Nishan Canagarajah

Bio: Nishan Canagarajah is an academic researcher from University of Bristol. The author has contributed to research in topics: Video tracking & Image segmentation. The author has an hindex of 32, co-authored 145 publications receiving 3642 citations.

Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes

Abstract: Three novel feature extraction schemes for texture classification are proposed. The schemes employ the wavelet transform, a circularly symmetric Gabor filter or a Gaussian Markov random field with a circular neighbour set to achieve rotation-invariant texture classification. The schemes are shown to give a high level of classification accuracy compared to most existing schemes, using both fewer features (four) and a smaller area of analysis (16/spl times/16). Furthermore, unlike most existing schemes, the proposed schemes are shown to be rotation invariant demonstrate a high level of robustness noise. The performances of the three schemes are compared, indicating that the wavelet-based approach is the most accurate, exhibits the best noise performance and has the lowest computational complexity.

Wavelets for Image Fusion

Abstract: In this chapter we present some recent results on the use of wavelet algorithms for image fusion. The chapter starts with a brief introduction of image fusion. The following sections describe three different wavelet transforms and the way they can be employed to fuse 2-D images. These include: the discrete wavelet transform (DWT); the dual-tree complex wavelet transform (DT-CWT); and Mallat’s discrete dyadic wavelet transform (DDWT), which can also be used to compute a multiscale edge representation of an image. The three wavelet fusion schemes are compared both qualitatively and quantitatively and are applied to fuse multifocus, remote sensing and medical (CT and MR) images. The experimental comparison clearly shows that DT-CWT fusion techniques provide better results than their DWT counterparts. In addition, the use of DT-CWT gives control over directional information in the images, while the use of multiscale edge fusion methods provides control over the edge information to be retained in the fused output. The chapter concludes with a discussion about the strong points and difficulties associated with each of the proposed wavelet fusion schemes and with some ideas for future research.

A robust automatic clustering scheme for image segmentation using wavelets

Abstract: The optimal features with which to discriminate between regions and, thus, segment an image often differ depending on the nature of the image. Many real images are made up of both smooth and textured regions and are best segmented using different features in different areas. A scheme that automatically selects the optimal features for each pixel using wavelet analysis is proposed, leading to a robust segmentation algorithm. An automatic method for determining the optimal number of regions for segmentation is also developed.

Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

Abstract: Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns.

Digital Watermarking

Abstract: Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

Image Processing

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.