scholarly journals Human binocular disparity estimation with natural stereo-images

2018 ◽  
Vol 18 (10) ◽  
pp. 993 ◽  
Author(s):  
David White ◽  
Johannes Burge
Keyword(s):  
Binocular Disparity ◽  
Disparity Estimation ◽  
Stereo Images

Efficient disparity estimation from stereo images using hybrid-guided image filter

2017 ◽  
Vol 66 (3) ◽  
pp. 139-151
Author(s):  
Khushboo Jain ◽  
Husanbir Singh Pannu ◽  
Kuldeep Singh ◽  
Avleen Malhi
Keyword(s):  
Disparity Estimation ◽  
Stereo Images ◽  
Image Filter ◽  
Guided Image Filter

scholarly journals Disparity estimation using Graph cuts for road applications

2021 ◽  
Vol 297 ◽  
pp. 01055
Author(s):  
Mohamed El Ansari ◽  
Ilyas El Jaafari ◽  
Lahcen Koutti
Keyword(s):  
Stereo Matching ◽  
Optimal Solution ◽  
Similarity Criterion ◽  
Graph Cuts ◽  
Search Space ◽  
Disparity Estimation ◽  
Stereo Images ◽  
Disparity Map ◽  
Current Frame ◽  
Edge Points

This paper proposes a new edge based stereo matching approach for road applications. The new approach consists in matching the edge points extracted from the input stereo images using temporal constraints. At the current frame, we propose to estimate a disparity range for each image line based on the disparity map of its preceding one. The stereo images are divided into multiple parts according to the estimated disparity ranges. The optimal solution of each part is independently approximated via the state-of-the-art energy minimization approach Graph cuts. The disparity search space at each image part is very small compared to the global one, which improves the results and reduces the execution time. Furthermore, as a similarity criterion between corresponding edge points, we propose a new cost function based on the intensity, the gradient magnitude and gradient orientation. The proposed method has been tested on virtual stereo images, and it has been compared to a recently proposed method and the results are satisfactory.

scholarly journals The effect of depth variation on disparity tasks in natural scenes

2017 ◽  
Author(s):  
Arvind V. Iyer ◽  
Johannes Burge
Keyword(s):  
Disparity Estimation ◽  
Stereo Image ◽  
Natural Scenes ◽  
Occlusion Detection ◽  
Spatial Integration ◽  
Stereo Images ◽  
Distance Measurements ◽  
Depth Variation ◽  
Disparity Detection ◽  
Local Depth

ABSTRACTLocal depth variation is a distinctive property of natural scenes and its effects on perception have only recently begun to be investigated. Here, we demonstrate how natural depth variation impacts performance in two fundamental tasks related to stereopsis: half-occlusion detection and disparity detection. We report the results of a computational study that uses a large database of calibrated natural stereo-images with precisely co-registered laser-based distance measurements. First, we develop a procedure for precisely sampling stereo-image patches from the stereo-images, based on the distance measurements. The local depth variation in each stereo-image patch is quantified by disparity contrast. Next, we show that increased disparity contrast degrades performance in half-occlusion detection and disparity detection tasks, and changes the size and shape of the optimal spatial integration areas (“receptive fields”) for computing the task-relevant decision variables. Then, we show that a simple binocular image statistic predicts disparity contrast in natural scenes. Finally, we report results on the most likely patterns of disparity variation in natural scenes. Our findings motivate computational and psychophysical investigations of the mechanisms that underlie disparity estimation in local regions of natural scenes.

scholarly journals Geometrically constrained sub-pixel disparity estimation from stereo images of the retinal fundus

2016 ◽  
Vol 2016 (5) ◽  
pp. 1-8
Author(s):  
Mohamad Kharboutly ◽  
Carlos Vázquez ◽  
Stéphane Coulombe ◽  
Jacques de Guise
Keyword(s):  
Disparity Estimation ◽  
Stereo Images ◽  
Geometrically Constrained ◽  
Retinal Fundus

Vehicle Detection and Disparity Estimation using Blended Stereo Images

2021 ◽  
pp. 1-1
Author(s):  
Changxin Zhou ◽  
Yazhou Liu ◽  
Pongsak Lasang ◽  
Quansen Sun
Keyword(s):  
Vehicle Detection ◽  
Disparity Estimation ◽  
Stereo Images
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