Computer vision applications face various challenges while detection and classification of objects in real world like large variation in appearances, cluttered back ground, noise, occlusion, low illumination etc.. In this paper a Wavelet based Histogram of Oriented Gradients (WHOG) feature descriptors are proposed to represent shape information by storing local gradients in image. This results in enhanced representation of shape information. The performance of the feature descriptors are tested on multiclass image data set having partial occlusion, different scales and rotated object images. The performance of WHOG feature based object classification is compared with HOG feature based classification. The matching of test image with its learned class is performed using Back Propagation Neural Network (BPNN) algorithm. Proposed features not only performed superior than HOG but also beat wavelet, moment invariant and Curvelet.

Blur is an undesirable phenomenon which appears as one of the most frequent causes of image degradation. Automatic blur detection is extremely enviable to restore barcode image or simply utilize them. That is to assess whether a given image is blurred or not. To detect blur, many algorithms have been proposed. These algorithms are different in their performance, time complexity, precision, and robustness in noisy environments. In this paper, we present an efficient method blur detection in barcode images, with no reference perceptual blur metric using wavelets.

An important constituents for image classification is the identification of significant characterstics about the specific class to distinguish intra class variations. Since performance of the classifiers is affected in the presence of noise, so selection of discriminative features is an important phase in classification. This superfluous information i.e. noise, e.g. additive noise may occur in images due to image sensors i.e. of the constant noise level in dark areas of the image or salt & pepper noise may be caused by analog to digitals conversion and bit error transmission etc.. Detection of noise is also very essential in the images for choosing appropriate filter. This paper presents an experimental assessment of neural classifier in terms of classification accuracy under three different constraints of images without noise, in presence of unknown noise and after elimination of noise.

Irish recognition has always been an attractive goal for researchers. The identification of the person based on iris recognition is very popular due to the uniqueness of the pattern of iris. Although a number of methods for iris recognition have been proposed by many researchers in the last few years. This paper proposes statistical texture feature based iris matching method for recognition using K-NN classifier. Statistical texture measures such as mean, standard deviation, entropy, skewness etc., and six features are computed of normalized iris image. K-NN classifier matches the input iris with the trained iris images by calculating the Euclidean distance between two irises. The performance of the system is evaluated on 500 iris images, which gives good classification accuracy with reduced FAR/FRR.

This paper compares the performance of various classifiers for multi class image classification. Where the features are extracted by the proposed algorithm in using Haar wavelet coefficient. The wavelet features are extracted from original texture images and corresponding complementary images. As it is really very difficult to decide which classifier would show better performance for multi class image classification. Hence, this work is an analytical study of performance of various classifiers for the single multiclass classification problem. In this work fifteen textures are taken for classification using Feed Forward Neural Network, Naïve Bays Classifier, K-nearest neighbor Classifier and Cascaded Neural Network.