A Shadow Elimination Algorithm Based on HSV Spatial Feature and Texture Feature

Song, Ranran; Liu, Min; Wu, Minghu; Wang, Juan; Liu, Cong

doi:10.1007/978-3-319-59463-7_58

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…Using the dark channel prior analysis method of guided filtering, the information enhancement processing of volleyball player's spike take-off action image is carried out, and (Ω, F, f(x), P) is set as the standardized parameter value in the detailed feature distribution domain space of volleyball player's spike take-off action image. Based on the histogram control of HSV color space [24], the output HSV color space parameter information is obtained as S � 1, 2, ..., N { }, which represents the color feature component of volleyball player's spike take-off action image. Combined with the method of over-enhancement defect compensation, the detailed feature detection function of volleyball player's spike take-off action image is shown in formula (15):…”

Section: Detail Enhancement Processing Of Image Action Feature Pointsmentioning

confidence: 99%

Recognition Method of Volleyball Players’ Spike and Take-Off Action Based on DTW Algorithm

Zhang

2022

Mobile Information Systems

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In order to improve the recognition ability of volleyball players’ spike take-off, and thus promote the accuracy of volleyball spike, this article puts forward the recognition technology of volleyball players’ spike take-off based on DTW algorithm combined with image recognition technology. The transmission relation model of detail features of volleyball players’ spike take-off action images is constructed, and the edge contour features of volleyball players’ spike take-off action images are detected and processed by detail wavelet feature decomposition and DWT algorithm. Using prior Atlas knowledge instead of dark primary color, the detailed information and color of the image are obtained. Through the method of filtering and maintaining, the image is guided to filter the details of the input volleyball player’s spike and take-off action image. Sobel operator is used to detect the weak edge information of the animation image. In the edge area, high-order moment feature decomposition and wavelet feature separation are used to enhance the details of the volleyball player’s spike and take-off action image and detect and enlarge the key action feature points, so as to improve the ability of enhancing and identifying the details of the action feature points of the image. The test shows that this method can effectively solve the noise and error in the process of recognizing the spike and take-off action of volleyball players, reduce the recognition error, and improve the ability to accurately locate the spike and take-off force point.

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Section: Detail Enhancement Processing Of Image Action Feature Pointsmentioning

confidence: 99%

Recognition Method of Volleyball Players’ Spike and Take-Off Action Based on DTW Algorithm

Zhang

2022

Mobile Information Systems

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“…RGB color space is designed for the color luminance principle, which is poorly descriptive for the color of reflective objects, and HSV color space is more suitable for the human visual system compared to RGB color space [9] .In HSV color space, H, S and V channels are independent of each other, H indicates hue, S indicates saturation and V indicates brightness, thus it can be seen that HSV color space separates color information and luminance information in the image well by channel, and for highly reflective objects, image enhancement can be completed by adjusting S channel and V channel. In this paper, we use the CV2 library in python-opencv to convert the RGB color space to HSV color space.…”

Section: Rgb Color Space and Hsv Color Space Conversionmentioning

confidence: 99%

Research on adaptive image enhancement algorithm in laser stripe extraction

Yan

Chen²,

Mu³

et al. 2022

2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022)

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In line structured light vision measurement, an adaptive image enhancement algorithm is proposed to address the problem that the conventional image enhancement algorithm is ineffective in enhancing the laser stripe features in the unevenly illuminated images acquired by the system. The algorithm separates the channels in HSV color space, processes the separated S and V channels separately using an iterative adaptive histogram equalization algorithm to enhance the image saturation and brightness, and merges the equalized S1 and V1 channels with the original H channel to convert the image to RGB color space to complete the image enhancement. The experimental result shows that the information entropy index value S of the proposed algorithm is higher than those of other comparative algorithms, and the algorithm performs better in laser stripe image enhancement with better robustness.

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“…However, a dynamic shadow, caused by a moving object, has a critical impact for accurately detecting moving objects, since it has the same motion properties as the moving object and is tightly connected to it. Shadows can be often removed from images of the sequence using their observed properties such as color, edges and texture or applying a model based on prior information such as illumination conditions and moving object shape [35,47,48]. However, dynamic shadows are still difficult to be distinguished from moving objects, especially for outdoor environment where the background is usually complex.…”

Section: Shadowsmentioning

confidence: 99%

Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges

Gawande¹,

Hajari²,

Golhar³

2020

Recent Trends in Computational Intelligence

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Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers' primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. These challenges occur at three different levels of pedestrian detection, viz. video acquisition, human detection, and its tracking. The challenges in acquiring video are, viz. illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc. These results in lower recognition rate. A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter. The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed.

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A Shadow Elimination Algorithm Based on HSV Spatial Feature and Texture Feature

Cited by 4 publications

References 11 publications

Recognition Method of Volleyball Players’ Spike and Take-Off Action Based on DTW Algorithm

Recognition Method of Volleyball Players’ Spike and Take-Off Action Based on DTW Algorithm

Research on adaptive image enhancement algorithm in laser stripe extraction

Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges

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