Mesh decomposition using motion information from animation sequences

Tong-Yee Lee; Ping-Hsien Lin; Shaur-Uei Yan; Chun-Hao Lin

doi:10.1002/cav.79

A Reduced Camera SLAM Approach for Indoor and Outdoor Navigation Using Laser Information for Landmark Initialization and Relative Motion Information

Proceedings of the 2016 International Technical Meeting of The Institute of Navigation ◽

10.33012/2016.13452 ◽

2016 ◽

Cited By ~ 2

Author(s):

Manuel Popp ◽

Jamal Atman ◽

Georg Scholz ◽

Jan Ruppelt ◽

Gert F. Trommer

Keyword(s):

Relative Motion ◽

Motion Information ◽

Indoor And Outdoor ◽

Outdoor Navigation

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Visual Tracking of an Object with its Motion Information

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.129.977 ◽

2009 ◽

Vol 129 (5) ◽

pp. 977-984

Author(s):

Atsutoshi Shimeno ◽

Seiichi Uchida ◽

Ryo Kurazume ◽

Rin-ichiro Taniguchi ◽

Tsutomu Hasegawa

Keyword(s):

Visual Tracking ◽

Motion Information

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A Driver’s Visual Attention Prediction Using Optical Flow

Sensors ◽

10.3390/s21113722 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3722

Author(s):

Byeongkeun Kang ◽

Yeejin Lee

Keyword(s):

Visual Attention ◽

Optical Flow ◽

Prediction Models ◽

Apparent Movement ◽

Image Sequences ◽

Motion Information ◽

Accuracy Improvement ◽

Flow Maps ◽

Current State ◽

Motion Features

Motion in videos refers to the pattern of the apparent movement of objects, surfaces, and edges over image sequences caused by the relative movement between a camera and a scene. Motion, as well as scene appearance, are essential features to estimate a driver’s visual attention allocation in computer vision. However, the fact that motion can be a crucial factor in a driver’s attention estimation has not been thoroughly studied in the literature, although driver’s attention prediction models focusing on scene appearance have been well studied. Therefore, in this work, we investigate the usefulness of motion information in estimating a driver’s visual attention. To analyze the effectiveness of motion information, we develop a deep neural network framework that provides attention locations and attention levels using optical flow maps, which represent the movements of contents in videos. We validate the performance of the proposed motion-based prediction model by comparing it to the performance of the current state-of-art prediction models using RGB frames. The experimental results for a real-world dataset confirm our hypothesis that motion plays a role in prediction accuracy improvement, and there is a margin for accuracy improvement by using motion features.

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Rock Particle Motion Information Detection Based on Video Instance Segmentation

Sensors ◽

10.3390/s21124108 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4108

Author(s):

Man Chen ◽

Maojun Li ◽

Yiwei Li ◽

Wukun Yi

Keyword(s):

Loss Function ◽

Particle Motion ◽

Numerical Models ◽

Major Axis ◽

Angular Distance ◽

Motion Information ◽

Vibration Load ◽

Rock Particle ◽

Information Detection ◽

Instance Segmentation

The detection of rock particle motion information is the basis for revealing particle motion laws and quantitative analysis. Such a task is crucial in guiding engineering construction, preventing geological disasters, and verifying numerical models of particles. We propose a machine vision method based on video instance segmentation (VIS) to address the motion information detection problem in rock particles under a vibration load. First, we designed a classification loss function based on Arcface loss to improve the Mask R-CNN. This loss function introduces an angular distance based on SoftMax loss that distinguishes the objects and backgrounds with higher similarity. Second, this method combines the abovementioned Mask R-CNN and Deep Simple Online and Real-time Tracking (Deep SORT) to perform rock particle detection, segmentation, and tracking. Third, we utilized the equivalent ellipse characterization method for segmented particles, integrating with the proportional calibration algorithm to test the translation and detecting the rotation by calculating the change in the angle of the ellipse’s major axis. The experimental results show that the improved Mask R-CNN obtains an accuracy of 93.36% on a self-created dataset and also has some advantages on public datasets. Combining the improved Mask R-CNN and Deep SORT could fulfill the VIS with a low ID switching rate while successfully detecting movement information. The average detection errors of translation and rotation are 5.10% and 14.49%, respectively. This study provides an intelligent scheme for detecting movement information of rock particles.

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Rotation Estimation: A Closed-Form Solution Using Spherical Moments

Sensors ◽

10.3390/s19224958 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4958

Author(s):

Hicham Hadj-Abdelkader ◽

Omar Tahri ◽

Houssem-Eddine Benseddik

Keyword(s):

Image Processing ◽

Closed Form ◽

Synthetic Data ◽

Closed Form Solution ◽

Form Solution ◽

Unified Model ◽

Experimental Results ◽

Motion Information ◽

Vision Sensors ◽

Spherical Projection

Photometric moments are global descriptors of an image that can be used to recover motion information. This paper uses spherical photometric moments for a closed form estimation of 3D rotations from images. Since the used descriptors are global and not of the geometrical kind, they allow to avoid image processing as features extraction, matching, and tracking. The proposed scheme based on spherical projection can be used for the different vision sensors obeying the central unified model: conventional, fisheye, and catadioptric. Experimental results using both synthetic data and real images in different scenarios are provided to show the efficiency of the proposed method.

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