Chunlin Jiao

Northwestern Polytechnical University

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Mantun Gao

Northwestern Polytechnical University

Lu Wang

Institute for Infocomm Research

Yikai Shi

Ningbo University

Jun Chu

Moscow State University

Hang Guo

Nanchang University

Jiaying Zhao

University of British Columbia

Ye Li

Shenzhen Institutes of Advanced Technology

Jun Miao

China Academy of Space Technology

Jing Cheng

Zhengzhou University of Light Industry

Xin Liu

Chinese Academy of Medical Sciences & Peking Union Medical College

Cooperative Institutions

Chinese Academy of Sciences

Nanchang University

Shenzhen Institutes of Advanced Technology

Nanchang Hangkong University

Xi'an University of Technology

Shenzhen University

National University of Defense Technology

Beijing University of Technology

Beijing University of Posts and Telecommunications

Shandong University

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Binocular stereo vision system design for lunar rover

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2007)

Jun Chu Chunlin Jiao Hang Guo Xiaoyu Zhang

In this paper, we integrate a pair of CCD cameras and a digital pan/title of two degrees of freedom into a binocular stereo vision system, which simulates the panoramic cameras system of the lunar rover. The constraints for placement and parameters choice of the stereo cameras pair are proposed based on science objective of Chang'e-IImission. And then these constraints are applied to our binocular stereo vision system and analyzed the location precise of it. Simulation and experimental result confirm the constraints proposed and the analysis of the location precise.

Stereo cameras

Computer stereo vision

Binocular disparity

Machine Vision

10.1117/12.743159

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Citations (2)

DeepSNMs for highly accelerated multi-contrast volumetric brain imaging

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2024)

Jiaying Zhao Sen Jia Jing Cheng Chunlin Jiao Zhuo‐Xu Cui

Motivation: The conventional multi-contrast 3D reconstruction process is time-consuming and lacks sufficient acceleration factors. Goal(s): To achieve highly accelerated multi-contrast brain imaging while enhancing reconstruction efficiency, the learnable CNN network is utilized. Approach: Deep learning regularized SNMs (Deep SNMs) is developed by unrolling parallel imaging reconstruction using spatial nulling maps (SNMs) with CNN regularization. The network is iteratively expanded by gradient descent blocks and 2D convolution blocks. Results: Compared to L1 regularized SNMs, the learnable CNN regularization simplifies reconstruction complexity and attains higher image quality. DeepSNMs achieves multi-contrast volumetric brain imaging reconstruction under caipirinha 9x and 12x acceleration. Impact: This work successfully accomplishes multi-contrast volumetric brain imaging with 9-fold and 12-fold caipirinha acceleration. By addressing the time-consuming challenge of reconstructing multi-contrast 3D images, this work effectively utilizes and integrates information from multiple contrasts concurrently.

10.58530/2024/2736

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A novel global relaxation iteration algorithm for panoramic annular 3D terrain registration

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2009)

Jun Chul Lu Wang Chunlin Jiao Jun Miao Guimei Zhang

It is necessary to reconstruct a large-scale landing-site mapping by recovering and registering the local scenes into a uniform annular scene for planetary exploration missions. This paper proposed a global relax iterative optimization method to registering the local scenes into a uniform annular scene. For this scheme, the transform matrix between any two adjacent 3D local scenes is fitted based on Carley transform. Subsequently, these local 3D scenes are registered into a uniform coordinate system using relax iterative optimization method. This optimization method has been tested on the image sequence of outdoor scenes. Experimental results show that the global registration means error decreases significantly from 1.33 meters to 0.002 meters in 47 images.

Image registration

10.1117/12.832010

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Color Image Segmentation in a Novel Dynamic Color Space

Chunlin Jiao Mantun Gao Yikai Shi

This paper presents a dynamic color model based on the electromagnetic wave theory to characterize the color feature of pixels, which simulates the continuous variety of visible light. In practice, the dynamic model can be discretized, and then we use a vector to represent the color feature of pixels in a K-dimension space, which is named the dynamic color space. To verify the dynamic space performance, an adopted segmentation method based on clustering techniques is proposed, which can be used to partition a color image in RGB space or in our dynamic color space. Experiments show that the segmentation results in our dynamic space is better than that in RGB space. Furthermore, the authors believe that the dynamic color space can be used to texture analysis or other image processing.

RGB color model

RGB color space

Color histogram

Color balance

10.1109/wcica.2008.4594555

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Citations (1)

MULTI-VIEW RECONSTRUCTION OF ANNULAR OUTDOOR SCENES FROM BINOCULAR VIDEO USING GLOBAL RELAXATION ITERATION

International Journal of Robotics and Automation (2011)

Jun Chu Peter Liu Chunlin Jiao Jun Miao Lu Wang

Binocular disparity

10.2316/journal.206.2011.3.206-3418

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Citations (6)

3D scene reconstruction based on the panoramic cameras system of the lunar rover

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2007)

Chunlin Jiao Mantun Gao Yikai Shi

To reconstruct a 3D scene around the lunar rover from stereo image-pairs captured by the panoramic cameras, based on which an intuitive platform can be put up for scientists to plan exploration commands, we set about to study the 3D scene reconstruction. This paper mainly presents a scheme of registering local scene models to reconstruction a large scene. When a few of local 3D scene models have been reconstructed respectively, we firstly find the common 3D point sets between every two adjacent local models based on edge detection and image matching, secondly fit the matrix of coordinate transformation employing the technique of separating rotation matrix and translate vector, and lastly register these local 3D models into a uniform coordinate system. In this scheme, we don't need to set a few of control points in a scene beforehand; and we determine the rotation matrix by a system of linear equations based on Cayley transformation. Experimental results of reconstructing indoor and outdoor scenes show that our scene registration method is feasible.

Transformation matrix

Rotation matrix

3D Reconstruction

Point set registration

10.1117/12.741269

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