Efficient edge-preserving stereo matching

Çığla, Cevahir; Alatan, A. Aydın

doi:10.1109/iccvw.2011.6130315

Cited by 36 publications

(36 citation statements)

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“…Cigla et al [31] separately performed horizontal and vertical aggregations using a similar 1-D filter model with separable successive weighted summations along each direction. A simple exponential function is utilized for computing the similarity between two neighboring pixels.…”

Section: Related Local Algorithmsmentioning

confidence: 99%

Domain Transformation-Based Efficient Cost Aggregation for Local Stereo Matching

Pham

Jeon

2013

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Binocular stereo matching is one of the most important algorithms in the field of computer vision. Adaptive support-weight approaches, the current state-of-the-art local methods, produce results comparable to those generated by global methods. However, excessive time consumption is the main problem of these algorithms since the computational complexity is proportionally related to the support window size. In this paper, we present a novel cost aggregation method inspired by domain transformation, a recently proposed dimensionality reduction technique. This transformation enables the aggregation of 2-D cost data to be performed using a sequence of 1-D filters, which lowers computation and memory costs compared to conventional 2-D filters. Experiments show that the proposed method outperforms the state-of-the-art local methods in terms of computational performance, since its computational complexity is independent of the input parameters. Furthermore, according to the experimental results with the Middlebury dataset and realworld images, our algorithm is currently one of the most accurate and efficient local algorithms.

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Section: Related Local Algorithmsmentioning

confidence: 99%

Domain Transformation-Based Efficient Cost Aggregation for Local Stereo Matching

Pham

Jeon

2013

IEEE Trans. Circuits Syst. Video Technol.

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“…Thus, various methods that reduce the computational complexity and operate rapidly were proposed [12], [15]. Herein, we use four cost aggregation methods: Adapt Weight, Cost Filter, InfoPermeable, and DT Aggregation (DTAggr).…”

Section: Cost Aggregationmentioning

confidence: 99%

“…Here, ∆R, ∆G, and ∆B represent the difference value of two adjacent pixels, and  is the smoothness parameter [12].…”

Section: Infopermeablementioning

confidence: 99%

High‐Quality Stereo Depth Map Generation Using Infrared Pattern Projection

Jeong¹,

Chang²,

Lim³

et al. 2013

ETRI Journal

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In this paper, we present a method for obtaining a highquality 3D depth. The advantages of active pattern projection and passive stereo matching are combined and a system is established. A diffractive optical element (DOE) is developed to project the active pattern. Cross guidance (CG) and auto guidance (AG) are proposed to perform the passive stereo matching in a stereo image in which a DOE pattern is projected. When obtaining the image, the CG emits a DOE pattern periodically and consecutively receives the original and pattern images. In addition, stereo matching is performed using these images. The AG projects the DOE pattern continuously. It conducts cost aggregation, and the image is restored through the process of removing the pattern from the pattern image. The ground truth is generated to estimate the optimal parameter among various stereo matching algorithms. Using the ground truth, the optimal parameter is estimated and the cost computation and aggregation algorithm are selected. The depth is calculated and bad-pixel errors make up 4.45% of the non-occlusion area.

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“…At this time, we use cost combining with alpha-blending for the ADCensus, and the final raw cost that is the sum of the pattern cost (T 1 ) and the non-pattern cost (T 2 ). The information permeability filtering(PF) proposed by Cevahir Cigla et al [2] is an ASW approach that has simple parameters and provides constant operational time for calculating cost aggregation. However, because there is no proximity weight term, PF can encounter problems with images containing large untextured regions.…”

mentioning

confidence: 99%

“…First of all, we calculate raw cost volume using the AD-Census [2,6]. At this time, we use cost combining with alpha-blending for the ADCensus, and the final raw cost that is the sum of the pattern cost (T 1 ) and the non-pattern cost (T 2 ).…”

mentioning

confidence: 99%

Real-time Hybrid Stereo Vision System for HD Resolution Disparity Map

Chang

Jeong

Hwang

2014

Proceedings of the British Machine Vision Conference 2014

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Stereo matching is a traditional method used to obtain 3D depth information and has been studied for decades. However, it is still difficult to apply stereo matching algorithms to practical devices due to real-time issues as well as the technique's inability to adequately handle untextured regions. In this paper, we propose a hybrid stereo matching system to remedy the disadvantages of active and passive stereo vision.Stereo matching algorithm Following Scharstein's taxonomy [5], the stereo matching algorithm divides into four steps: matching cost computation, cost aggregation, disparity computation and disparity refinement. First of all, we calculate raw cost volume using the AD-Census [2,6]. At this time, we use cost combining with alpha-blending for the ADCensus, and the final raw cost that is the sum of the pattern cost (T 1 ) and the non-pattern cost (T 2 ). The information permeability filtering(PF) proposed by Cevahir Cigla et al. [2] is an ASW approach that has simple parameters and provides constant operational time for calculating cost aggregation. However, because there is no proximity weight term, PF can encounter problems with images containing large untextured regions. Modified information permeability(MPF), including a proximity weight term, is defined in Section 2.2. Our proposed system uses WTA as disparity computation, because it is very simple algorithm.Stereo vision system design The proposed system is composed of the stereo head and the stereo emulator. The stereo head includes a LVDS module, an LD/LED projection module and two CMOS sensors. Input from the two CMOS sensors is received in the form of 10-bit monochrome images at a resolution of 1280 x 720 pixels at a rate of 60fps. Image streams from the left & right cameras are transferred to the FPGA board through the LVDS module, which includes control signals. The stereo emulator is based on FPGA modules to obtain disparity maps from stereo pairs. The stereo emulator contains a deserialized module, a USB 3.0 controller and four FPGAs. The USB 3.0 controller transfers the results to the computer and is received parameters for FPGA. Figure 1 (a) represents our entire system. The hybrid system captures a pair of pattern images and an alternating pair of non-pattern images to evaluate disparity. The CMOS sensors are synchronized with LD/LED projection module, so that the images are obtained in operating time with the LD (pattern) in one frame and with the LED (non-pattern) in the next frame.Implemeting stereo algorithm Figure 1 (b) is a block diagram of the hybrid stereo vision system. The stereo matching algorithm consists of four processing elements implemented in each single FPGA. The PrePE is first composed through rectification(based on the Caltech method) and image filtering(based on bilateral filtering). The MPE Left generates a left-referenced disparity that is implemented using the algorithm described in Section 2. Certain modifications are taken into account while implementing the system on the FPGA, and usually appear in the cost ag...

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Efficient edge-preserving stereo matching

Cited by 36 publications

References 1 publication

Domain Transformation-Based Efficient Cost Aggregation for Local Stereo Matching

Domain Transformation-Based Efficient Cost Aggregation for Local Stereo Matching

High‐Quality Stereo Depth Map Generation Using Infrared Pattern Projection

Real-time Hybrid Stereo Vision System for HD Resolution Disparity Map

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