Accurate dense stereo matching for road scenes

Zeglazi, Oussama; Rziza, Mohammed; Amine, Aouatif; Demonceaux, Cédric

doi:10.1109/icip.2017.8296375

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…We explore the proposed costs for stereo matching through two different algorithms: a stereo matching algorithm without aggregation stage and a fast local adaptive aggregation technique. These cost functions are then compared to the top cost functions

[ 22 ] and

[ 23 ]. The optimal parameter values that were proposed in [ 22 , 23 ] were retained.…”

Section: Resultsmentioning

confidence: 99%

“…These cost functions are then compared to the top cost functions

[ 22 ] and

[ 23 ]. The optimal parameter values that were proposed in [ 22 , 23 ] were retained. Experiments were conducted on the KITTI 2012 [ 12 ] and KITTI 2015 [ 13 ] training datasets in order to evaluate the proposed approach in the context of intelligent vehicles applications.…”

Section: Resultsmentioning

confidence: 99%

“…These distortions are generated while using the absolute color difference between corresponding pixels [ 22 ]. At each level of radiometric distortion, we compute the mean disparity errors for all KITTI training set for

[ 22 ], and

[ 23 ] cost functions. It can be visualized from the Figure 3 that the proposed cost

give the lowest error rate at all radiometric distortion levels.…”

Section: Resultsmentioning

confidence: 99%

“…The results of this study have proven that the cost function derived from the

or its variants, as the Cross-Comparison Census (

) combined with the mean sum of relative pixel intensity differences within a

window, provide overly a good performance on the KITTI 2012 benchmark. A variant of

cost function [ 23 ] was proposed in order to handle better the radiometric distortions. The authors claim that the proposed cost function outperforms the conventional cost functions on the KITTI 2012 benchmark.…”

Section: Related Workmentioning

confidence: 99%

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Structural Similarity Measurement Based Cost Function for Stereo Matching of Automotive Applications

et al. 2020

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The human visual perception uses structural information to recognize stereo correspondences in natural scenes. Therefore, structural information is important to build an efficient stereo matching algorithm. In this paper, we demonstrate that incorporating the structural information similarity, extracted either from image intensity (SSIM) directly or from image gradients (GSSIM), between two patches can accurately describe the patch structures and, thus, provides more reliable initial cost values. We also address one of the major phenomenons faced in stereo matching for real world scenes, radiometric changes. The performance of the proposed cost functions was evaluated within two stages: the first one considers these costs without aggregation process while the second stage uses the fast adaptive aggregation technique. The experiments were conducted on the real road traffic scenes KITTI 2012 and KITTI 2015 benchmarks. The obtained results demonstrate the potential merits of the proposed stereo similarity measurements under radiometric changes.

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[ 22 ] and

[ 23 ]. The optimal parameter values that were proposed in [ 22 , 23 ] were retained.…”

Section: Resultsmentioning

confidence: 99%

“…These cost functions are then compared to the top cost functions

[ 22 ] and

Section: Resultsmentioning

confidence: 99%

[ 22 ], and

[ 23 ] cost functions. It can be visualized from the Figure 3 that the proposed cost

give the lowest error rate at all radiometric distortion levels.…”

Section: Resultsmentioning

confidence: 99%

“…The results of this study have proven that the cost function derived from the

or its variants, as the Cross-Comparison Census (

) combined with the mean sum of relative pixel intensity differences within a

window, provide overly a good performance on the KITTI 2012 benchmark. A variant of

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Structural Similarity Measurement Based Cost Function for Stereo Matching of Automotive Applications

et al. 2020

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“…Pixels were grouped into super pixels, which were then used for matching cost aggregation by combining image gradient matching and census transform. As compared to other stereo algorithms, such as belief propagation, graph cut or semi global matching, the algorithm estimates the best possible [19].…”

Section: Related Workmentioning

confidence: 99%

Multi Cost Function Fuzzy Stereo Matching Algorithm for Object Detection and Robot Motion Control

Shetty,

Hegde,

Vaz

et al. 2023

Journal of Robotics and Control

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Stereo matching algorithms work with multiple images of a scene, taken from two viewpoints, to generate depth information. Authors usually use a single matching function to generate similarity between corresponding regions in the images. In the present research, the authors have considered a combination of multiple data costs for disparity generation. Disparity maps generated from stereo images tend to have noisy sections. The presented research work is related to a methodology to refine such disparity maps such that they can be further processed to detect obstacle regions. A novel entropy based selective refinement (ESR) technique is proposed to refine the initial disparity map. The information from both the left disparity and right disparity maps are used for this refinement technique. For every disparity map, block wise entropy is calculated. The average entropy values of the corresponding positions in the disparity maps are compared. If the variation between these entropy values exceeds a threshold, then the corresponding disparity value is replaced with the mean disparity of the block with lower entropy. The results of this refinement are compared with similar methods and was observed to be better. Furthermore, in this research work, the v-disparity values are used to highlight the road surface in the disparity map. The regions belonging to the sky are removed through HSV based segmentation. The remaining regions which are our ROIs, are refined through a u-disparity area-based technique. Based on this, the closest obstacles are detected through the use of k-means segmentation. The segmented regions are further refined through a u-disparity image information-based technique and used as masks to highlight obstacle regions in the disparity maps. This information is used in conjunction with a kalman filter based path planning algorithm to guide a mobile robot from a source location to a destination location while also avoiding any obstacle detected in its path. A stereo camera setup was built and the performance of the algorithm on local real-life images, captured through the cameras, was observed. The evaluation of the proposed methodologies was carried out using real life out door images obtained from KITTI dataset and images with radiometric variations from Middlebury stereo dataset.

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Two Layer Tensor Form Convolutional Sparse Coding for Stereo Matching

Cheng

Cui

2021

IEEE Access

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Stereo matching is an important research topic in the field of computer vision. It recovers depth information from a pair of color images. Unfortunately, converting multi-dimensional (more than two-dimensional) data into two-dimensional data, such formulations ignore the spatial structure of multidimensional images/data. Tensors can be used to describe high-dimensional data structure, which can retain the hidden structure of data, but cannot obtain the deep features that helps to improve the performance of the algorithm. Therefore, it is very important to establish a deep tensor model. In this paper, we propose a two layer tensor form convolutional sparse coding model, which can automatically learn the deep convolutional kernel. Based on the learned two layer convolutional kernels, a two-layer dictionary learning model is established. Then, a new weighted matching cost method is constructed, which combines shallow and deep features. The experimental results on the Middlebury benchmark v2 and Middlebury benchmark v3 show that the proposed two layer tensor convolutional sparse coding is effective for stereo matching.INDEX TERMS Stereo matching, high-order tensor, convolutional sparse coding, deep learning, dictionary learning.

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Accurate dense stereo matching for road scenes

Cited by 9 publications

References 21 publications

Structural Similarity Measurement Based Cost Function for Stereo Matching of Automotive Applications

Structural Similarity Measurement Based Cost Function for Stereo Matching of Automotive Applications

Multi Cost Function Fuzzy Stereo Matching Algorithm for Object Detection and Robot Motion Control

Two Layer Tensor Form Convolutional Sparse Coding for Stereo Matching

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