradient and edge information to perform weighted fusion of the results. In the cost aggregation process, we introduce a collaborative adaptive window. Each pixel acquires information from the support window of the guided filter (GF) and other pixels within the MST. Furthermore, we integrate the SLIC superpixel segmentation algorithm into MST construction. These two components work synergistically to assign appropriate adaptive weights to pixels, facilitating coordinated cost volume aggregation. Different optimization methods are applied to address mismatched points of various types in post-disparity processing.Performance evaluation using the Middlebury dataset and KITTI dataset demonstrates that our proposed algorithm not only enhances matching accuracy in regions with discontinuous disparities and weak textures but also exhibits significantly improved robustness to interference. Additionally, the resulting disparity map displays smoother edges. • Cost Calculation Phase: Introducing a Gaussian-weighted transformation window, along with gradient and edge information, enables weighted fusion of results. • Cost Aggregation Process: Collaborative adaptive window introduction allows each pixel to gather information from the support window of the guided filter and neighboring pixels within the Minimum Spanning Tree(MST).Furthermore,the SLIC superpixel segmentation algorithm is integrated into the construction of the MST. • Post-Disparity Processing Optimization: Applying diverse optimization methods addresses mismatches among different types of points. [ABSTRACT FROM AUTHOR]

Object detection and segmentation can improve the accuracy of image recognition, but traditional methods can only extract the shallow information of the target, so the performance of algorithms is subject to many limitations. With the development of neural network technology, semantic segmentation algorithms based on deep learning can obtain the category information of each pixel. However, the algorithm cannot effectively distinguish each object of the same category, so YOLOMask, an instance segmentation algorithm based on complementary fusion network, is proposed in this paper. Experimental results on public data sets COCO2017 show that the proposed fusion network can accurately obtain the category and location information of each instance and has good real-time performance. [ABSTRACT FROM AUTHOR]

The traditional two-dimensional Otsu algorithm only considers the limitations of the maximum variance of between-cluster variance of the target class and background class; this paper proposes evolutionary game improved algorithm. Algorithm takes full consideration of own pixel cohesion of target and background. It can meet the same of maximum variance of between-cluster variance. To ensure minimum threshold discriminant function within the variance, this kind of evolutionary game algorithm searching space for optimal solution is applied. Experimental results show that the method proposed in this paper makes the detail of segmentation image syllabify and has better antijamming capability; the improved genetic algorithm which used searching optimal solution has faster convergence speed and better global search capability. [ABSTRACT FROM AUTHOR]

In this paper, we present a new algorithm for real-time single-object tracking in videos in unconstrained environments. The algorithm comprises two different components that are trained “in one shot” at the first video frame: a detector that makes use of the generalized Hough transform with color and gradient descriptors and a probabilistic segmentation method based on global models for foreground and background color distributions. Both components work at pixel level and are used for tracking in a combined way adapting each other in a co-training manner. Moreover, we propose an adaptive shape model as well as a new probabilistic method for updating the scale of the tracker. Through effective model adaptation and segmentation, the algorithm is able to track objects that undergo rigid and non-rigid deformations and considerable shape and appearance variations. The proposed tracking method has been thoroughly evaluated on challenging benchmarks, and outperforms the state-of-the-art tracking methods designed for the same task. Finally, a very efficient implementation of the proposed models allows for extremely fast tracking. [ABSTRACT FROM PUBLISHER]

The fuzzy C‐means clustering (FCM) algorithm is widely used in greyscale and colour image segmentation, especially in real colour images. However, in the process of interested regions extraction, it performs barely satisfactory due to the use of single distance metric in traditional FCM. In order to address the issue, a fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement (FCM‐SM) are proposed. First of all, since superpixel image adapts well to irregular image boundaries, the FCM algorithm is elevated to the superpixel level. Moreover, the superpixel merging module based on colour similarity is introduced to improve the traditional watershed transformation by incorporating more gradient and intensity information. Finally, the multi‐feature adaptive fusion measurement is designed in the clustering process to incorporate multiple sources of spatial information, which comprehensively measures the information from different fields to enhance the segmentation capability further. Experiments performed on Berkeley Segmentation Dataset and Brain Tumor Segmentation Dataset demonstrate that the proposed algorithm provides better segmentation results than benchmarks during the object extraction. [ABSTRACT FROM AUTHOR]

Classification of digital image content is mainly done by identifying low level image features such as corners and edges. The literature shows variety of algorithms for the identification of corner and non-corner pixels, important for objects' identification and image segmentation. However, all of these algorithms produce different results for same data and therefore, suitable for limited applications. This paper proposes a hybrid solution of combining complementary corner detection algorithms to improve image pixels' classification. This has been done by identifying the best detection algorithm for corner points with small and large angles and producing a hybrid algorithm by combining the latter two. Results have shown that Harris detector combined with Global and Local Curvature Points (GLC) improved the detection rate by 28% in synthetic images, but 50% in real images whereas, the combination of Shi's detection algorithm with GLC enhanced the detection rate by 25.9% in synthetic images and 123% in real images, showing a significant improvement. [ABSTRACT FROM AUTHOR]

Superpixel segmentation for PolSAR images can heavily decrease the number of primitives for subsequent interpretation while reducing the impact of speckle noise. However, traditional superpixel segmentation methods for PolSAR images only focus on the boundary adherence, the significance of superpixel segmentation will be lost when the accuracy is improved at the expense of computation efficiency. To solve this problem, this paper proposes a novel superpixel segmentation algorithm for PolSAR images based on hexagon initialization and edge refinement. First, the PolSAR image is initialized as hexagonal distribution, where the complexity of searching pixels for relabelling in the local regions can be reduced by 30% theoretically. Second, all pixels in the PolSAR image are initialized as unstable pixels based on the hexagonal superpixels, which can boost the segmentation performance in the heterogeneous regions and effectively maintain all the potential edge pixels. Third, the revised Wishart distance and the spatial distance are integrated as a distance measure to relabel all unstable pixels. Finally, the postprocessing procedure based on a dissimilarity measure is applied to generate the final superpixels. Extensive experiments conducted on both the simulated and real-world PolSAR images demonstrate the superiority and effectiveness of our proposed algorithm in terms of computation efficiency and segmentation accuracy, compared to three other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A novel efficient and robust methodology for quick face recognition by using Rule based Local Binary Pattern (RLBP) has been presented. The face image is said to be divided into a number of 3x3 regions, called as micro patterns, indicating the structure of the gray level pixels within a neighborhood to describe the spatial context of represented as rule number to evaluate the membership degree of the central pixel to the others within a neighborhood. A Local face distributor for each of the 3x3 neighborhood, called LBP descriptor, is obtained by applying Rules from which the RLBP feature distributions are extracted. Use of contributes to more than a single bin in the distribution of LBP values in the feature vector. The recognition is performed using a nearest neighbor classifier in the computed feature space. Experiments clearly show that the use of RLBP leads to improved reliable face recognition than to the original methods, LBP and Rule Based LBP. [ABSTRACT FROM AUTHOR]

In this paper, we address the problems of contour detection, bottom-up grouping, object detection and semantic segmentation on RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset (Silberman et al., ECCV, ). We propose algorithms for object boundary detection and hierarchical segmentation that generalize the $$gPb-ucm$$ approach of Arbelaez et al. (TPAMI, ) by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We train RGB-D object detectors by analyzing and computing histogram of oriented gradients on the depth image and using them with deformable part models (Felzenszwalb et al., TPAMI, ). We observe that this simple strategy for training object detectors significantly outperforms more complicated models in the literature. We then turn to the problem of semantic segmentation for which we propose an approach that classifies superpixels into the dominant object categories in the NYUD2 dataset. We design generic and class-specific features to encode the appearance and geometry of objects. We also show that additional features computed from RGB-D object detectors and scene classifiers further improves semantic segmentation accuracy. In all of these tasks, we report significant improvements over the state-of-the-art. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing image segmentation algorithm is complex and the segmentation precision is not high enough, this paper proposed a medical image clustering segmentation algorithm based on geometric distance optimization centroid and rough fuzzy C-means ( RFCM). First it set up the set of pixels represented by the soft set and calculated the distance between each pixel and the centroid. Then it grouped the pixels into clusters based on the minimum distance between the pixel and the centroid. In order to apply the soft set to the coarse fuzzy C-means, it defined a fuzzy soft set. It further converted the input image into a binary image, and selected an appropriate centroid by calculating the geometric distance of the connected region. Finally, using these new centroids to calculate the membership value of the updated pixel, it completed the fuzzy clustering. The performance of the proposed hybrid algorithm was evaluated on three medical databases, such as Allen Brain Atlas. The obtained Jaccard coefficient and segmentation accuracy (SA) were better than several comparison algorithms. Experiments show that the proposed clustering segmentation algorithm has good performance. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low segmentation accuracy in semantic segmentation of foggy images, this paper proposed a semantic segmentation method of foggy images that based on atrous space pyramid pooling, Xception module and residual network. On the one hand, this algorithm used the atrous space pyramid pooling and residual network to extract the multi-scale context features of the input image by the parallel convolution of multiple sampling rates and the convolution kernel size of 1 x 1. On the other hand, it used the decoder structure to classify the extracted features with Xception module after pretraining, and the prediction results of each pixel enhanced the refinement of the segmentation boundary and got the segmentation result with fine boundary. Experimental results show that the average intersection and union ratio of the proposed algorithm on foggy cityscapes dataset are 73. 03 %,73. 81 % and 74. 50%, and the segmentation performance is good. [ABSTRACT FROM AUTHOR]

• We propose a method for multiscale, border-guided 360 ∘ image oversegmentation. • We propose a superpixel method for 360 ∘ images that concern borders and regularity. • We propose an annotation-free metric for assessing 360 ∘ image segmentation methods. • The novel methods are compared and assessed under reference metrics and datasets. • Our methods achieve state-of-the-art results and are faster than peering approaches. Superpixels are fundamental in many visual computing applications, and most existing algorithms are designed to work with pinhole-based images. However, immersive applications are gaining visibility with the growing number of devices for capturing and visualizing 360 ∘ media. This paper introduces two fast and accurate superpixel algorithms tailored to the spherical domain. The methods are assessed under common figures of merit, and a benchmark annotated dataset. Additionally, an annotation-free evaluation metric for exploiting larger datasets is introduced. The methods introduced in this paper perform quantitatively close to or better than state-of-the-art approaches and are faster. [ABSTRACT FROM AUTHOR]

In order to resolve the disadvantages of fuzzy C-means (FCM) clustering algorithm for image segmentation, an improved Kernel-based fuzzy C-means (KFCM) clustering algorithm is proposed. First, the reason why the kernel function is introduced is researched on the basis of the classical KFCM clustering. Then, using spatial neighborhood constraint property of image pixels, an adaptive weighted coefficient is introduced into KFCM to control the influence of the neighborhood pixels to the central pixel automatically. At last, a judging rule for partition fuzzy clustering numbers is proposed that can decide the best clustering partition numbers and provide an optimization foundation for clustering algorithm. An adaptive kernel-based fuzzy C-means clustering with spatial constraints (AKFCMS) model for image segmentation approach is proposed in order to improve the efficiency of image segmentation. Various experiment results show that the proposed approach can get the spatial information features of an image accurately and is robust to realize image segmentation. [ABSTRACT FROM AUTHOR]

Highlights • Two interaction mechanisms are allowed: fully-user-supervised (BnS) or machine-assisted (BnQ). • The manifold ranking is integrated with the query-based interactive mechanism. • The foreground-object-confidence update rule is efficient and effective. • The average response time per-interaction is quite short. • The initial bounding box can be imprecise. This paper presents an efficient and effective interactive segmentation scheme for extracting the region of a foreground object in an image. Our goal is to design an interactive segmentation algorithm that unifies the bounding-box-based, seed-based, and query-based interaction mechanisms for pursuing (i) high efficiency in simple interaction mechanism, (ii) few interaction rounds, and (iii) short response time. The proposed algorithm starts with a user-provided bounding box and obtains candidate background superpixels for inferring the foreground object. Our algorithm tolerates imprecise bounding boxes and provides two kinds of interactions for acquiring correct labels from the user. The user can either input the seed/scribble annotations or label the algorithm-queried regions. Our algorithm selects the most uncertain region as a query, and this query-based interaction mechanism reduces the burden of the user on deciding suitable annotation locations. The average response time per-interaction of our algorithm is merely 0.014 s. Our experiments demonstrate that the algorithm achieves an efficient unified scheme for interactive image segmentation. [ABSTRACT FROM AUTHOR]

This paper presents a novel variational model based on fuzzy region competition and statistical image variation modeling for image segmentation. In the energy functional of the proposed model, each region is characterized by the pixel-level color feature and region-level spatial/frequency information extracted from various image domains, which are modeled by the windowed bit-plane-dependence probability models. To efficiently minimize the energy functional, we apply an alternating minimization procedure with the use of Chambolle's fast duality projection algorithm, where the closed-form solutions of the energy functional are obtained. Our method gives soft segmentation result via the fuzzy membership function, and moreover, the use of multi-domain statistical region characterization provides additional information that can enhance the segmentation accuracy. Experimental results indicate that the proposed method has a superior performance and outperforms the current state-of-the-art superpixel-based and deep-learning-based approaches. [ABSTRACT FROM AUTHOR]

This paper include the problem of segmenting an image into regions represent (objects), segment this object by define boundary between two regions using a connected component labeling. Then develop an efficient segmentation algorithm based on this method, to apply the algorithm to image segmentation using different kinds of images, this algorithm consist four steps at the first step convert the image gray level the are applied on the image, these images then in the second step convert to binary image, edge detection using Canny edge detection in third Are applie the final step is images. Best segmentation rates are (90%) obtained when using the developed algorithm compared with (77%) which are obtained using (ccl) before enhancement. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing is an important technique for remote sensing image exploitation. It aims to decompose a mixed pixel into a collection of spectrally pure components (called endmembers ), and their corresponding proportions (called fractional abundances ). In recent years, many studies have revealed that unmixing using spectral information alone does not sufficiently incorporate the spatial information in the remotely sensed hyperspectral image, as the pixels are treated as isolated entities without taking into account the existing local correlation among them. To address this issue, several spatial preprocessing methods have been developed to include spatial information in the spectral unmixing process. In this paper, we present a new spatial preprocessing method which presents several advantages over existing methods. The proposed method is derived from the Simple Linear Iterative Clustering (SLIC) method, which adapts the global search scope of the clustering into local regions. As a result, the spatial correlation and the spectral similarity are intrinsically incorporated at the clustering step, which results in O ( N ) computational complexity of the clustering procedure with N being the number of pixels in the image. First, a regional clustering is iteratively performed by using spatial and spectral information simultaneously. The obtained result is a set of clustered partitions that exhibit both spectral similarity and spatial correlation. Then, for each partition we select a subset of candidate pixels with high spectral purity. Finally, the obtained candidate pixels are gathered together and fed to a spectral-based endmember extraction method to extract the final endmembers and their corresponding fractional abundances. Our newly developed method naturally integrates the spatial and the spectral information to retain the most relevant endmember candidates. Our experimental results, conducted using both synthetic and real hyperspectral scenes, indicate that the proposed method can obtain accurate unmixing results with less than 0.5% of the number of pixels used by other state-of-the-art methods. This confirms the advantages of integrating spatial and spectral information for hyperspectral unmixing purposes. [ABSTRACT FROM AUTHOR]

We present a novel superpixel generation algorithm based on a new definition of geodesic distance, called bilateral geodesic distance. In contrast to the traditional geodesic distance, the new bilateral geodesic distance of two pixels considers the distance between their positions as well as their color difference. Superpixel generation is essentially a problem of clustering image pixels with respect to a set of properly selected seeds. We first use an adaptive hexagonal subdivision method to determine the initial seed-based image gradient. Then, we use the bilateral geodesic distance to measure the similarity between the pixels and the seeds. We apply an improved fast marching method to generate superpixels’ contour regions with the expansion velocities dependent on a new gradient formulation that depends on the seeds’ properties. The experimental results indicate that our algorithm is not only much faster than the structure-based method, which uses conventional geodesic distance, but also outperforms the existing methods in terms of region compactness and region boundary regularity. [ABSTRACT FROM AUTHOR]

Many techniques and software packages have been developed to segment individual cells within microscopy images, necessitating a robust method to evaluate images segmented into a large number of unique objects. Currently, segmented images are often compared with ground-truth images at a pixel level; however, this standard pixel-level approach fails to compute errors due to pixels incorrectly assigned to adjacent objects. Here, we define a per-object segmentation evaluation algorithm (POSEA) that calculates segmentation accuracy metrics for each segmented object relative to a ground truth segmented image. To demonstrate the performance of POSEA, precision, recall, and f-measure metrics are computed and compared with the standard pixel-level evaluation for simulated images and segmented fluorescence microscopy images of three different cell samples. POSEA yields lower accuracy metrics than the standard pixel-level evaluation due to correct accounting of misclassified pixels of adjacent objects. Therefore, POSEA provides accurate evaluation metrics for objects with pixels incorrectly assigned to adjacent objects and is robust for use across a variety of applications that require evaluation of the segmentation of unique adjacent objects. [ABSTRACT FROM AUTHOR]

• We present PSCT new data-driven sampling method based clustering algorithms. • PSCT output can be used for image segmentation and image reconstruction. • Image reconstructed with PSCT show similar performance than original images in our tests. In this paper, we describe Pixel Sampling Clustering Technique (PSCT), a data-driven sampling procedure used to reduce pixel sets. We view the pixels in an image as a high redundancy 3D space. We also refer to this space as our color model. Our method aims to retain a relevant sample of the data so it can act as a new smaller, hence more efficient, color model. PSCT uses a pair of fast density-based clustering algorithms in tandem. First, it applies Birch and then DBSCAN to keep the most densely represented colors. We cluster the resulting color model and use the labels to segment images. We also complement the sampling method with a refinement algorithm intended to improve color representation. In our paper, we show how to reconstruct images using our reduced color model. We also show that reconstructed images have enough information to perform image related learning tasks with almost the same accuracy than the original images but with only a small fraction of the data. We test our sampling method in three image related supervised and unsupervised tasks and compare them with state-of-the-art methods. For our experiments, we use two image datasets: MIT's Vision Texture Dataset and Berkeley's BSD500. [ABSTRACT FROM AUTHOR]