A New Approach to Sparse Image Representation Using MMV and K-SVD

This paper addresses the problem of image representation based on a sparse decomposition over a learned dictionary. We propose an improved matching pursuit algorithm for Multiple Measurement Vectors (MMV) and an adaptive algorithm for dictionary learning based on multi-Singular Value Decomposition (SVD), and combine them for image representation. Compared with the traditional K-SVD and orthogonal matching pursuit MMV (OMPMMV) methods, the proposed method runs faster and achieves a higher overall reconstruction accuracy

Structural graph matching using the EM algorithm and singular value decomposition

This paper describes an efficient algorithm for inexact graph matching. The method is purely structural, that is, it uses only the edge or connectivity structure of the graph and does not draw on node or edge attributes. We make two contributions: 1) commencing from a probability distribution for matching errors, we show how the problem of graph matching can be posed as maximum-likelihood estimation using the apparatus of the EM algorithm; and 2) we cast the recovery of correspondence matches between the graph nodes in a matrix framework. This allows one to efficiently recover correspondence matches using the singular value decomposition. We experiment with the method on both real-world and synthetic data. Here, we demonstrate that the method offers comparable performance to more computationally demanding method

Face Recognition Technique Using Gabor Wavelets And Singular Value Decomposition

Gabor Wavelets (GWs) (also known as Gabor filter) and Singular Value Decomposition (SVD) have been studied extensively in the area of face recognition. In this project, face recognition system is developed using combination of GWs and SVD. Both techniques are used to extract facial features from the human facial image and presented in the form of feature vector. For GWs, only 12 out of 40 GWs are selected to extract facial features from the facial images. This offers the advantage of reducing computational time of feature extraction. As for SVD, only the first five singular values are selected and its associated right singular vectors are used as the facial feature vectors. The use of SVD in addition to the GWs increases the reliability of the face recognition system. In the face verification and matching stage, the similarity level between facial images is determined by computing the distance between the resulting facial feature vectors obtained from GWs and SVD respectively. Overall, the Gabor-SVD based face recognition technique showed constructive and promising result in recognizing the valid user and rejecting invalid users on the JAFFE database

Geometric potential of cartosat-1 stereo imagery

Cartosat-1 satellite, launched by Department of Space (DOS), Government of India, is dedicated to stereo viewing for large scale mapping and terrain modelling applications. This stereo capability fills the limited capacity of very high resolution satellites for three-dimensional point determination and enables the generation of detailed digital elevation models (DEMs) not having gaps in mountainous regions like for example the SRTM height model.The Cartosat-1 sensor offers a resolution of 2.5m GSD in panchromatic mode. One CCD-line sensor camera is looking with a nadir angle of 26' in forward direction, the other 5' aft along the track. The Institute "Area di Geodesia e Geomatica"-Sapienza Università di Roma and the Institute of Photogrammetry and Geoinformation, Leibniz University Hannover participated at the ISPRS-ISRO Cartosat-1 Scientific Assessment Programme (CSAP), in order to investigate the generation of Digital Surface Models (DSMs) from Cartosat-1 stereo scenes. The aim of this work concerns the orientation of Cartosat-1 stereo pairs, using the given RPCs improved by control points and the definition of an innovative model based on geometric reconstruction, that is used also for the RPC extraction utilizing a terrain independent approach. These models are implemented in the scientific software (SISAR-Software per Immagini Satellitari ad Alta Risoluzione) developed at Sapienza Università di Roma. In this paper the SISAR model is applied to different stereo pairs (Castelgandolfo and Rome) and to point out the effectiveness of the new model, SISAR results are compared with the corresponding ones obtained by the software OrthoEngine 10.0 (PCI Geomatica).By the University of Hannover a similar general satellite orientation program has been developed and the good results, achieved by bias corrected sensor oriented RPCs, for the test fields Mausanne (France) and Warsaw (Poland) have been described.For some images, digital height models have been generated by automatic image matching with least squares method, analysed in relation to given reference height models. For the comparison with the reference DEMs the horizontal fit of the height models to each other has been checked by adjustment