scholarly journals Hardware Design Considerations for Edge-Accelerated Stereo Correspondence Algorithms

VLSI Design ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Christos Ttofis ◽  
Theocharis Theocharides
Keyword(s):  
Computer Vision ◽  
Processing Speed ◽  
Search Space ◽  
Depth Information ◽  
Stereo Correspondence ◽  
Disparity Map ◽  
Real Time Processing ◽  
Support Weight ◽  
Design Considerations ◽  
Adaptive Support

Stereo correspondence is a popular algorithm for the extraction of depth information from a pair of rectified 2D images. Hence, it has been used in many computer vision applications that require knowledge about depth. However, stereo correspondence is a computationally intensive algorithm and requires high-end hardware resources in order to achieve real-time processing speed in embedded computer vision systems. This paper presents an overview of the use of edge information as a means to accelerate hardware implementations of stereo correspondence algorithms. The presented approach restricts the stereo correspondence algorithm only to the edges of the input images rather than to all image points, thus resulting in a considerable reduction of the search space. The paper highlights the benefits of the edge-directed approach by applying it to two stereo correspondence algorithms: an SAD-based fixed-support algorithm and a more complex adaptive support weight algorithm. Furthermore, we present design considerations about the implementation of these algorithms on reconfigurable hardware and also discuss issues related to the memory structures needed, the amount of parallelism that can be exploited, the organization of the processing blocks, and so forth. The two architectures (fixed-support based versus adaptive-support weight based) are compared in terms of processing speed, disparity map accuracy, and hardware overheads, when both are implemented on a Virtex-5 FPGA platform.

scholarly journals PDANet: Self-Supervised Monocular Depth Estimation Using Perceptual and Data Augmentation Consistency

2021 ◽  
Vol 11 (12) ◽  
pp. 5383
Author(s):  
Huachen Gao ◽  
Xiaoyu Liu ◽  
Meixia Qu ◽  
Shijie Huang
Keyword(s):  
Data Augmentation ◽  
State Of The Art ◽  
Depth Estimation ◽  
Input Image ◽  
Depth Information ◽  
Disparity Map ◽  
Estimation Model ◽  
Absolute Relative Error ◽  
Texture Region ◽  
Monocular Depth

In recent studies, self-supervised learning methods have been explored for monocular depth estimation. They minimize the reconstruction loss of images instead of depth information as a supervised signal. However, existing methods usually assume that the corresponding points in different views should have the same color, which leads to unreliable unsupervised signals and ultimately damages the reconstruction loss during the training. Meanwhile, in the low texture region, it is unable to predict the disparity value of pixels correctly because of the small number of extracted features. To solve the above issues, we propose a network—PDANet—that integrates perceptual consistency and data augmentation consistency, which are more reliable unsupervised signals, into a regular unsupervised depth estimation model. Specifically, we apply a reliable data augmentation mechanism to minimize the loss of the disparity map generated by the original image and the augmented image, respectively, which will enhance the robustness of the image in the prediction of color fluctuation. At the same time, we aggregate the features of different layers extracted by a pre-trained VGG16 network to explore the higher-level perceptual differences between the input image and the generated one. Ablation studies demonstrate the effectiveness of each components, and PDANet shows high-quality depth estimation results on the KITTI benchmark, which optimizes the state-of-the-art method from 0.114 to 0.084, measured by absolute relative error for depth estimation.

Hardware friendly adaptive support-weight approach for stereo matching

2016 ◽  
Author(s):  
Zuoxun Hou ◽  
Pei Han ◽  
Hongwei Zhang ◽  
Ran An
Keyword(s):  
Stereo Matching ◽  
Support Weight ◽  
Adaptive Support

Automated Design of Image Operators that Detect Interest Points

2008 ◽  
Vol 16 (4) ◽  
pp. 483-507 ◽  
Author(s):  
Leonardo Trujillo ◽  
Gustavo Olague
Keyword(s):  
Computer Vision ◽  
Search Space ◽  
Search Problem ◽  
Interest Points ◽  
Fitness Evaluation ◽  
Modern Computer ◽  
New Perspective ◽  
Processing Techniques ◽  
Point Detection ◽  
Optimization Search

This work describes how evolutionary computation can be used to synthesize low-level image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence, and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo and Olague, 2006a,b) by presenting 15 new operators that were synthesized through the GP-based search. Some of the synthesized operators can be regarded as improved manmade designs because they employ well-known image processing techniques and achieve highly competitive performance. On the other hand, since the GP search also generates what can be considered as unconventional operators for point detection, these results provide a new perspective to feature extraction research.

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.

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