Abstract
The evaluation of disparity (range) maps includes the selection of an objective image quality (or error) measure. Among existing measures, the percentage of bad matched pixels is commonly used. However, it requires a disparity error tolerance and ignores the relationship between range and disparity. In this research, twelve error measures are characterized in order to provide the bases to select accurate stereo algorithms during the evaluation process. Adaptations of objective quality measures for disparity maps’ accuracy evaluation are proposed. The adapted objective measures operate in a manner similar to the original objective measures, but allow special handling of missing data. Additionally, the adapted objective measures are sensitive to errors in range and surface structure, which cannot be measured using the bad matched pixels. Their utility was demonstrated by evaluating a set of 50 stereo disparity algorithms known in the literature. Consistency evaluation of the proposed measures was performed using the two conceptually different stereo algorithm evaluation methodologies—ordinary ranking and partition and grouping of the algorithms with comparable accuracy. The evaluation results showed that partition and grouping make a fair judgment about disparity algorithms’ accuracy.
Highlights
A stereo correspondence algorithm uses a stereo image pair as an input and produces an estimated disparity map as an output [1,2]
The results vary according to the choice of the objective algorithm, where the AdaptWeight algorithm provides disparity estimations that are closer to the ground-truth data than the TreeDP disparity map calculations, which is valid for the previously described Structural SIMilarity Index (SSIM)-based objective measures
In order to perform image disparity algorithm evaluation, two methodologies were applied: Middlebury’s methodology and the A∗ groups methodology. The ranking of these algorithms using pixel-based objective measures was done by sorting the average ranks obtained for different stereo image pairs (Tsukuba, Venus, Teddy and Cones) and criteria; this means that for the 12 rank values’ averaging while using SSIM-based and proposed local-based objective measures, the algorithms ranking was done by sorting the average ranks obtained for the four stereo image pairs
Summary
A stereo correspondence algorithm uses a stereo image pair as an input and produces an estimated disparity map (a new image) as an output [1,2]. The accuracy of stereo correspondence algorithms can be assessed by the evaluation of disparity maps, either qualitatively or quantitatively. A quantitative (or objective) approach is robust against several human-related biasing factors, offering advantages over a qualitative (subjective) approach This assessment has practical applications such as component and procedure comparison, parameter tuning, supports decision-making by researchers and practitioners, and in general, to measure the progress in the field. The results of objective measures are used as inputs for quantitative evaluation methodologies, which aim to evaluate the performance of stereo algorithms. Among the existing quantitative evaluation methodologies, Middlebury’s methodology is commonly used [12] It uses the percentage of BMP as the error measure. The section will describe the methodologies and dataset used for disparity maps’ evaluation This is followed by the sections for experimental evaluation of known and the newly proposed objective error measures.
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