A robust cost function for stereo matching of road scenes

Miron, Alina; Aïnouz, Samia; Rogozan, Alexandrina; Bensrhair, Abdelaziz

doi:10.1016/j.patrec.2013.11.009

Cited by 31 publications

(28 citation statements)

References 23 publications

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“…However, Yang et al applied a bilateral filter (i.e., a type of edge preserving filter) to improve the flattening of edges and to smooth areas near depth discontinuities. Recently, Miron et al [26] tested various matching cost functions in their stereo disparity map algorithms for intelligent vehicle applications. They concluded that the SD algorithm produced the largest error.…”

Section: Squared Differences (Sd)mentioning

confidence: 99%

Literature Survey on Stereo Vision Disparity Map Algorithms

2016

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This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondence algorithms performed in 2002. To assist future researchers in developing their own stereo matching algorithms, a summary of the existing algorithms developed for every stage of processing is also provided. The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. By contrast, a hardware-based implementation requires one or more additional processors for its processing module, such as graphical processing unit or a field programmable gate array. This literature survey also presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings.

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Section: Squared Differences (Sd)mentioning

confidence: 99%

Literature Survey on Stereo Vision Disparity Map Algorithms

2016

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“…By using the combination of multiple single similarity measures into a composite similarity measure, it has been proven to be an effective method to calculate the matching cost [7][8][9][10]. The adaptive multi-cost approach proposed in this work defines a novel multi-cost function to calculate the raw matching score and employs an adaptive window aggregation strategy to filter the cost volume.…”

Section: Initial Disparity Map Estimationmentioning

confidence: 99%

Accurate Dense Stereo Matching Based on Image Segmentation Using an Adaptive Multi-Cost Approach

Men

et al. 2016

Symmetry

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This paper presents a segmentation-based stereo matching algorithm using an adaptive multi-cost approach, which is exploited for obtaining accuracy disparity maps. The main contribution is to integrate the appealing properties of multi-cost approach into the segmentation-based framework. Firstly, the reference image is segmented by using the mean-shift algorithm. Secondly, the initial disparity of each segment is estimated by an adaptive multi-cost method, which consists of a novel multi-cost function and an adaptive support window cost aggregation strategy. The multi-cost function increases the robustness of the initial raw matching costs calculation and the adaptive window reduces the matching ambiguity effectively. Thirdly, an iterative outlier suppression and disparity plane parameters fitting algorithm is designed to estimate the disparity plane parameters. Lastly, an energy function is formulated in segment domain, and the optimal plane label is approximated by belief propagation. The experimental results with the Middlebury stereo datasets, along with synthesized and real-world stereo images, demonstrate the effectiveness of the proposed approach.

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“…However, they usually produce less accurate depth values. A wide range of stereo matching algorithms have been proposed and their performance has been examined in various surveys [1,2]. A stereo matching algorithm can be performed in four major steps : cost computation, cost aggregation, disparity computation and disparity refinement [3].…”

Section: Introductionmentioning

confidence: 99%

Accurate dense stereo matching for road scenes

Zeglazi

Rziza

Amine

et al. 2017

2017 IEEE International Conference on Image Processing (ICIP)

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Stereo matching task is the core of applications linked to the intelligent vehicles. In this paper, we present a new variant function of the Census Transform (CT) which is more robust against radiometric changes in real road scenes. We demonstrate that the proposed cost function outperforms the conventional cost functions using the KITTI benchmark 1. The cost aggregation method is also updated for taking into account the edge information. This enables to improve significantly the aggregated costs especially within homogenous regions. The Winner-Takes-All (WTA) strategy is used to compute disparity values. To further eliminate the remainder matching ambiguities, a post-processing step is performed. Experiments were conducted on the new Middlebury 2 dataset, as well as on the real road traffic scenes of the KITTI database. Obtained disparity results have demonstrated that the proposed method is promising.

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A robust cost function for stereo matching of road scenes

Cited by 31 publications

References 23 publications

Literature Survey on Stereo Vision Disparity Map Algorithms

Literature Survey on Stereo Vision Disparity Map Algorithms

Accurate Dense Stereo Matching Based on Image Segmentation Using an Adaptive Multi-Cost Approach

Accurate dense stereo matching for road scenes

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