Image enhancement for point feature detection in built environment

2014 ◽  
Author(s):  
Ville V. Lehtola ◽  
Petri Ronnholm
Keyword(s):  
Built Environment ◽  
Image Enhancement ◽  
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

scholarly journals Rotation Covariant Image Processing for Biomedical Applications

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Henrik Skibbe ◽  
Marco Reisert
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Biomedical Applications ◽  
Feature Detection ◽  
Mathematical Framework ◽  
3D Image ◽  
Image Description ◽  
Volumetric Images ◽  
Object Feature ◽  
High Level

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences.

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

scholarly journals THE EFFECT OF IMAGE ENHANCEMENT METHODS DURING FEATURE DETECTION AND MATCHING OF THERMAL IMAGES

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 575-578 ◽  
Author(s):  
O. Akcay ◽  
E. O. Avsar
Keyword(s):  
Image Enhancement ◽  
Digital Image ◽  
Image Matching ◽  
Feature Detection ◽  
Optical Resolution ◽  
Extraction Methods ◽  
Image Process ◽  
Low Resolution ◽  
Thermal Images ◽  
Speeded Up Robust Features

A successful image matching is essential to provide an automatic photogrammetric process accurately. Feature detection, extraction and matching algorithms have performed on the high resolution images perfectly. However, images of cameras, which are equipped with low-resolution thermal sensors are problematic with the current algorithms. In this paper, some digital image processing techniques were applied to the low-resolution images taken with Optris PI 450 382 x 288 pixel optical resolution lightweight thermal camera to increase extraction and matching performance. Image enhancement methods that adjust low quality digital thermal images, were used to produce more suitable images for detection and extraction. Three main digital image process techniques: histogram equalization, high pass and low pass filters were considered to increase the signal-to-noise ratio, sharpen image, remove noise, respectively. Later on, the pre-processed images were evaluated using current image detection and feature extraction methods Maximally Stable Extremal Regions (MSER) and Speeded Up Robust Features (SURF) algorithms. Obtained results showed that some enhancement methods increased number of extracted features and decreased blunder errors during image matching. Consequently, the effects of different pre-process techniques were compared in the paper.

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