Robustness of Point Feature Detection

2013 ◽  
pp. 91-99 ◽  
Author(s):  
Zijiang Song ◽  
Reinhard Klette
Keyword(s):  
Feature Detection ◽  
Point Feature

A shadow-removal based saliency map for point feature detection of underwater objects

2015 ◽  
Author(s):  
Liqin Fu ◽  
Yiru Wang ◽  
Zhebin Zhang ◽  
Rui Nian ◽  
Tianhong Yan ◽  
...  
Keyword(s):  
Feature Detection ◽  
Saliency Map ◽  
Shadow Removal ◽  
Point Feature

Fully automatic hybrid registration method based on point feature detection without user intervention

2006 ◽  
Author(s):  
Bang-Bon Koo ◽  
Jong-Min Lee ◽  
June-Sic Kim ◽  
In-Young Kim ◽  
Jun-Soo Kwon ◽  
...  
Keyword(s):  
Feature Detection ◽  
Registration Method ◽  
Fully Automatic ◽  
User Intervention ◽  
Hybrid Registration ◽  
Point Feature

A deep neural network-based vehicle re-identification method for bridge load monitoring

2021 ◽  
pp. 136943322110339
Author(s):  
Yufeng Zhang ◽  
Junxin Xie ◽  
Jiayi Peng ◽  
Hui Li ◽  
Yong Huang
Keyword(s):  
Neural Network ◽  
Feature Detection ◽  
Image Data ◽  
Image Features ◽  
Superior Performance ◽  
Multiple Sources ◽  
Identification Method ◽  
Load Monitoring ◽  
Bridge Structures ◽  
Point Feature

The accurate tracking of vehicle loads is essential for the condition assessment of bridge structures. In recent years, a computer vision method that is based on multiple sources of data from monitoring cameras and weight-in-motion (WIM) systems has become a promising strategy in bridge vehicle load identification for structural health monitoring (SHM) and has attracted increasing attention. The implementation of vehicle re-identification, namely, the identification of the same vehicle from images that were captured at different locations or time instants, is the key topic of this study. In this study, a vehicle re-identification method that is based on HardNet, a deep convolutional neural network (CNN) specialized in picking up local image features, is proposed. First, we obtain the vehicle point feature positions in the image through feature detection. Then, the HardNet is employed to encode the point feature image patches into deep learning feature descriptors. Re-identification of the target vehicle is achieved by matching the encoded descriptors between two images, which are robust toward scaling, rotation, and other types of noises. A comparison study of the proposed method with three published vehicle re-identification methods is performed using vehicle image data from a real bridge, and the superior performance of our proposed method is demonstrated.

scholarly journals A generalised framework for saliency-based point feature detection

2017 ◽  
Vol 157 ◽  
pp. 117-137 ◽  
Author(s):  
Mark Brown ◽  
David Windridge ◽  
Jean-Yves Guillemaut
Keyword(s):  
Feature Detection ◽  
Point Feature

Feature Detection Requires Spatial Attention

2007 ◽  
Author(s):  
Jan Theeuwes ◽  
Erik van der Burg ◽  
Artem V. Belopolsky
Keyword(s):  
Spatial Attention ◽  
Feature Detection

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

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