scholarly journals Application of Video Processing Technology Based on Diffusion Equation Model in Basketball Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yafeng Feng ◽  
Xianguo Liu
Keyword(s):  
Event Detection ◽  
Anisotropic Diffusion ◽  
Active Contour ◽  
Active Contour Model ◽  
Motion Blur ◽  
Block Matching ◽  
Video Image ◽  
Diffusion Method ◽  
Snake Model ◽  
Contour Model

Video event detection and annotation work is an important content of video analysis and the basis of video content retrieval. Basketball is one of the most popular types of sports. Event detection and labeling of basketball videos can help viewers quickly locate events of interest and meet retrieval needs. This paper studies the application of anisotropic diffusion in video image smoothing, denoising, and enhancement. An improved form of anisotropic diffusion that can be used for video image enhancement is analyzed. This paper studies the anisotropic diffusion method for coherent speckle noise removal and proposes a video image denoising method that combines anisotropic diffusion and stationary wavelet transform. This paper proposes an anisotropic diffusion method based on visual characteristics, which adds a factor of video image detail while smoothing, and improves the visual effect of diffusion. This article discusses how to apply anisotropic diffusion methods and ideas to video image segmentation. We introduced the classic watershed segmentation algorithm and used forward-backward diffusion to process video images to reduce oversegmentation, introduced the active contour model and its improved GVF Snake, and analyzed the idea of how to use anisotropic diffusion and improve the GVF Snake model to get a new GGVF Snake model. In the study of basketball segmentation of close-up shots, we propose an improved Hough transform method based on a variable direction filter, which can effectively extract the center and radius of the basketball. The algorithm has good robustness to basketball partial occlusion and motion blur. In the basketball segmentation research of the perspective shot, the commonly used object segmentation method based on the change area detection is very sensitive to noise and requires the object not to move too fast. In order to correct the basketball segmentation deviation caused by the video noise and the fast basketball movement, we make corrections based on the peak characteristics of the edge gradient. At the same time, the internal and external energy calculation methods of the traditional active contour model are improved, and the judgment standard of the regional optimal solution and segmentation validity is further established. In the basketball tracking research, an improved block matching method is proposed. On the one hand, in order to overcome the influence of basketball’s own rotation, this article establishes a matching criterion that has nothing to do with the location of the area. On the other hand, this article improves the diamond motion search path based on the basketball’s motion correlation and center offset characteristics to reduce the number of searches and improve the tracking speed.

scholarly journals Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Guodong Wang ◽  
Jie Xu ◽  
Qian Dong ◽  
Zhenkuan Pan
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Higher Order ◽  
Medical Image Segmentation ◽  
Diffusion Method ◽  
Split Bregman ◽  
Contour Model ◽  
The Difference

Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active contour model can converge to the edge of the image even with the intensity inhomogeneities. Because of the introduction of Laplace information, the difference scheme becomes more difficult. To enhance the efficiency of the segmentation, the fast Split Bregman algorithm is designed for the segmentation implementation. The performance of our method is demonstrated through numerical experiments of some medical image segmentations with intensity inhomogeneities.

scholarly journals UNSUPERVISED AUTOMATIC BUILDING EXTRACTION USING ACTIVE CONTOUR MODEL ON UNREGISTERED OPTICAL IMAGERY AND AIRBORNE LIDAR DATA

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 181-188 ◽  
Author(s):  
T. H. Nguyen ◽  
S. Daniel ◽  
D. Guériot ◽  
C. Sintès ◽  
J.-M. Le Caillec
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Airborne Lidar ◽  
Training Data ◽  
Building Extraction ◽  
Snake Model ◽  
Urban Scenes ◽  
Contour Model ◽  
Optical Imagery ◽  
Airborne Lidar Data

<p><strong>Abstract.</strong> Automatic extraction of buildings in urban scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly with the emergence of LiDAR systems since mid-1990s. However, in reality, this task is still very challenging due to the complexity of building size and shape, as well as its surrounding environment. Active contour model, colloquially called snake model, which has been extensively used in many applications in computer vision and image processing, has also been applied to extract buildings from aerial/satellite imagery. Motivated by the limitations of existing snake models dedicated to the building extraction, this paper presents an unsupervised and automatic snake model to extract buildings using optical imagery and an unregistered airborne LiDAR dataset, without manual initial points or training data. The proposed method is shown to be capable of extracting buildings with varying color from complex environments, and yielding high overall accuracy.</p>

The Concave Contour Detection Based on GVFsnake

2010 ◽  
Vol 108-111 ◽  
pp. 1296-1301
Author(s):  
Jie Cao ◽  
Xiao Jun Liu ◽  
Zong Li Liu
Keyword(s):  
Active Contour ◽  
Field Experiments ◽  
Active Contour Model ◽  
Research Field ◽  
Contour Detection ◽  
Gradient Vector Flow ◽  
External Energy ◽  
Edge Information ◽  
Snake Model ◽  
Contour Model

Active contour model is an important research field in computer vision and many researchers studied the variational method in recent years. The traditional snake model is unable to converge to the concave area and it has a lower convergence. By improving the external energy, researchers introduced a gradient vector flow active contour model (GVFsnake). Several standard images are used to segmenting experiments, and the results show that GVF has obvious advantages compared with traditional snake model in the iteration number of force field. Experiments show that the method is faster and better to converge in the concave area. The edge information can be kept well and diffused more quickly.

scholarly journals Active Contour Model with Adaptive weighted function for Robust Image Segmentation under Biased Conditions

2021 ◽  
pp. 114811
Author(s):  
Aditi Joshi ◽  
Mohammed Saquib Khan ◽  
Asim Niaz ◽  
Farhan Akram ◽  
Hyun Chul Song ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Weighted Function ◽  
Contour Model ◽  
Robust Image

Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model

2021 ◽  
pp. 1-19
Author(s):  
Maria Tamoor ◽  
Irfan Younas
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Heart Diseases ◽  
Medical Image Segmentation ◽  
Gaussian Kernel ◽  
Initial Contour ◽  
Automated Method ◽  
Contour Model

Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.

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