Mr. Naotomo Tatematsu Profile

Naotomo Tatematsu

at Waseda Univ

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Proceedings Article | 23 January 2012 Paper

Accurate dense 3D reconstruction of moving and still objects from dynamic stereo sequences based on temporal modified-RANSAC and feature-cut

Naotomo Tatematsu, Jun Ohya

Proceedings Volume 8301, 830105 (2012) https://doi.org/10.1117/12.908037

KEYWORDS: Image segmentation, 3D modeling, Optical flow, 3D image processing, Clouds, Stereoscopic cameras, 3D acquisition, 3D image reconstruction, Data modeling, Optical tracking

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This paper improves the authors' conventional method for reconstructing the 3D structure of moving and still objects that are tracked in the video and/or depth image sequences acquired by moving cameras and/or range finder. The authors proposed a Temporal Modified-RANSAC based method [1] that (1) can discriminate each moving object from the still background in color image and depth image sequences acquired by moving stereo cameras or moving range finder, (2) can compute the stereo cameras' egomotion, (3) can compute the motion of each moving object, and (4) can reconstruct the 3D structure of each moving object and the background. However, the TMR based method has the following two problems concerning the 3D reconstruction: lack of accuracy of segmenting into each object's region and sparse 3D reconstructed points in each object's region. To solve these problems of our conventional method, this paper proposes a new 3D segmentation method that utilizes Graph-cut, which is frequently used for segmentation tasks. First, the proposed method tracks feature points in the color and depth image sequences so that 3D optical flows of the feature points in every N frames are obtained. Then, TMR classifies all the obtained 3D optical flows into regions (3D flow set) for the background and each moving object; simultaneously, the rotation matrix and the translation vector for each 3D flow set are computed. Next, Graph-Cut using the energy function that consists of color probability, structure probability and a-priori probability is performed so that pixels in each frame are segmented into object regions and the background region. Finally, 3D point clouds are obtained from the segmentation result image and depth image, and then the point clouds are merged using the rotation and translation from the N-th frame prior to the current frame so that 3D models for the background and each moving object are constructed with dense 3D point data.

Proceedings Article | 24 January 2011 Paper

Study of temporal modified-RANSAC based method for the extraction and 3D shape reconstruction of moving objects from dynamic stereo images and for estimating the camera pose

Naotomo Tatematsu, Jun Ohya

Proceedings Volume 7878, 78780E (2011) https://doi.org/10.1117/12.872277

KEYWORDS: Optical flow, 3D image processing, Stereoscopic cameras, 3D modeling, Cameras, Image segmentation, Optical tracking, 3D image reconstruction, Image analysis, 3D acquisition

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This paper proposes a Temporal Modified-RANSAC based method that can discriminate each moving object from the still background in the stereo video sequences acquired by moving stereo cameras, can compute the stereo cameras' egomotion, and can reconstruct the 3D structure of each moving object and the background. We compute 3D optical flows from the depth map and results of tracking feature points. We define "3D flow region" as a set of connected pixels whose 3D optical flows have a common rotation matrix and translation vector. Our Temporal Modified-RANSAC segments the detected 3D optical flows into 3D flow regions and computes the rotation matrix and translation vector for each 3D flow region. As opposed to the conventional Modified-RANSAC for only two frames, The Temporal Modified- RANSAC can handle temporal images with arbitrary length by performing the Modified-RANSAC to the set of a 3D flow region that classified in the latest frame and new 3D optical flows detected in the current frame iteratively. Finally, the 3D points computed from the depth map in all the frames are registered using each 3D flow region's matrix to the initial positions in the initial frame so that the 3D structures of the moving objects and still background are reconstructed. Experiments using multiple moving objects and real stereo sequences demonstrate promising results of our proposed method.

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