Phase-correlation guided area matching for realtime vision and video encoding

Alba, Alfonso; Arce-Santana, Edgar R.; Aguilar-Ponce, Ruth M.; Campos-Delgado, Daniel U.

doi:10.1007/s11554-012-0243-z

Cited by 11 publications

(6 citation statements)

References 29 publications

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“…Table 2 demonstrates that the proposed algorithm improves most algorithms in the literature. The algorithms presented in [2,23,11] allow estimating dense disparity maps. However, the averaging effect due to large window sizes used generates errors at depth discontinuities.…”

Section: Resultsmentioning

confidence: 99%

An FPGA Stereo Matching Processor Based on the Sum of Hamming Distances

Aguilar-González

Arias-Estrada

2016

Lecture Notes in Computer Science

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Stereo matching is a useful algorithm to infer depth information from two or more of images and has uses in mobile robotics, threedimensional building mapping and three-dimensional reconstruction of objects. In area-based algorithms, the similarity between one pixel of an image (key frame) and one pixel of another image is measured using a correlation index computed on neighbors of these pixels (correlation windows). In order to preserve edges, the use of small correlation windows is necessary while for homogeneous areas, large windows are required. In addition, to improve the execution time, stereo matching algorithms often are implemented in dedicated hardware such as FPGA or GPU devices. In this article, we present an FPGA stereo matching processor based on the Sum of Hamming Distances (SHD). We propose a grayscalebased similarity criterion, which allows separating the objects and background from the correlation window. By using the similarity criterion, it is possible to improve the performance of any grayscale-based correlation coefficient and reach high performance for homogeneous areas and edges. The developed FPGA architecture reaches high performance compared to other real-time stereo matching algorithms, up to 10% more accuracy and enables to increase the processing speed near to 20 megapixels per second.

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Section: Resultsmentioning

confidence: 99%

An FPGA Stereo Matching Processor Based on the Sum of Hamming Distances

Aguilar-González

Arias-Estrada

2016

Lecture Notes in Computer Science

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“…The perception of the depth values of the points contained in a scene is one of the most important tasks of the computer vision systems and has been used in several applications such as positioning systems for mobile robots and both recognition, detection and three-dimensional reconstruction of objects [2,3,4,13,24].…”

Section: Introductionmentioning

confidence: 99%

FPGA implementation of an efficient similarity-based adaptive window algorithm for real-time stereo matching

Pérez-Patricio

Aguilar-González²

2015

J Real-Time Image Proc

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International audienceThe stereo matching is one of the most widely used algorithms in real-time image processing applications such as positioning systems for mobile robots, three-dimensional building mapping and both recognition, detection and three-dimensional reconstruction of objects. In area-based algorithms, the similarity between one pixel of the left image and one pixel of the right image is measured using a correlation index computed on vicinities of these pixels called correlation windows. In order to preserve edges, small windows need to be used. On the other hand, for homogeneous areas, large windows are required. Due to only local information is used, matching between primitives is difficult. In this article, FPGA implementing of an efficient similarity-based adaptive window algorithm for dense disparity maps estimation in real-time is described. In order to evaluate the proposed algorithm behavior, the developed FPGA architecture was simulated via ModelSim-Altera 6.6c using different synthetic stereo pairs and different sizes for correlation window. In addition, the FPGA architecture was implemented in an FPGA Cyclone IIEP2C35F672C6 embedded in an Altera development board DE2. The disparity maps are computed at a rate of 76 frames per second for stereo pairs of 1280×1024 pixel resolution and a maximum expected disparity equal to 15. The developed FPGA architecture offers better results with respect to the most of real-time area-based stereo matching algorithms reported in the literature, allows increasing the processing speed up to 93,061,120 pixels per second and enables it to be implemented in the majority of the medium-gamma FPGA devices

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“…Recently, another method was proposed to obtain a reduced basis with very little computational overhead [10]. This method is based on the estimation of the phase-only correlation (POC) function, which for two 1D discrete-time signals f (x) and g(x) is defined as…”

Section: Introductionmentioning

confidence: 99%

“…is a piece-wise constant function, then the POC function is a sum of distorted delta functions, centered at the locations which corresponds to the displacement values that the function d(x) can take (see [10] for details). This result can be extended to 2D signals (images) and higher-dimensional signals.…”

Section: Introductionmentioning

confidence: 99%

Efficiency Analysis of POC-Derived Bases for Combinatorial Motion Estimation

Reyes

Alba

Arce-Santana

2014

Image and Video Technology

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Motion estimation is a fundamental problem in many computer vision applications. One solution to this problem consists in defining a large enough set of candidate motion vectors, and using a combinatorial optimization algorithm to find, for each point of interest, the candidate which best represents the motion at the point of interest. The choice of the candidate set has a direct impact in the accuracy and computational complexity of the optimization method. In this work, we show that a set containing the most representative maxima of the phase-correlation function between the two input images, computed for different overlapping regions, provides better accuracy and contains less spurious candidates than other choices in the literature. Moreover, a preselection stage, based in a local motion estimation algorithm, can be used to further reduce the cardinality of the candidate set, without affecting the accuracy of the results.

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Phase-correlation guided area matching for realtime vision and video encoding

Cited by 11 publications

References 29 publications

An FPGA Stereo Matching Processor Based on the Sum of Hamming Distances

An FPGA Stereo Matching Processor Based on the Sum of Hamming Distances

FPGA implementation of an efficient similarity-based adaptive window algorithm for real-time stereo matching

Efficiency Analysis of POC-Derived Bases for Combinatorial Motion Estimation

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