Matching method of partial shoeprint images based on PCA-SIFT algorithm
نویسنده
چکیده
To improve the accuracy of image matching shoeprint image feature matching method based on PCA-SIFT is proposed. Firstly, feature detection and pre-matching of images are done by using PCA-SIFT (principal component analysisscale invariant feature transform) algorithm. And then, the correlation coefficient is used as similarity measurement, which can filter image interest points. By this method, the image matching pairs can be obtained. Finally, the RANSAC (random sample consensus) algorithm is used to eliminate the mismatching pairs. The simulation results demonstrate that the proposed algorithm is more robust while maintaining good registration accuracy when analyzing partial shoeprint images in the presence of geometric distortions such as scale and rotation distortions compared with conventional algorithms. Keywords— PCA-SIFT, shoeprint image, image matching, RANSAC.
منابع مشابه
Local image Features for Shoeprint Image Retrieval
This paper deals with the retrieval of scene-of-crime (or scene) shoeprint images from a reference database of shoeprint images by using a new local feature detector and an improved local feature descriptor. Our approach is based on novel modifications and improvements of a few recent techniques in this area: (1) the scale adapted Harris detector, which is an extension to multi-scale domains of...
متن کاملAdaptive Principle Component Analysis to Improve Scale Invariant Feature Transform Matching for Face Recognition Applications
Image matching using feature extraction is an important issue in computer vision tasks. The main drawback of matching process is the bottleneck problem that rapidly appeared when the number of features increased. This paper produced an adaptive approach to improve Scale Invariant Feature Transform (SIFT) matching. The main idea is to increase the number of SIFT points by using Adaptive PCA in w...
متن کاملPerformance Evaluation of Local Detectors in the Presence of Noise for Multi-Sensor Remote Sensing Image Matching
Automatic, efficient, accurate, and stable image matching is one of the most critical issues in remote sensing, photogrammetry, and machine vision. In recent decades, various algorithms have been proposed based on the feature-based framework, which concentrates on detecting and describing local features. Understanding the characteristics of different matching algorithms in various applications ...
متن کاملAccurate Fruits Fault Detection in Agricultural Goods using an Efficient Algorithm
The main purpose of this paper was to introduce an efficient algorithm for fault identification in fruits images. First, input image was de-noised using the combination of Block Matching and 3D filtering (BM3D) and Principle Component Analysis (PCA) model. Afterward, in order to reduce the size of images and increase the execution speed, refined Discrete Cosine Transform (DCT) algorithm was uti...
متن کاملA Comparative Study of Sift and PCA for Content Based Image Retrieval
This paper presents a comparative approach for Content Based Image Retrieval (CBIR) using Scale Invariant Feature Transform (SIFT) algorithm and Principal Component Analysis (PCA) for color images. The motivation to use SIFT algorithm for CBIR is due to the fact that SIFT is invariant to scale, rotation and translation as well as partially invariant to affine distortion and illumination changes...
متن کامل