In this paper, a novel algorithm which integrates the RGB color histogram and texture features for content based image retrieval. A new set of two-dimensional (2-D) M-band dual tree complex wavelet transform (M_band_DT_CWT) and rotated M_band_DT_CWT are designed to improve the texture retrieval performance. Unlike the standard dual tree complex wavelet transform (DT_CWT), which gives a logarithmic frequency resolution, the M-band decomposition gives a mixture of a logarithmic and linear frequency resolution. Most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we propose a novel approach for image retrieval using M_band_DT_CWT and rotated M_band_DT_CWT (M_band_DT_RCWT) by computing the energy, standard deviation and their combination on each subband of the decomposed image. To check the retrieval performance, two texture databases are used. Further, it is mentioned that the databases used are Brodatz gray scale database and MIT VisTex Color database. The retrieval efficiency and accuracy using proposed features is found to be superior to other existing methods.

This paper presents a hierarchical clustering algorithm for crop type classification problem using multi-spectral satellite image. In unsupervised techniques, the automatic generation of clusters and its centers is not exploited to their full potential. Hence, a hierarchical clustering algorithm is proposed which uses splitting and merging techniques. Initially, the splitting method is used to search for the best possible number of clusters and its centers using non-parametric technique i.e., clonal selection method. Using these clusters, a merging method is used to group the data points based on a parametric method (K-means algorithm). The performance of the proposed hierarchical clustering algorithm is compared with two unsupervised algorithms (K-means and Self-Organizing Map) that are available in the literature. A performance comparison of the proposed algorithm with the conventional algorithms is presented. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is more accurate.

The Block-Matching (BM) method for motion estimation in most video coding is largely discussed in the case of perspective images. The omnidirectional cameras provide images with large field of view. These images contain global information about motion and permit to remove the ambiguity present with little camera motion in perspective case. Nevertheless, these images contain significant radial distortions. The Block-Matching in these catadioptric images is not a resolved problem, and still a challenging research field. A rectangular block representing the neighborhood in BM of a point and used in the perspective images is not appropriate for catadioptric cameras. The work presented in this article concerns the local motion estimation in catadioptric videos with the Adapted Block-Matching (ABM). The ABM based on an adapted neighborhood, the local motion estimation allows successful compensation prediction in catadioptric images. The Adapted Block-Matching is obtained from the equivalence between the omnidirectional image and the projection of scene points on a unit sphere.

Image resolution enhancement in wavelet domain has been one of the most active research areas in image processing. Many methods and techniques, based on wavelet transformation have been proposed in last couple of years. In this paper, we present a review on the state-of-the-art techniques for wavelet based image resolution enhancement. We summarize them with enhancement ability in peak signal to noise ratio (PSNR) and give comments on their performance. In addition, through our review, we have found some essential criteria and issues related to performance assessment of different resolution enhancement techniques. Our experimental results have proved the significance of these issues. Future directions for image resolution enhancement research are stated at the end.

The objective of image restoration approach is to recover a true image from a degraded version. This problem can be stated as blind or non-blind depending upon whether blur parameters are known prior to the restoration process. Blind restoration deals with parameter identification before deconvolution. Though there exists multiple blind restorations techniques but blur type recognition is extremely desirable before application of any blur parameters estimation approach. In this paper, we develop a blur classification approach that deploys a feed forward neural network to categories motion, defocus and combined blur types. The features deployed for designing of classification system include mean and standard deviation of ridgelet energies. Our simulation results show the preciseness of proposed method.

Pupil detection and iris localisation using scanning method and feature extraction is performed with five level decomposition techniques, with these two proposed algorithm we could achieve efficient and fast person authentication in biometric security systems. Statistical performance evaluation is also performed using parameters False acceptance rate (FAR), False rejection rate (FRR), Correct recognition rate (CRR), Equal error rate (EER), Match ratio etc, using CASIA database.

A reliability evaluation system for the recognition of Devanagri Numerals is proposed in this paper. Reliability of classification is very important in applications of optical character recognition. As we know that the outliers and ambiguity may affect the performance of recognition system, a rejection measure must be there for the reliable recognition of the pattern. For each character image pre-processing steps like normalization, binarization, noise removal and boundary extraction is performed. After calculating the bounding box features are extracted for each partition of the numeral image. Features are calculated on three different zoning methods. Directional feature is considered which is obtained using chain code and gradient direction quantization of the orientations. The Zoning firstly, is considered made up of uniform partitions and secondly of non-uniform compartments based on the density of the pixels. For classification 1-nearest neighbor based classifier, quadratic bayes classifier and linear bayes classifier are chosen as base classifier. The base classifiers are combined using four decision combination rules namely maximum, Median, Average and Majority Voting. The framework is used to test the reliability of recognition system against ambiguity.