Image Feature Correspondence Selection: A Comparative Study and a New Contribution

2020 ◽  
Vol 29 ◽  
pp. 3506-3519
Author(s):  
Chen Zhao ◽  
Zhiguo Cao ◽  
Jiaqi Yang ◽  
Ke Xian ◽  
Xin Li
Keyword(s):  
Comparative Study ◽  
Image Feature ◽  
Feature Correspondence

A comparative study of image feature detection and matching algorithms for touchless fingerprint systems.

2016 ◽  
Vol 2016 (15) ◽  
pp. 1-9 ◽  
Author(s):  
Sos S. Agaian ◽  
Marzena (Mary Ann) Mulawka ◽  
Rahul Rajendran ◽  
Shishir P Rao ◽  
Shreyas Kamath K.M ◽  
...  
Keyword(s):  
Comparative Study ◽  
Feature Detection ◽  
Image Feature

FPGA-Based Stereo Vision System Using Gradient Feature Correspondence

2015 ◽  
Vol 27 (6) ◽  
pp. 681-690 ◽  
Author(s):  
Hayato Hagiwara ◽  
◽  
Yasufumi Touma ◽  
Kenichi Asami ◽  
Mochimitsu Komori
Keyword(s):  
Mobile Robot ◽  
Stereo Vision ◽  
Vision System ◽  
Image Feature ◽  
Interest Point ◽  
Feature Correspondence ◽  
Speeded Up Robust Features ◽  
Stereo Vision System ◽  
Simpler Algorithm ◽  
Gradient Feature

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/10.jpg"" width=""300"" /> Mobile robot with a stereo vision</div>This paper describes an autonomous mobile robot stereo vision system that uses gradient feature correspondence and local image feature computation on a field programmable gate array (FPGA). Among several studies on interest point detectors and descriptors for having a mobile robot navigate are the Harris operator and scale-invariant feature transform (SIFT). Most of these require heavy computation, however, and using them may burden some computers. Our purpose here is to present an interest point detector and a descriptor suitable for FPGA implementation. Results show that a detector using gradient variance inspection performs faster than SIFT or speeded-up robust features (SURF), and is more robust against illumination changes than any other method compared in this study. A descriptor with a hierarchical gradient structure has a simpler algorithm than SIFT and SURF descriptors, and the result of stereo matching achieves better performance than SIFT or SURF.

Direct Interpretation of Dynamic Images and Camera Motion for Visual Servoing Without Image Feature Correspondence

1997 ◽  
Vol 9 (2) ◽  
pp. 104-110 ◽  
Author(s):  
Koichiro Deguchi ◽  
Keyword(s):  
Visual Servoing ◽  
General Scheme ◽  
Principal Component ◽  
Tangent Plane ◽  
Image Feature ◽  
Interaction Matrix ◽  
Normal Vector ◽  
Camera Motion ◽  
Robot Arm ◽  
Feature Correspondence

A general scheme to represent the relation between dynamic images and camera motion is presented. Then its application to visual servoing is proposed. For a specific object, every possible combination of the camera pose and the obtained image should be constrained on a lower dimensional hyper surface in the product space of the whole combination of image data and camera position. Visual servoing, for example, is interpreted as finding a path on this surface leading to a given image. Our approach is to analyze the properties of this surface, and use its differential or tangential property for visual servoing. The coefficient matrix of the tangent plane of this surface is related to the so-called Interaction Matrix. For this approach, the reduction of the dimension of the image information becomes a key problem. We propose to use the principal component analysis and to represent images with a composition of small number of ""eigenimages"" by using Karhune Loève (K-L) expansion. A normal vector We confirm the feasibility of our basic idea for visual servoing with some experiments using a real robot arm.

Author response for "Laminar distribution of cortical projection neurons to the pulvinar: A comparative study in cats and mice"

2020 ◽  
Author(s):  
Bruno Oliveira Ferreira de Souza ◽  
Éve‐Marie Frigon ◽  
Robert Tremblay‐Laliberté ◽  
Christian Casanova ◽  
Denis Boire
Keyword(s):  
Comparative Study ◽  
Author Response ◽  
Projection Neurons ◽  
Cortical Projection ◽  
Laminar Distribution
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