Texture representation using wavelet filterbanks

Texture analysis is a fundamental issue in image analysis and computer vision. While considerable research has been carried out in the texture analysis domain, problems relating to texture representation have been addressed only partially and active research is continuing. The vast majority of algorithms for texture analysis make either an explicit or implicit assumption that all images are captured under the same measurement conditions, such as orientation and illumination. These assumptions are often unrealistic in many practical applications;This dissertation addresses the viewpoint-invariance problem in texture classification by introducing a rotated wavelet filterbank. The proposed filterbank, in conjunction with a standard wavelet filterbank, provides better freedom of orientation tuning for texture analysis. This allows one to obtain texture features that are invariant with respect to texture rotation and linear grayscale transformation. In this study, energy estimates of channel outputs that are commonly used as texture features in texture classification are transformed into a set of viewpoint-invariant features. Texture properties that have a physical connection with human perception are taken into account in the transformation of the energy estimates;Experiments using natural texture image sets that have been used for evaluating other successful approaches were conducted in order to facilitate comparison. We observe that the proposed feature set outperformed methods proposed by others in the past. A channel selection method is also proposed to minimize the computational complexity and improve performance in a texture segmentation algorithm. Results demonstrating the validity of the approach are presented using experimental ultrasound tendon images

A Review of Wavelet Based Fingerprint Image Retrieval

A digital image is composed of pixels and information about brightness of image and RGB triples are used to encode color information. Image retrieval problem encountered when searching and retrieving images that is relevant to a user’s request from a database. In Content based image retrieval, input goes in the form of an image. In these images, different features are extracted and then the other images from database are retrieved accordingly. Biometric distinguishes the people by their physical or behavioral qualities. Fingerprints are viewed as a standout amongst the most solid for human distinguishment because of their uniqueness and ingenuity. To retrieve fingerprint images on the basis of their textural features,by using different wavelets. From the input fingerprint image, first of all center point area is selected and then its textural features are extracted and stored in database. When a query image comes then again its center point is selected and then its texture feature are extracted. Then these features are matched for similarity and then resultant image is displayed. DOI: 10.17762/ijritcc2321-8169.15026

Features for Cross Spectral Image Matching: A Survey

In recent years, cross spectral matching has been gaining attention in various biometric systems for identification and verification purposes. Cross spectral matching allows images taken under different electromagnetic spectrums to match each other. In cross spectral matching, one of the keys for successful matching is determined by the features used for representing an image. Therefore, the feature extraction step becomes an essential task. Researchers have improved matching accuracy by developing robust features. This paper presents most commonly selected features used in cross spectral matching. This survey covers basic concepts of cross spectral matching, visual and thermal features extraction, and state of the art descriptors. In the end, this paper provides a description of better feature selection methods in cross spectral matching