A low complexity image stitching using advanced feature matching in mobile environments

2014 ◽  
Vol 10 (2) ◽  
pp. 129-136 ◽  
Author(s):  
Hyochang Ahn ◽  
Yong-Hwan Lee ◽  
June-Hwan Lee ◽  
Han-Jin Cho
Keyword(s):  
Feature Matching ◽  
Low Complexity ◽  
Image Stitching ◽  
Mobile Environments

Method of Footprint Image Stitching Based on Multiscale SIFT Feature Matching

2013 ◽  
Vol 303-306 ◽  
pp. 1056-1059
Author(s):  
Sen Wang ◽  
Yin Hui Zhang ◽  
Zhong Hai Shi ◽  
Zi Fen He
Keyword(s):  
Wavelet Transform ◽  
Feature Matching ◽  
Wavelet Coefficient ◽  
Low Frequency ◽  
Original Method ◽  
Inverse Transformation ◽  
Image Stitching ◽  
Image Splicing ◽  
Sift Feature ◽  
Matching Pair

The image stitching method is widely used into the suspect's footprint information extraction. In order to improve the image detail and the matching precision, the Footprint map image stitching method which is based on the wavelet transform and the SIFT feature matching is put forward. The wavelet transform in this method is perform based on the pretreatment of image, move the low frequency wavelet coefficient to zero, adjusting thresholds of the high frequency wavelet coefficient and inverse transformation, then, use the SIFT to extract and match the key-points of the processed images. For the error matching pair of coarse match, you can use the RANSAC to filter them out. This article demonstrates its advantage through to the original image splicing comparisons. The experimental results show that the method display more clear detail and the precision of matching than the original method.

Detection method of PCB component based on automatic optical stitching algorithm

Circuit World ◽  
2015 ◽  
Vol 41 (4) ◽  
pp. 133-136 ◽  
Author(s):  
Ge Qiang ◽  
Zheng Shanshan ◽  
Zhao Yang ◽  
Chen Mao
Keyword(s):  
Solder Joint ◽  
High Speed ◽  
Feature Matching ◽  
Region Of Interest ◽  
Scale Space ◽  
Image Stitching ◽  
Small Data ◽  
Full Line ◽  
Content Type ◽  
Line Image

Purpose – This paper aims to propose image stitching by reduction of full line and taking line image as registration image to solve the problem of automatic optic inspection in PCB detection. In addition, surf registration was introduced for image stitching to improve the accuracy and speed of stitching. Design/methodology/approach – First, image stitching proceeded by method of full line reduction and taking line image as registration image; second, surf registration was introduced based on the traditional PCB image stitching algorithm. Scale space of the image pyramid was adopted for confirming relative future points between stitching image. The registration means of nearest neighbourhood and next neatest neighborhood was selected for feature matching and fused in region of interest to fulfil image stitching. Findings – The improved stitching algorithm with small data size of image, high speed and noncumulative transitive error eliminated displacement deviation and solved the stitching gap caused by uneven illumination, to greatly improve the accuracy and speed of stitching. Research limitations/implications – The research of this paper can only used for appearance detection and cannot be used for solder joint inspection with circuit detection or invisible solder joint detection; it can identify and mark PCB component defects but cannot classify automatically, thus artificial confirmation and processing is needed. Originality/value – Based on the traditional image stitching means, this paper proposed full line reduction for image stitching, which reduces processing of data and speeds up image stitching; in addition, surf registration was introduced into the study of PCB stitching algorithm, which greatly improves the accuracy and speed of stitching and solves stitching gap formed by opposite variation trend of image local edge caused by uneven illumination.

Low-Complexity Voice Detector for Mobile Environments

2009 ◽  
Author(s):  
Michal Ries ◽  
Bruno Gardlo ◽  
Markus Rupp ◽  
Phillip De Leon
Keyword(s):  
Low Complexity ◽  
Mobile Environments

A Low-Complexity and Hardware-Oriented Image Stitching Algorithm

2020 ◽  
Author(s):  
Fu-Jung Wen ◽  
Jr-Yu Lin ◽  
Yih-Shyh Chiou ◽  
Ting-Lan Lin ◽  
Chiung-An Chen ◽  
...  
Keyword(s):  
Low Complexity ◽  
Image Stitching

scholarly journals Panoramic Image Stitching: A Survey

2020 ◽  
Vol 6 (12) ◽  
pp. 93-97
Author(s):  
Vanshul Bhasker
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
Current Status ◽  
Image Stitching ◽  
Panoramic Image ◽  
Electronic Document ◽  
Image Processing Techniques ◽  
Image Blending ◽  
Homography Estimation ◽  
Processing Techniques

This electronic document is a report on Image Stitching. Image stitching is the process of creating an image panorama from a given set of images that have some common(overlapping) area in them. Previous researches done on this topic show that there is still a lot of scope for improvement in this field as although we are able to achieve good results but we haven’t really been able to achieve perfection. There are a lot of factors that are to be blamed here. While Stitching Images, there could be many challenges such as images being corrupt by noise and/or presence of parallax in the images. Image Stitching process is divided into 5 major steps: Image Registration, Feature Detection, Feature Matching, Homography Estimation and Image Blending. In this document we are going to discuss the current status of image processing techniques and what are the challenges being faced.

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