Optimizing ZNCC calculation in binocular stereo matching

Lin, Chuan; Li, Ya; Xu, Gelin; Cao, Yijun

doi:10.1016/j.image.2017.01.001

Cited by 23 publications

(5 citation statements)

References 48 publications

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“…The SAD similarity measure, which is used for the semiglobal technique, is not as effective as the ZNCC score in conjunction with census‐transform‐based matching, as employed by PaLPaBEL. Both the ZNCC score and census‐transform‐based matching are widely regarded as being robust similarity measures, especially in harsh lighting conditions (Lin et al., ).…”

Section: Resultsmentioning

confidence: 99%

A Stereo‐Matching Technique for Recovering 3D Information from Underwater Inspection Imagery

O’Byrne

Pakrashi

Schoefs

et al. 2017

Computer aided Civil Eng

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Underwater inspections stand to gain from using stereo imaging systems to collect three‐dimensional measurements. Although many stereo‐matching algorithms have been devised to solve the correspondence problem, that is, find the same points in multiple images, these algorithms often perform poorly when applied to images of underwater scenes due to the poor visibility and the complex underwater light field. This article presents a new stereo‐matching algorithm, called PaLPaBEL (Pyramidal Loopy Propagated BELief) that is designed to operate on challenging imagery. At its core, PaLPaBEL is a semiglobal method based on a loopy belief propagation message passing algorithm applied on a Markov random field. A pyramidal scheme is adopted that enables wide disparity ranges and high‐resolution images to be handled efficiently. For performance evaluation, PaLPaBEL is applied to underwater stereo images captured under various visibility conditions in a laboratory setting, and to synthetic imagery created in a virtual underwater environment. The technique is also demonstrated on stereo images obtained from a real‐world inspection. The successful results indicate that PaLPaBEL is well suited for underwater application and has value as a tool for the cost‐effective inspection of marine structures.

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Section: Resultsmentioning

confidence: 99%

A Stereo‐Matching Technique for Recovering 3D Information from Underwater Inspection Imagery

O’Byrne

Pakrashi

Schoefs

et al. 2017

Computer aided Civil Eng

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“…ZNCC is a widely used method in computer vision problems, and there have been recent efforts to simplify or speed up its calculation [ 61 ]. If a region within an image file is translated as a two-dimensional array of values, ZNCC can be utilized for video tracking or other computer vision applications.…”

Section: Methodsmentioning

confidence: 99%

Design of a Low-Power Embedded System Based on a SoC-FPGA and the Honeybee Search Algorithm for Real-Time Video Tracking

Soubervielle‐Montalvo

Perez-Cham

Puente

et al. 2022

Sensors

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Video tracking involves detecting previously designated objects of interest within a sequence of image frames. It can be applied in robotics, unmanned vehicles, and automation, among other fields of interest. Video tracking is still regarded as an open problem due to a number of obstacles that still need to be overcome, including the need for high precision and real-time results, as well as portability and low-power demands. This work presents the design, implementation and assessment of a low-power embedded system based on an SoC-FPGA platform and the honeybee search algorithm (HSA) for real-time video tracking. HSA is a meta-heuristic that combines evolutionary computing and swarm intelligence techniques. Our findings demonstrated that the combination of SoC-FPGA and HSA reduced the consumption of computational resources, allowing real-time multiprocessing without a reduction in precision, and with the advantage of lower power consumption, which enabled portability. A starker difference was observed when measuring the power consumption. The proposed SoC-FPGA system consumed about 5 Watts, whereas the CPU-GPU system required more than 200 Watts. A general recommendation obtained from this research is to use SoC-FPGA over CPU-GPU to work with meta-heuristics in computer vision applications when an embedded solution is required.

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“…The traditional prior-free methods directly estimate the parallax by addressing the similarity between the left and right image. The similarity refers to the hand-crafted features, illumination invariance, or other specially designed metrics such as zero-mean normalized cross correlation [35]. The global methods [11,[36][37][38] minimize a cost function that contains a similarity data term and a smoothness term.…”

Section: Related Workmentioning

confidence: 99%

BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching

Song¹,

Zhu²,

Liu³

et al. 2022

Preprint

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In stereoscope-based Minimally Invasive Surgeries (MIS), dense stereo matching plays an indispensable role in 3D shape recovery, AR, VR, and navigation tasks. Although numerous Deep Neural Network (DNN) approaches are proposed, the conventional prior-free approaches are still popular in the industry because of the lack of open-source annotated data set and the limitation of the task-specific pre-trained DNNs. Among the prior-free stereo matching algorithms, there is no successful real-time algorithm in none GPU environment for MIS. This paper proposes the first CPU-level real-time priorfree stereo matching algorithm for general MIS tasks. We achieve an average 17 Hz on 640 × 480 images with a singlecore CPU (i5-9400) for surgical images. Meanwhile, it achieves slightly better accuracy than the popular ELAS. The patchbased fast disparity searching algorithm is adopted for the rectified stereo images. A coarse-to-fine Bayesian probability and a spatial Gaussian mixed model were proposed to evaluate the patch probability at different scales. An optional probability density function estimation algorithm was adopted to quantify the prediction variance. Extensive experiments demonstrated the proposed method's capability to handle ambiguities introduced by the textureless surfaces and the photometric inconsistency from the non-Lambertian reflectance and dark illumination. The estimated probability managed to balance the confidences of the patches for stereo images at different scales. It has similar or higher accuracy and fewer outliers than the baseline ELAS in MIS, while it is 4-5 times faster. The code and the synthetic data sets are available at https://github.com/JingweiSong/BDIS-v2.

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Optimizing ZNCC calculation in binocular stereo matching

Cited by 23 publications

References 48 publications

A Stereo‐Matching Technique for Recovering 3D Information from Underwater Inspection Imagery

A Stereo‐Matching Technique for Recovering 3D Information from Underwater Inspection Imagery

Design of a Low-Power Embedded System Based on a SoC-FPGA and the Honeybee Search Algorithm for Real-Time Video Tracking

BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching

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