A method for reducing of computational time on image registration employing wavelet transformation

2007 ◽  
Author(s):  
Yutaro Yamamura ◽  
Hyoungseop Kim ◽  
Joo kooi Tan ◽  
Seiji Ishikawa ◽  
Akiyoshi Yamamoto
Keyword(s):  
Image Registration ◽  
Wavelet Transformation ◽  
Computational Time

scholarly journals HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor

2021 ◽  
Vol 13 (12) ◽  
pp. 2328
Author(s):  
Yameng Hong ◽  
Chengcai Leng ◽  
Xinyue Zhang ◽  
Zhao Pei ◽  
Irene Cheng ◽  
...  
Keyword(s):  
Image Registration ◽  
Local Binary Pattern ◽  
Computational Time ◽  
Rotation Invariant ◽  
Edge Information ◽  
Important Research Topic ◽  
New Construction ◽  
Novel Method ◽  
Direction Information ◽  
Angle Calculation

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.

A Novel Medical Image Registration Based on Contour Features and MultiScale Information

2014 ◽  
Vol 513-517 ◽  
pp. 3020-3023
Author(s):  
Yun Feng Yang ◽  
Cheng Xin Lin ◽  
Peng Xiao Wang ◽  
Jia Li ◽  
Bo Li
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Clinical Medicine ◽  
Computational Time ◽  
Medical Image Registration ◽  
Canny Operator ◽  
Registration Method ◽  
Contour Lines ◽  
Principal Axes ◽  
Edge Features

Medical image registration is the important technique in the clinical medicine field. A novel hierarchical registration method of the medical images based on multiscale information and contour line is proposed in the paper. At First, contour lines of the couple images are extracted based on the edge features obtained by Canny operator, and contour lines of the couple images are resample in order to reduce the calculation cost in the registration process. Secondly, the Principal Axes method is used to accomplish the rough registration based on the resampled contour lines. Thirdly, multiscale image serials obtained by down-sample transform are used to accomplish the couple images fine registration. Experiment results show that the method not only can achieve more accurate registration results, but also can reduce the computational time greatly. The accurate registration results also can be achieved in the noisy environment.

scholarly journals Efficient Image Registration for Underwater Optical Mapping Using Geometric Invariants

2019 ◽  
Vol 7 (6) ◽  
pp. 178
Author(s):  
Armagan Elibol ◽  
Nak Young Chong
Keyword(s):  
Image Registration ◽  
Optical Mapping ◽  
Probabilistic Methods ◽  
Computational Time ◽  
Intermediate Step ◽  
Geometrical Properties ◽  
Geometric Invariants ◽  
Salient Points ◽  
Feature Based ◽  
Image Pairs

Image registration is one of the most fundamental and widely used tools in optical mapping applications. It is mostly achieved by extracting and matching salient points (features) described by vectors (feature descriptors) from images. While matching the descriptors, mismatches (outliers) do appear. Probabilistic methods are then applied to remove outliers and to find the transformation (motion) between images. These methods work in an iterative manner. In this paper, an efficient way of integrating geometric invariants into feature-based image registration is presented aiming at improving the performance of image registration in terms of both computational time and accuracy. To do so, geometrical properties that are invariant to coordinate transforms are studied. This would be beneficial to all methods that use image registration as an intermediate step. Experimental results are presented using both semi-synthetically generated data and real image pairs from underwater environments.

scholarly journals Structural Representation: Reducing Multi-Modal Image Registration to Mono-Modal Problem

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Keyvan Kasiri ◽  
David Clausi ◽  
Paul Fieguth
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
The Other ◽  
Optimization Process ◽  
Computational Time ◽  
Phase Congruency ◽  
Structural Representation ◽  
Brain Images ◽  
Modal Approach ◽  
Gradient Information

<p>Registration of multi-modal images has been a challenging task<br />due to the complex intensity relationship between images. The<br />standard multi-modal approach tends to use sophisticated similarity<br />measures, such as mutual information, to assess the accuracy<br />of the alignment. Employing such measures imply the increase in<br />the computational time and complexity, and makes it highly difficult<br />for the optimization process to converge. The presented registration<br />method works based on structural representations of images<br />captured from different modalities, in order to convert the multimodal<br />problem into a mono-modal one. Two different representation<br />methods are presented. One is based on a combination of<br />phase congruency and gradient information of the input images,<br />and the other utilizes a modified version of entropy images in a<br />patch-based manner. Sample results are illustrated based on experiments<br />performed on brain images from different modalities.</p>

Devanagari Handwritten Marathi Compound Character Recognition System

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Chitra Bhole
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Character Recognition ◽  
Wavelet Transformation ◽  
Recognition System ◽  
Computational Time ◽  
Deep Convolution Neural Network ◽  
Time Required

Handwritten character recognition a field of research in AI, computer vision, and pattern recognition. Devanagari handwritten Marathi compound character recognition is most tedious tasks because of its complexity as compared to other languages. As compound character is combination of two or more characters it becomes challenging task to recognize it. However, the researchers used various methods like Neural Network, SVM, KNN, Wavelet transformation to classify the features of compound Marathi characters and tried to give the accuracy in the recognition of it. But the problem of feature extraction, and time required is large. In this paper I am proposing the Offline handwritten Marathi compound character recognition using deep convolution neural network which reduces the computational time and increases the accuracy.

Data Fusion of Radar Image and ECDIS Based on Matching of Harris Feature Points

2011 ◽  
Vol 317-319 ◽  
pp. 2026-2029 ◽  
Author(s):  
Chuang Zhang ◽  
Zhong Zhou Fan
Keyword(s):  
Image Registration ◽  
Image Fusion ◽  
Wavelet Transformation ◽  
Transformation Method ◽  
Image Features ◽  
Image Feature ◽  
Feature Points ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Electronic Chart

This paper analyzes automatic image registration algorithm, and image features of radar and electronic chart, then, image registration algorithm based on Harris feature point are given, in which the methods are specially described. Wavelet transformation method on image feature should be considered. Moreover, simulate results of image fusion.

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