A FAST AND EFFECTIVE OCCLUSION DETECTION ALGORITHM

1995 ◽  
Vol 09 (03) ◽  
pp. 565-578
Author(s):  
WEE-SOON CHING
Keyword(s):  
Stereo Matching ◽  
Adaptive Method ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Occlusion Detection ◽  
Threshold Method ◽  
3D Computer Vision ◽  
3D Data ◽  
Fixed Threshold ◽  
Correlation Threshold

Occlusion detection is an important problem in 3D computer vision which uses multiple views, such as stereo vision. The presence of occlusion complicates the problem of vergence and the subsequent stereo matching in the generation of 3D data. This paper presents an approach which detects the presence of occlusion concurrently during the vergence process. The main limitation of the approach where the maximum correlation coefficient can be very high even when a significant amount of occlusions is present in the stereo images is shown. This paper presents an adaptive method of adjusting the correlation threshold with respect to the contrast-levels of the image being analyzed to alleviate this limitation. The proposed adaptive threshold method ensures that the sensitivity of detecting mismatches is less dependent upon the contrast-levels of the image being analyzed. The computational advantage of the proposed adaptive threshold method over the fixed threshold method is also presented. Experimental results which show the strengths of the proposed adaptive threshold method over the fixed threshold method on real scenes are given.

scholarly journals Local mean based adaptive thresholding to classify the cartilage and background superpixels

2019 ◽  
Vol 15 (1) ◽  
pp. 211
Author(s):  
Hong Seng Gan ◽  
Bakhtiar Al-Jefri Adb Salam ◽  
Aida Syafiqah Ahmad Khaizi ◽  
Muhammad Hanif Ramlee ◽  
Wan Mahani Wan Mahmud ◽  
...  
Keyword(s):  
Medical Image ◽  
Automatic Segmentation ◽  
Medical Image Processing ◽  
Adaptive Threshold ◽  
Integral Image ◽  
Threshold Method ◽  
Illumination Changes ◽  
Fixed Threshold ◽  
Local Mean ◽  
Background Area

<em><span>Semi-automatic segmentation is common in medical image processing because anatomical geometries demonstrated by human anatomical parts often requires manual supervision to provide desirable results. However, semi-automatic segmentation has been infamous for requiring excessive human intervention and time consuming. In order to reduce a forementioned problems, seed labels have been generated automatically using superpixels in our previous works. A fixed threshold method has been implemented to classify cartilage and background superpixels but this method is reported to lack the adaptiveness to changing image properties in 3D magnetic resonance image of knee. As a result, the coverage of background seeds are not sufficient to cover whole background area in some cases. In this work, we proposed a local mean based adaptive threshold method as a better alternative to the fixed threshold method. We calculated local mean for each block in an integral image and then use it to differentiate background superpixels from cartilage superpixels. The method is robust to illumination changes and simple to use. We tested the adaptive threshold on 35 knee images of different anatomical geometries and proved the proposed method could provide more comprehensive background seed labels distribution compared to fixed threshold method</span></em>

13‐4: Adaptive Threshold Method for Dither Noise Reduction in Response Time Acceleration for 8K LCD TV

2021 ◽  
Vol 52 (1) ◽  
pp. 161-164
Author(s):  
Ho-Joon Chung ◽  
Hong-Ki Kwon ◽  
Jin-Yong Park ◽  
Tae-Woo Kim ◽  
Hyeon-Su Park ◽  
...  
Keyword(s):  
Response Time ◽  
Noise Reduction ◽  
Adaptive Threshold ◽  
Threshold Method

scholarly journals An automated cloud detection method based on the green channel of total-sky visible images

2015 ◽  
Vol 8 (11) ◽  
pp. 4671-4679 ◽  
Author(s):  
J. Yang ◽  
Q. Min ◽  
W. Lu ◽  
W. Yao ◽  
Y. Ma ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Mie Scattering ◽  
Brightness Distribution ◽  
Adaptive Threshold ◽  
Cloud Detection ◽  
Blue Band ◽  
Green Channel ◽  
The Past ◽  
Visible Images ◽  
Cloud Mask

Abstract. Obtaining an accurate cloud-cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total-sky images. By analyzing the imaging principle of cameras, the green channel has been selected to replace the 2-D red-to-blue band for detecting cloud pixels from partly cloudy total-sky images in this study. The brightness distribution in a total-sky image is usually nonuniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT), which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, an adaptive threshold, and binarization. Five experimental cases show that the GBSAT algorithm produces more accurate retrieval results for all these test total-sky images.

scholarly journals AN OPTIONAL THRESHOLD WITH SVM CLOUD DETECTION ALGORITHM AND DSP IMPLEMENTATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 771-777 ◽  
Author(s):  
Guoqing Zhou ◽  
Xiang Zhou ◽  
Tao Yue ◽  
Yilong Liu
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Threshold Method ◽  
Dsp Implementation ◽  
Svm Algorithm

This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.

A WT-Based Edge Detection with Adaptive Threshold

2011 ◽  
Vol 204-210 ◽  
pp. 1386-1389
Author(s):  
Deng Yin Zhang ◽  
Li Xiao ◽  
Shun Rong Bo
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Value Iteration ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Image Edge ◽  
Threshold Setting

The existing edge detection algorithms with wavelet transform need to artificially set the threshold value and are lack of flexibility.To salve the limitations, in this paper, we propose a WT(wavelet transform)-based edge detection algorithm with adaptive threshold, which uses threshold value iteration method to achieve adaptive threshold setting. Comparison of experiment results for the CT image shows that the method which improve the clarity and continuity of the image edge can effectively distinguish edge and noise, and get more completely information of the edge. It has good application value in the fields of medical clinical diagnosis and image processing.

Visual interpretability analysis of Deep CNNs using an Adaptive Threshold method on Diabetic Retinopathy images

2021 ◽  
Author(s):  
George Ioannou ◽  
Tasos Papagiannis ◽  
Thanos Tagaris ◽  
Georgios Alexandridis ◽  
Andreas Stafylopatis
Keyword(s):  
Diabetic Retinopathy ◽  
Adaptive Threshold ◽  
Threshold Method

Comparison of the early aberration reporting system (EARS) W2 methods to an adaptive threshold method

2011 ◽  
Vol 30 (5) ◽  
pp. 489-504 ◽  
Author(s):  
John L. Szarka ◽  
Linmin Gan ◽  
William H. Woodall
Keyword(s):  
Reporting System ◽  
Adaptive Threshold ◽  
Threshold Method
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