A Stereo Matching Algorithm Based on Four‐Moded Census and Relative Confidence Plane Fitting

Men, Yubo; Zhang, Guoyin; Men, Chaoguang; Li, Xiang; Ma, Ning

doi:10.1049/cje.2015.10.024

Cited by 16 publications

(15 citation statements)

References 8 publications

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“…Since the tangential distortion is small, the main consideration in this paper is the radial distortion [13]. At the same time, the use of epipolar constraint for binocular correction can make the feature points lie on the epipolar line in both images [14], which greatly accelerates the calculation speed and reduces errors matches [15]. The effect pictures before and after binocular correction are shown in Figure 6 and Figure 7:…”

Section: Camera Processing Modulementioning

confidence: 99%

Binocular intelligent following robot based on YOLO-LITE

Zheng

2021

MATEC Web Conf.

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In order to solve the problem that the deep neural network model is large in scale, the calculation time is too long, and the real-time performance is severely limited when combined with embedded devices, so studied the intelligent follower robot system based on YOLO-LITE algorithm combined with Raspberry Pi 3B+. The system mainly includes camera processing, target detection and other modules. Obtained the internal and external parameters of the camera through calibration, and according to these parameters to correct the binocular camera. Recognized and located the target in each frame of image, calculated the distance from the camera to the target and the center location error, and driven the car to move. The experimental results show that the following car has excellent real-time performance, the average detection frame rate can reach 20Fps, and the average detection accuracy can reach more than 80%.

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Section: Camera Processing Modulementioning

confidence: 99%

Binocular intelligent following robot based on YOLO-LITE

Zheng

2021

MATEC Web Conf.

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“…In the measurement space, a plane can be expressed as [20] ffalse(sans-serifΘ,Pfalse)=Ax+By+Cz+D=0 where the vector sans-serifΘ=false[A,B,C,Dfalse] contains the plane parameters and P=false(x,y,zfalse) represents a point on the plane.…”

Section: Calibration Principlementioning

confidence: 99%

“…In the measurement space, a plane can be expressed as [ 20 ]

where the vector

contains the plane parameters and

represents a point on the plane.…”

Section: Calibration Principlementioning

confidence: 99%

A Novel Calibration Method of Articulated Laser Sensor for Trans-Scale 3D Measurement

Kang

Duan

et al. 2019

Sensors

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The articulated laser sensor is a new kind of trans-scale and non-contact measurement instrument in regular-size space and industrial applications. These sensors overcome many deficiencies and application limitations of traditional measurement methods. The articulated laser sensor consists of two articulated laser sensing modules, and each module is made up of two rotary tables and one collimated laser. The three axes represent a non-orthogonal shaft architecture. The calibration method of system parameters for traditional instruments is no longer suitable. A novel high-accuracy calibration method of an articulated laser sensor for trans-scale 3D measurement is proposed. Based on perspective projection models and image processing techniques, the calibration method of the laser beam is the key innovative aspect of this study and is introduced in detail. The experimental results show that a maximum distance error of 0.05 mm was detected with the articulated laser sensor. We demonstrate that the proposed high-accuracy calibration method is feasible and effective, particularly for the calibration of laser beams.

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“…Me et al [13] used a four-mode census transform stereo matching method to improve the matching quality. This method used bidirectional constraint dynamic programming and relative confidence plane fitting.…”

Section: Introductionmentioning

confidence: 99%

Stereo Matching with Spatiotemporal Disparity Refinement Using Simple Linear Iterative Clustering Segmentation

Huang

Liu

2021

Electronics

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Stereo matching is a challenging problem, especially for computer vision, e.g., three-dimensional television (3DTV) or 3D visualization. The disparity maps from the video streams must be estimated. However, the estimated disparity sequences may cause undesirable flickering errors. These errors result in poor visual quality for the synthesized video and reduce the video coding information. In order to solve this problem, we here propose a spatiotemporal disparity refinement method for local stereo matching using the simple linear iterative clustering (SLIC) segmentation strategy, outlier detection, and refinements of the temporal and spatial domains. In the outlier detection, the segmented region in the initial disparity is used to distinguish errors in the binocular disparity. Based on the color similarity and disparity difference, we recalculate the aggregated cost to determine adaptive disparities to recover the disparity errors in disparity sequences. The flickering errors are also effectively removed, and the object boundaries are well preserved. Experiments using public datasets demonstrated that our proposed method creates high-quality disparity maps and obtains a high peak signal-to-noise ratio compared to state-of-the-art methods.

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A Stereo Matching Algorithm Based on Four‐Moded Census and Relative Confidence Plane Fitting

Cited by 16 publications

References 8 publications

Binocular intelligent following robot based on YOLO-LITE

Binocular intelligent following robot based on YOLO-LITE

A Novel Calibration Method of Articulated Laser Sensor for Trans-Scale 3D Measurement

Stereo Matching with Spatiotemporal Disparity Refinement Using Simple Linear Iterative Clustering Segmentation

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