New Insights on Finger Knuckle Print Identification with Steerable Filter for Faster and Accurate Matching

2019 ◽  
Author(s):  
A. Kavipriya ◽  
A. Muthukumar
Keyword(s):  
Accurate Matching ◽  
Steerable Filter

Three-dimensional Reconstruction of Microscopic Image Based on Multi-ST Algorithm

2020 ◽  
Vol 64 (2) ◽  
pp. 20506-1-20506-7
Author(s):  
Min Zhu ◽  
Rongfu Zhang ◽  
Pei Ma ◽  
Xuedian Zhang ◽  
Qi Guo
Keyword(s):  
3D Reconstruction ◽  
Three Dimensional ◽  
Scale Model ◽  
Microscopic Image ◽  
Multi Scale ◽  
Gaussian Pyramid ◽  
Cost Aggregation ◽  
Dimensional Reconstruction ◽  
Image Pairs ◽  
Accurate Matching

Abstract Three-dimensional (3D) reconstruction is extensively used in microscopic applications. Reducing excessive error points and achieving accurate matching of weak texture regions have been the classical challenges for 3D microscopic vision. A Multi-ST algorithm was proposed to improve matching accuracy. The process is performed in two main stages: scaled microscopic images and regularized cost aggregation. First, microscopic image pairs with different scales were extracted according to the Gaussian pyramid criterion. Second, a novel cost aggregation approach based on the regularized multi-scale model was implemented into all scales to obtain the final cost. To evaluate the performances of the proposed Multi-ST algorithm and compare different algorithms, seven groups of images from the Middlebury dataset and four groups of experimental images obtained by a binocular microscopic system were analyzed. Disparity maps and reconstruction maps generated by the proposed approach contained more information and fewer outliers or artifacts. Furthermore, 3D reconstruction of the plug gauges using the Multi-ST algorithm showed that the error was less than 0.025 mm.

Accurate matching method of multimodal image based on phase congruency and local mutual information

2014 ◽  
Author(s):  
Chunyang Zhao ◽  
Huaici Zhao ◽  
Gang Zhao
Keyword(s):  
Mutual Information ◽  
Phase Congruency ◽  
Matching Method ◽  
Accurate Matching

Identification of Cell-Cycle Phases Using Neural Network and Steerable Filter Features

2006 ◽  
pp. 702-709
Author(s):  
Xiaodong Yang ◽  
Houqiang Li ◽  
Xiaobo Zhou ◽  
Stephen T. C. Wong
Keyword(s):  
Neural Network ◽  
Cell Cycle ◽  
Cell Cycle Phases ◽  
Steerable Filter

Blockchain-Based Ride-Sharing System with Accurate Matching and Privacy-Preservation

2021 ◽  
Author(s):  
Mahmoud M. Badr ◽  
Mohamed Baza ◽  
Sherif Abdelfattah ◽  
Mohamed Mahmoud ◽  
Waleed Alasmary
Keyword(s):  
Privacy Preservation ◽  
Ride Sharing ◽  
Accurate Matching

A New 3D Orientation Steerable Filter

2000 ◽  
pp. 203-212 ◽  
Author(s):  
Weichuan Yu ◽  
Kostas Daniilidis ◽  
Gerald Sommer
Keyword(s):  
Steerable Filter

An Accurate Full Car Ride Model Using Model Reducing Techniques

2002 ◽  
Vol 124 (4) ◽  
pp. 697-705 ◽  
Author(s):  
Chul Kim ◽  
Paul I. Ro
Keyword(s):  
Model Complexity ◽  
Ride Quality ◽  
Singular Perturbation Method ◽  
Suspension Systems ◽  
Proposed Model ◽  
Model Size ◽  
Improved Performance ◽  
Accurate Matching ◽  
Multibody Dynamics Model ◽  
Selection Of

In this study, an approach to obtain an accurate yet simple model for full-vehicle ride analysis is proposed. The approach involves linearization of a full car MBD (multibody dynamics) model to obtain a large-order vehicle model. The states of the model are divided into two groups depending on their effects on the ride quality and handling performance. Singular perturbation method is then applied to reduce the model size. Comparing the responses of the proposed model and the original MBD model shows an accurate matching between the two systems. A set of identified parameters that makes the well-known seven degree-of-freedom model very close to the full car MBD model is obtained. Finally, the benefits of the approach are illustrated through design of an active suspension system. The identified model exhibits improved performance over the nominal models in the sense that the accurate model leads to the appropriate selection of control gains. This study also provides an analytical method to investigate the effects of model complexity on model accuracy for vehicle suspension systems.

Spatial Cueing and the Relation between the Accuracy of “Where” and “What” Decisions in Visual Search

1989 ◽  
Vol 41 (4) ◽  
pp. 747-773 ◽  
Author(s):  
Hermann J. Müller ◽  
Patrick M. A. Rabbitt
Keyword(s):  
Visual Search ◽  
Shape Analysis ◽  
Target Localization ◽  
Target Orientation ◽  
Spatial Cueing ◽  
Localization Accuracy ◽  
Accurate Localization ◽  
Orientation Experiment ◽  
Candidate Location ◽  
Accurate Matching

To study the processes underlying selective attention in visual search, the relation between the accuracy of “where” (location) and “what” (same/different orientation matching) decisions was analysed under various display conditions. Target-non-target discriminability was varied by contrasting single and multiple element displays; further, attention was directly manipulated by spatial cueing. In Experiment 1, analyses for both single and multiple displays showed that localization accuracy remained above chance when same/different matching failed; the inverse also obtained. It seems that accurate matching is not a prerequisite for target localization, nor is accurate localization a prerequisite for same/different matching. However, localization is a prerequisite for the accurate recognition of target orientation (Experiment 2). In this case, it seems that features critical for localization “call” attention to a particular candidate location. This facilitates further (shape) analysis of the stimulus that is found there. This orienting process is by-passed if attention is cued to the location in advance.

Highly Accurate Matching of Weakly Localized Features

2019 ◽  
Vol 12 (4) ◽  
pp. 1833-1863
Author(s):  
Erez Farhan
Keyword(s):  
Accurate Matching

U-V-Disparity based Obstacle Detection with 3D Camera and steerable filter

2011 ◽  
Author(s):  
Y. Gao ◽  
X. Ai ◽  
Y. Wang ◽  
J. Rarity ◽  
N. Dahnoun
Keyword(s):  
Obstacle Detection ◽  
3D Camera ◽  
Steerable Filter
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