Three-dimensional video inpainting using gradient fusion and clustering

To overcome over-segmentation of Watershed transform, a novel improved Watershed algorithm based on adaptive marker-extraction is proposed. The original marker-based Watershed algorithm is improved by considering multiple feature information of local minima and adaptively selecting threshold. The proposed method consists of five steps: 1) Calculating gradient directly with color vectors; 2) Low-pass filtering of gradient image with BTPF; 3) Employing Hminima transform to extract true local minima whose depth is lower than that of threshold H, which is adaptively adjusted according to gradient image's statistical character. 4) Further marker-extraction being based on water basin scale. 5) Imposing the markers on the original gradient image as its minima; finally, Watershed transform is implied to the marked gradient image to segment the image. Experimental results show that, compared with other testing Watershed algorithms, the proposed method can more efficiently reduce over-segmentation and obtain better segmentation performance with lower computational complexity; in addition, it has better anti-noise performance and edge-location capability as well.

It is the key step of automatic apple picking and sorting to segment of the apple image in natural environment using image information processing technology. In order to improve the intellectualization of apple picking process and effectively extract the target of image segmentation, a novel image segmentation method of Huaniu apple based on pulse coupled neural network (PCNN) and watershed algorithm is proposed. Firstly, the PCNN model is improved and optimized on the basis of image RGB to L*A*B space conversion. Then, the improved PCNN combined with watershed algorithm was used to segment the apple image on considering the image texture and color feature information. The theoretical analysis and experimental results show that the novel method is effectively able to segment target image, and can preserve the image details and the edges as well. The proposed algorithm has better subjective vision effect and objective quality than the traditional watershed and other related algorithms.

Nowadays with a rapid growth of multimedia in-formation and advances in digital video technologies number of video captured is increasing day by day. The most of the recorded video which was generated is far more than the operators can watch, so identifying any interesting object or event has become very time consuming task, this kind of task might be feasible for some personal videos but this tedious task impossible when there will be endless video. So video synopsis acts as productive tool for indexing and browsing of such lengthy video. Video synopsis is a way of creating a compact representation of original video by presenting essential activities. In this paper video inpainting algorithm is proposed to generate video synopsis. Video inpainting is the method which aims to remove portion of video or object and restoring that missing portion by visually steady manner. In this paper video inpainting algorithm is implemented to inpaint the missing portion of a video sequences. This algorithm includes two stages one is simple processing and another one is video inpainting. In first stage foreground and background frame segmentation will be done. In second stage to inpaint the background objects image inpainting method and background replacement method will be used. To inpaint moving foreground objects background subtraction and object segmentation is used.

Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy and stagnation into local optimal solutions. Therefore, to alleviate these shortcomings, in this paper, we present a modified remora optimization algorithm (MROA) for global optimization and image segmentation tasks. We used Brownian motion to promote the exploration ability of ROA and provide a greater opportunity to find the optimal solution. Second, lens opposition-based learning is introduced to enhance the ability of search agents to jump out of the local optimal solution. To substantiate the performance of MROA, we first used 23 benchmark functions to evaluate the performance. We compared it with seven well-known algorithms regarding optimization accuracy, convergence speed, and significant difference. Subsequently, we tested the segmentation quality of MORA on eight grayscale images with cross-entropy as the objective function. The experimental metrics include peak signal-to-noise ratio (PSNR), structure similarity (SSIM), and feature similarity (FSIM). A series of experimental results have proved that the MROA has significant advantages among the compared algorithms. Consequently, the proposed MROA is a promising method for global optimization problems and image segmentation.

A novel dynamic Nelder-based Electric Eel Foraging algorithm for global optimization and pathological colorectal cancer image segmentation.

This paper presents an interactive algorithm for segmentation of natural images. The task is formulated as a problem of spline regression, in which the spline is derived in Sobolev space and has a form of a combination of linear and Green's functions. Besides its nonlinear representation capability, one advantage of this spline in usage is that, once it has been constructed, no parameters need to be tuned to data. We define this spline on the user specified foreground and background pixels, and solve its parameters (the combination coefficients of functions) from a group of linear equations. To speed up spline construction, K-means clustering algorithm is employed to cluster the user specified pixels. By taking the cluster centers as representatives, this spline can be easily constructed. The foreground object is finally cut out from its background via spline interpolation. The computational complexity of the proposed algorithm is linear in the number of the pixels to be segmented. Experiments on diverse natural images, with comparison to existing algorithms, illustrate the validity of our method.

Clustering-based segmentation algorithm is one of the common methods in image segmentation. However, how to avoid the influence of the initialization and the singular value is a problem we have to face. For overcoming this problem, we proposed a new image segmentation technology which is based on the AIC criterion and not affected by the singular values. The simulation results show that the method has good performance on stability and accuracy. Image segmentation is the key step in image analysis of computer vision and image processing field. It has been widely applied in various areas such as in the process of automating production, sensing images and biomedical images. The main goal of image segmentation is to simplify an image into segments that have a strong correlation with objects in the real world. The existing segmentation algorithm can be generally divided into three classes: edge detection, region- based segmentation and data clustering. Edge detection method relies on the edges of the object with background and the other objects. For the images with noise, it is difficult to separate the boundary and the target background (1). Region- based segmentation method segments an image into several meaningful sub-domains that are non-overlapping and same- nature. But it may lead to the over-segmentation, and has to combine with other methods to use (2). Data clustering method is based on the whole image and considers the distance between each data. Advantages of data clustering method are low complexity and easy to implement. Disadvantages are sensitive to the noise and selection of the initial centroids (3). Data clustering is one of the common techniques in image segmentation. Its purpose is to cluster pixels into several parts (regions) according to the feature of image. Based on the properties of clustering algorithm, researchers have proposed various image segmentation algorithm, such as based on hierarchical clustering, based on K-means clustering, based on fuzzy clustering, based on mean shift, and so on (3-5). Clustering analysis is an unsupervised process of partitioning a data set into subsets of similar data objects. The elementary principle is as far as possible to increase the difference between categories and reduce the difference within the category. In the process of image segmentation based on data clustering, thus the objective is that the similar pixels are divided into the same regions and the non-similar pixels are divided into different regions. However, Two difficulties we must face are that how to determine the cluster centers and avoid influence of noise. In this paper, a new image segmentation algorithm is proposed based on the AIC criterion. The new segmentation technology can achieve an ideal performance and not affected by the initialization of centroids and the singular values. The simulation results verify the efficiency of proposed algorithm.

One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresholding problem, using Kapur’s measure between class variance functions. The suggested DAOA is used to evaluate images, using eight standard test images from two different groups: nature and CT COVID-19 images. Peak signal-to-noise ratio (PSNR) and structural similarity index test (SSIM) are standard evaluation measures used to determine the accuracy of segmented images. The proposed DAOA method’s efficiency is evaluated and compared to other multilevel thresholding methods. The findings are presented with a number of different threshold values (i.e., 2, 3, 4, 5, and 6). According to the experimental results, the proposed DAOA process is better and produces higher-quality solutions than other comparative approaches. Moreover, it achieved better-segmented images, PSNR, and SSIM values. In addition, the proposed DAOA is ranked the first method in all test cases.

This paper studied the key technology of the extraction of urban road information using high-resolution QuickBird image. And this paper discussed the road information extraction algorithms, K-mean image clustering and segmentation algorithm, lambda-schedule image merging algorithm and the construction of knowledge database using spectral and shape features, from three angles, image segmentation algorithm, image merging algorithm and specific road information extraction algorithm. Studies showed that, after the introduction of the spectral and texture information, road extraction method reached a higher area and shape consistency, and provide a valuable reference for related research in the field.

With the development of the camera, video camera, infrared and various sensors such as visual information capture device with the technology, it has become a simple question that the visual information is obtained, but now in the era of information explosion, how to capture the interest of visual information in part become an important research direction, which is one of the most fundamental problems in the digital image processing. The image processing and segmentation is the bottom and the most basic work, only the good image segmentation result can perform image analysis work. This paper proposes the extracting method of the basketball match video games based on image segmentation algorithm.

This paper describes an efficient algorithm for color image segmentation based on a multiresolution application of a wavelet transform and watershed segmentation algorithm. The procedure toward complete segmentation consists of four steps: pyramid representation, image segmentation, region projection and region merging. First, pyramid representation creates multiresolution images using a wavelet transform. Second, image segmentation segments the lowest-resolution image of the pyramid using a watershed segmentation algorithm. Third, the segmented low-resolution image with label is projected into a full-resolution image (original image) by inverse wavelet transform. Finally, region merging merges the segmented regions using fuzzy similarity. Experimental results of the presented method can be applied to the segmentation of noise or degraded images as well as reduce over-segmentation.

Although human motion form capture is widely used in multiple fields, it often requires a significant amount of time and cost to learn how to operate the device during use.Therefore, this article attempted to apply computer vision (CV) technology and image segmentation algorithms to human motion form capture technology, simplifying the operation scheme and improving recognition accuracy and efficiency.This article provided an in-depth analysis of human motion form capture technology.Firstly, it identified several parts of the current human motion form capture technology that can be optimized, and introduced the effects of these optimized parts on human motion form capture in sports training.This article took the form capture of aerobics athletes as a sample and extracted 50 keyframe images containing aerobics scoring actions from 100 aerobics activity videos.The extraction interval for these keyframe images was at least 10 seconds.Next, this article used histogram equalization to enhance the image, while segmenting and recognizing the human motion forms of the five types of actions in the keyframe images, highlighting the level of action standards of athletes in aerobics.Finally, this article selected 6 key frame images containing different movements of aerobics athletes for comparative experimental analysis.In this experiment, both commonly used optical unlabeled capture techniques and motion morphology capture techniques combining CV and image segmentation algorithms were used to capture the human body in the image.The addition of CV technology and image segmentation has improved the overall performance of human motion morphology capture technology by approximately 26.02%.The integration of CV technology and image segmentation algorithms into human motion form capture technology has greatly improved image processing efficiency.At the same time, CV technology and image segmentation algorithms have also enabled better image processing accuracy in human motion form capture.

The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous fine-grained texture segments present in the images. At first, an initial seed point is found for the largest and most homogeneous segment of the image. This initial seed point of the segment is expanded using a region growing method. Other texture segments of the image are extracted analogously in turn. At the second stage, the procedure of merging the extracted segments belonging to the same texture class is performed. Then, the detected texture segments are input to a neural network with competitive layers which accomplishes more accurate delineation of the shapes of the extracted texture segments. The proposed segmentation procedure is fully unsupervised, i.e., it does not use any a priori knowledge on either the type of textures or the number of texture segments in the image. The research results in development of the segmentation algorithm realized as a computer program tested in a series of experiments that demonstrate its efficiency on grayscale natural scenes.

An improved watershed algorithm based on fuzzy threshold was proposed for the over-segmentation of watershed images due to their sensitivity to detail and noise. Firstly, the noise is suppressed by median filtering, and the image is preprocessed with multi-fuzzy threshold segmentation technology to obtain the obvious gray image of the edge region. Then, the main contour of the image is extracted by the fuzzy threshold algorithm. Finally, the image is segmented again by the watershed algorithm. Experimental results show that the new algorithm is much better than the traditional watershed algorithm and can effectively solve the over-segmentation problem of watershed algorithm. The segmentation results are more consistent with the visual characteristics of human beings, and it is an effective, accurate and practical image segmentation method.

Reversible image watermarking schemes are used to protect ownership and copyrights of digital images. This paper proposes a novel reversible image watermarking scheme based on adaptive image interpolation, segmentation and additive prediction error expansion (PEE). Proposed interpolation comprises of weighted average of neighboring pixels by allocating higher and lower weights to less and more distant neighboring pixel values respectively. The proposed adaptive image interpolation focuses on detection of edges thus minimizing artifacts imposed by interpolation. The idea of embedding varying amount of watermark bits in different image segments has been explored. Simple linear iterative clustering (SLIC) based image segmentation is performed to separate very sharp, sharp, smooth and very smooth regions in image. Higher number of watermark bits are embedded in sharp regions by using additive prediction error expansion embedding technique. Simulations of proposed and existing techniques were performed on different images and compared using embedding capacity (EC), peak signal to noise ratio (PSNR), computational efficiency, image quality, mean square error (MSE), normalized cross correlation (NCC) and structural similarity index (SSIM). The experimental results show that proposed scheme achieves better results in terms of EC, PSNR, computational efficiency, image quality, MSE, NCC and SSIM as compared to existing techniques.