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How to improve the high resolution and remove the noise of composite infrared thermal wave detection image have become important research topics in machine vision. Therefore, this paper proposes a composite image enhancement algorithm based on edge adaptive total variation of direction. Using the improved adaptive directional total variation model to construct the guidance image of the guidance filter can provide better structural information for the guidance filter and conduct the guidance filter. This process is iterated to eliminate noise and ladder effect. Experimental results show that this algorithm is superior to other advanced methods in objective evaluation index and subjective vision. The algorithm proposed in this paper can not only effectively eliminate noise and ladder effect, but also highlight image details and structure information, which proves the effectiveness of this algorithm. Discover the cutting-edge algorithm for high-resolution improvement and noise elimination in infrared thermal wave detection images. Our innovative composite image enhancement technique, based on edge adaptive total variation, outperforms traditional methods. The improved directional model provides better structural information, optimizing the guidance filter process. Experimental results reveal its superiority in objective evaluation and subjective vision. This algorithm effectively eliminates noise and ladder effect while highlighting image details, validating its remarkable effectiveness. [ABSTRACT FROM AUTHOR]

Intelligent agriculture has become the development trend of agriculture in the future, and it has a wide range of research and application scenarios. Using machine learning to complete basic tasks for people has become a reality, and this ability is also used in machine vision. In order to save the time in the fruit picking process and reduce the cost of labor, the robot is used to achieve the automatic picking in the orchard environment. Cherry target detection algorithms based on deep learning are proposed to identify and pick cherries. However, most of the existing methods are aimed at relatively sparse fruits and cannot solve the detection problem of small and dense fruits. In this paper, we propose a cherry detection model based on YOLOv5s. First, the shallow feature information is enhanced by convolving the feature maps sampled by two times down in BackBone layer of the original network model to the input end of the second and third CSP modules. In addition, the depth of CSP module is adjusted and RFB module is added in feature extraction stage to enhance feature extraction capability. Finally, Soft- Non-Maximum Suppression (Soft-NMS) is used to minimize the target loss caused by occlusion. We test the performance of the model, and the results show that the improved YOLOv5s-cherry model has the best detection performance for small and dense cherry detection, which is conducive to intelligent picking. [ABSTRACT FROM AUTHOR]

Computer Vision (CV) has become an essential field in Artificial Intelligence applications. Object detection and recognition (ODR) is one of the fundamental tasks of computer vision implementations. However, developing an efficient ODR model is still a significant problem. The model's execution time and speed are the most critical features during the inference or detection and recognition process, which need to be improved using the latest object detection architectures. In this paper, the handwritten detection and recognition (HDR) model is developed based on previously known algorithms with their efficiency, such as Faster R-CNN and YOLOv4 in the first hand. On the other hand, two new models capable of detecting and recognizing handwritten digits using the latest ODR algorithm are proposed, one based on the latest YOLO family architecture (YOLOv5-HDR) with high speed and accuracy and the other using the transformers architecture (DETR). To the best of our knowledge, this is the first study to achieve a details comparison between YOLOv5 and transformers-based models in handwritten digit detection. Finally, the detailed performance analysis achieved by the paper proves that the YOLOv4-based model achieved the testing inference 13% faster than Faster R-CNN. However, the proposed YOLOv5-based model outperformed the YOLOv4 and the transformers-based one as it increased the testing execution time 25% faster than the YOLOv4, three times faster than the DETR model. A further adversarial attack test has been conducted to ensure the robust performance of the proposed model. Furthermore, numerical experiment results and their analyses demonstrate the robustness and effectiveness of the proposed YOLOv5-based model being the most stable for handwritten digit detection and recognition tasks. [ABSTRACT FROM AUTHOR]

In computer vision, automatically identifying and locating text in images or videos is an important task. Traditional text detection methods are not effective in detecting complex scenes in irregular rectangular areas such as text bending, spacing, and special shapes, which are mainly reflected in the fragmentation of text detection areas. In this paper, a text detection method based on edge attention mechanism is proposed to better adapt to complex scenes. The proposed method takes Encoder–Decoder as the core idea. First of all, an edge attention module is designed, including global attention and local attention. The global attention module is used to perceive the features of text regions and nontext regions, while the local attention module is used to learn the information of text boundaries. Then a multi-scale feature fusion process is designed, which can strengthen the edge information and key information of text regions. Finally, the model outputs probability maps and threshold maps, and generates high-precision binary maps of text regions. After experimental verification, the proposed method on the public data set significantly reduces the fragmentation of the detection area, improves the detection accuracy of the text area, and has better robustness for text detection scenes with unconventional rectangular areas. [ABSTRACT FROM AUTHOR]

Image segmentation (IE) in several disciplines of image processing and computer vision is an essential topic. Segmentation splits a picture into the areas or items that it constitutes. Image segmentation may be achieved with many approaches, some easier than others because of sophisticated programming requirements. The most common technique for segmenting pictures is edge detection (ED) based on sudden (locomotive) intensity fluctuations. This paper aims to study edge detection approaches for the division of images and acquired experimental findings, Sobel, Prewitt, Robert, CannyLoG (Laplacian of Gaussian). It is vital to ensure that picture segmentation algorithms deliver correct results quickly and efficiently for computer vision to reach its full potential. Computer vision approaches require more investigation in hierarchical architectural IoT networks created for seeing the world. In this work, the new way to provide joint image detection (JID) algorithm is to provide multi-scaling approaches for edge detection and segmentation using IoT edge computing (EC). This JID-EC method avoids the requirement to choose and track the edge explicitly. This study provides an overview of fundamental ideas, techniques, and algorithms common to segment images and edge detection, focusing on the segmentation and visualization of joint-articular cartilage images. The reason for this failure is that it is an image noise-sensitive high pass filter. The need for improved algorithms to meet a suitable value of low and high thresholds should thus be stressed for picture noise such as a canny edge, and the performance is achieved with an efficiency of 95.2%. [ABSTRACT FROM AUTHOR]

Curved surface mesh generation is a key step for many areas. Here, a mesh generation algorithm for closed curved surface based on Delaunay refinement is proposed. We focus on improving the shape quality of the meshes generated and making them conform to 2-manifold. The Delaunay tetrahedralization of initial sample is generated first, the initial surface mesh which is a subset of the Delaunay tetrahedralization can be achieved. A triangle is refined by inserting a new point if it is large or of bad quality. For each sample, we also check the triangles that adjoin it whether from a topological disk. If not, the largest triangle will be refined. Finally, the surface mesh is updated after a new point is inserted into the sample. The definition of mesh size function for surface mesh generation is also put in this paper. Meshing experiments of some models demonstrate that the new algorithm is advantageous in generating high quality surface mesh, the count of mesh is suitable and can well approximate the curved surface. The presented method can be used for a wide range of problems including computer graphics, computer vision and finite element method. [ABSTRACT FROM AUTHOR]

Computer vision-based recognition of Bangle vehicle license plates (LPs) is an arduous task in dirty and muddy situations. This paper proposes an efficient method for real-time computer vision-based recognition of Bangla vehicle LPs using contour analysis and prediction algorithms. The method initially applies gray scaling the input RGB images, histogram equalization to improve the grayscale image quality, edge detection using Sobel edge detector, and adaptive thresholding to convert it to a binary image. The system localizes the vehicle LP based on the maximum rectangular contour area and converts it into a predefined size. Noise removal technique using morphological dilation and erosion operation is used, followed by Gaussian filtering on binary image to improve the image quality further. The system clusters the two-lined LP into seven clusters. The sub-clustering is applied on specific clusters and makes 68 individual sub-clusters. The system extracts vector contour (VC) from each 68 individual classes. After VC extraction, the system normalizes it into a q predefined length. The system applies inter co-relation function (ICF) to categorize each sub-cluster to its previously defined individual class. For that, it calculates the maximum similarity between test and previously trained VCs. The system applies the dependency prediction algorithm in parallel to predict the whole string (district name) in the cluster-1 based on previously categorized class or classes (starting character or characters of the district part). (Metro) or (null) from cluster-2, "-" (hyphen) from cluster-3 and 6 are predicted automatically as their positions are fixed. The system is trained using 68 classes in which each class contains 100 samples generated by the augmentation technique. The system is tested using another set of 68 classes with a total of 6 8 × 1 0 0 = 6 8 0 0 images acquiring the recognition accuracy of 96.62% with the mean computational cost of 8.363 ms/f. The system is also tested using 500 vehicle whole Bangla LPs achieving the mean whole LP recognition accuracy of 95.41% with a mean computational cost of 35.803 ms/f. [ABSTRACT FROM AUTHOR]

A visual secret sharing (VSS) scheme is intended to share secret information in a group to avoid potential treat of interruption and modification. In this paper, we present a novel VSS scheme based on the improved local binary pattern (LBP) operator. It makes full use of local contrast features of LBP for concealing secret image data into different image shares, which can be used to recover the secret easily and exactly. By varying LBP extensions, we can design various kinds of VSS schemes for sharing secret information. Compared to the currently available VSS algorithms, the proposed scheme demonstrates better randomness in shares with less pixel expansion and exact determination in reconstruction with lower computational cost. [ABSTRACT FROM AUTHOR]

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on binocular computer vision systems that meet stringent response time and accuracy requirements. A key problem in these vision systems is stereo matching, which involves matching pixels from two input images in order to construct the output, a 3D map. Building upon the existing local stereo matching algorithms, this paper proposes a novel stereo matching algorithm that is based on a weighted guided filtering foundation. The proposed algorithm consists of three main steps; each step is designed with the goal of improving accuracy. First, the matching costs are computed using a unique combination of complementary methods (absolute difference, Census, and gradient algorithms) to reduce errors. Second, the costs are aggregated using an adaptive weighted guided image filtering method. Here, the regularization parameters are adjusted adaptively using the Canny method, further reducing errors. Third, a disparity map is generated using the winner-take-all strategy; this map is subsequently refined using a densification method to reduce errors. Our experimental results indicate that the proposed algorithm provides a higher level of accuracy in comparison to a collection of the existing state-of-the-art local algorithms. [ABSTRACT FROM AUTHOR]

Occlusion area detection is a crucial step affecting the performance of the binocular stereo matching algorithm, but the traditional method of occlusion area detection has two major problems, including left–right consistency detection (LRC). First, these algorithms must obtain the left and right disparity maps with precision. Second, these algorithms cannot detect the occlusion region at the image's borders. We propose the single view occlusion area detective (SVOAD) algorithm to detect these occlusion areas and better deal with them. The SVOAD can detect the area of occlusion from a single image, thereby reducing the computational cost. Additionally, the algorithm can detect the occlusion region in all image regions. This paper also improves the guided filter so that it works better with the end-to-end neural network and makes the SVOAD algorithm work better. [ABSTRACT FROM AUTHOR]

Background subtraction is a prerequisite and often the very first step employed in several high-level and real-time computer vision applications. Several parametric and non-parametric change detection algorithms employing multiple feature spaces have been proposed to date but none has proven to be robust against all challenges that can possibly be posed in a complex real-time environment. Amongst the varied challenges posed, illumination variations, shadows, dynamic backgrounds, camouflaged and bootstrapping artifacts are some of the well-known problems. This paper presents a light-weight hybrid change detection algorithm that integrates a novel combination of RGB color space and conditional YCbCr-based XCS-LBP texture descriptors (YXCS-LBP) into a modified pixel-based background model. The conditional employment of light-weight YXCS-LBP texture features with the modified Visual background extractor (ViBe) aiming at reduction in false positives, produces outperforming results without incurring much memory and computational cost. The random and time-subsampled update strategy employed with the proposed classification procedure ensures the efficient suppression of shadows and bootstrapping artifacts along with the complete retention of long-term static objects in the foreground masks. Comprehensive performance analysis of the proposed technique on publicly available Change Detection dataset (2014 CDnet dataset) demonstrates the superiority of the proposed technique over different state-of-the-art-methods against varied challenges. [ABSTRACT FROM AUTHOR]

Fiducial marker systems are composed of a number of patterns that are mounted in the environment and automatically detected by computer vision algorithms using digital techniques. Thus, these systems are valuable in augmented reality (AR), robot navigation, and other applications. This paper proposes a new AR marker called CH-marker, which uses Hamming check codes to encode multiple kinds of colors and restore binary codes in the squares occluded in the markers. The marker solves the registration failure, which occurs when the markers are partially occluded in dynamic scenes. Experiments showed that the proposed marker is effective, reliable, and can meet the application demand of AR. [ABSTRACT FROM AUTHOR]

In order to detect the type of vehicle seat and the missing part of the spring hook, this paper proposes an improved RANSAC-SURF method. First, the image is filtered by a Gauss filter. Second, an improved RANSAC-SURF algorithm is used to detect the types of vehicle seats. Extract the feature points of vehicle seats. The feature points are matched according to the improved RANSAC-SURF algorithm. Third, the image distortion of the vehicle seat is corrected by the method of perspective transformation. Determine whether the seat's spring hook is missing or not according to the absolute value of the gray difference between the image collected by the camera and the image of the normal installation. The experimental results show that the MSE of the Gauss filter under a 5 * 5 template is 19.0753, and the PSNR is 35.3261, which is better than that of the mean filter and the median filter. The total matching logarithm of feature points and the number of intersection points are 188 and 18, respectively, in the improved RANSAC-SURF matching algorithm. [ABSTRACT FROM AUTHOR]

Images captured in dim light are hardly satisfactory and increasing the International Organization for Standardization (ISO) for a short duration of exposure makes them noisy. The image restoration methods have a wide range of applications in the field of medical imaging, computer vision, remote sensing, and graphic design. Although the use of flash improves the lighting, it changed the image tone besides developing unnecessary highlight and shadow. Thus, these drawbacks are overcome using the image restoration methods that recovered the image with high quality from the degraded observation. The main challenge in the image restoration approach is recovering the degraded image contaminated with the noise. In this research, an effective algorithm, named T2FRF filter, is developed for the restoration of the image. The noisy pixel is identified from the input fingerprint image using Deep Convolutional Neural Network (Deep CNN), which is trained using the neighboring pixels. The Rider Optimization Algorithm (ROA) is used for the removal of the noisy pixel in the image. The enhancement of the pixel is performed using the type II fuzzy system. The developed T2FRF filter is measured using the metrics, such as correlation coefficient and Peak Signal to Noise Ratio (PSNR) for evaluating the performance. When compared with the existing image restoration method, the developed method obtained a maximum correlation coefficient of 0.7504 and a maximum PSNR of 28.2467dB, respectively. [ABSTRACT FROM AUTHOR]

The recognition rate of computer vision algorithms is highly dependent on the image quality. To enhance the visual quality of the images captured under high-dynamic range (HDR) scenes, we propose an efficient and adaptive tone mapping algorithm based on guided image filter (GIF). The HDR image is compressed adaptively according to its average luminance. Then we decompose it into a base layer and a detail layer using the guided image filter. We improve the base layer and enhance the detail layer simultaneously, and combine the two layers to get the final low-dynamic range (LDR) image. Since the parameters are linked with image statistics, they adaptively fit to various kinds of images. The objective evaluation results on HDR image sets demonstrate the superiority of our proposed algorithm. Meanwhile, the result of our algorithm can reduce the halo artifacts and preserve more detail by subjective observation. [ABSTRACT FROM AUTHOR]