Wavelet-based image segment representation

2002 ◽  
Vol 38 (19) ◽  
pp. 1091 ◽  
Author(s):  
Liu Ying ◽  
S. Ranganath ◽  
Xiaofang Zhou
Keyword(s):  
Image Segment

scholarly journals Intelligent Image Segment for Material Composition Detection

2017 ◽  
Vol 100 ◽  
pp. 04026
Author(s):  
Xiaodan Liang ◽  
Na Lin ◽  
Hanning Chen ◽  
Wenxin Liu
Keyword(s):  
Material Composition ◽  
Image Segment

Improved Content-Color-Dependent Screening (CCDS): Adaptive Bilateral Filtering and Color-Aware Sobel Edge Detector

2021 ◽  
Vol 2021 (16) ◽  
pp. 252-1-252-7
Author(s):  
Yang Yan ◽  
Jan P. Allebach
Keyword(s):  
Digital Image ◽  
Bilateral Filter ◽  
Bilateral Filtering ◽  
Segmentation Method ◽  
Edge Detector ◽  
Number Of Clusters ◽  
Image Segment ◽  
Continuous Tone

In previous work [1] , content-color-dependent screening (CCDS) determines the best screen assignments for either regular or irregular haltones to each image segment, which minimizes the perceived error compared to the continuous-tone digital image. The model first detects smooth areas of the image and applies a spatiochromatic HVS-based model for the superposition of the four halftones to find the best screen assignment for these smooth areas. The segmentation is not limited to separating foreground and background. Any significant color regions need to be segmented. Hence, the segmentation method becomes crucial. In this paper, we propose a general segmentation method with a few improvements: The number of K-means clusters is determined by the elbow method to avoid assigning the number of clusters manually for each image. The noise removing bilateral filter is adaptive to each image, so the parameters do not need to be tested and adjusted based on the visual output results. Also, some color regions can be clearly separated out from other color regions by applying a color-aware Sobel edge detector.

Wavelet in POCS for image segment representation

2003 ◽  
Vol 39 (19) ◽  
pp. 1379
Author(s):  
Y. Liu ◽  
S. Ranganath
Keyword(s):  
Image Segment

scholarly journals Method for Improving an Image Segment in a Video Stream Using Median Filtering and Blind Deconvolution Based on Evolutionary Algorithms

2020 ◽  
pp. paper80-1-paper80-11
Author(s):  
Andrey Trubakov ◽  
Anna Trubakova
Keyword(s):  
Evolutionary Algorithms ◽  
Blind Deconvolution ◽  
Main Idea ◽  
Video Stream ◽  
Poor Quality ◽  
Surveillance Systems ◽  
Focal Distance ◽  
Noise Component ◽  
Image Segment ◽  
Software Product

Video surveillance systems, dash cameras and security systems have become an inescapable part of the most institutions ground environment. Their main purpose is to prevent incidents and to analyze the situation in case of extemporaneous events. Though as often as not it is necessary to increase an image segment many times over to investigate some incidents. Sometimes it is dozens of times. However, the obtained material is mostly of poor quality. This is connected either with noise or resolution characteristics, including focal distance. The paper considers an approach for improving image segments, which were obtained after multiple zooming. The main idea of the proposed solution is to use methods of blind deconvolution. In this case, the selection of restoration parameters is carried out using evolutionary algorithms with automatic evaluation of the result. That seems like the most important detail here is pre-processing besides noise minimization within the image, because when the image is repeatedly enlarged the effect of the noise component also increases. To avoid this thing, we suggest using ordinal statistics and average convolution for a series of images. The proposed solution was implemented as a software product, and its operation was tested on a number of video segments made under different shooting conditions. The results are presented at the end of this article.

Recognition and Tracking of Large Seam Based on Robotic Vision

2000 ◽  
Author(s):  
Hong Yue ◽  
Lixin Sun ◽  
Kai Li ◽  
Jianru Shi
Keyword(s):  
Image Processing ◽  
Tracking System ◽  
Seam Tracking ◽  
Welding Quality ◽  
Large Structure ◽  
The Real ◽  
Robotic Vision ◽  
Weld Joints ◽  
Image Segment

Abstract For the seams of large Structure, the low accordance of joint openings usually results in displacement of the real welding track from the robot teaching track, which will affect the welding quality of the structure. It has very important meanings to apply robotic vision for recognizing the feature parameters of the weld joints. A set of seam tracking system is presented. In order to remove arc noise, the methods of LOG filter and image segment filter are used in image processing.

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