Dimitrios Androutsos

Color Image Watermarking Using Multidimensional Fourier Transforms

This paper presents two vector watermarking schemes that are based on the use of complex and quaternion Fourier transforms and demonstrates, for the first time, how to embed watermarks into the frequency domain that is consistent with our human visual system. Watermark casting is performed by estimating the just-noticeable distortion of the images, to ensure watermark invisibility. The first method encodes the chromatic content of a color image into the CIE chromaticity coordinates while the achromatic content is encoded as CIE tristimulus value. Color watermarks (yellow and blue) are embedded in the frequency domain of the chromatic channels by using the spatiochromatic discrete Fourier transform. It first encodes and as complex values, followed by a single discrete Fourier transform. The most interesting characteristic of the scheme is the possibility of performing watermarking in the frequency domain of chromatic components. The second method encodes the components of color images and watermarks are embedded as vectors in the frequency domain of the channels by using the quaternion Fourier transform. Robustness is achieved by embedding a watermark in the coefficient with positive frequency, which spreads it to all color components in the spatial domain and invisibility is satisfied by modifying the coefficient with negative frequency, such that the combined effects of the two are insensitive to human eyes. Experimental results demonstrate that the two proposed algorithms perform better than two existing algorithms - ac- and discrete cosine transform-based schemes.

Adaptive fuzzy systems for multichannel signal processing

Processing multichannel signals using digital signal processing techniques has received increased attention lately due to its importance in applications such as multimedia technologies and telecommunications. The objective of this paper is twofold: 1) to introduce adaptive filtering techniques to the reader who is just beginning in this area and 2) to provide a review for the reader who may be well versed in signal processing. The perspective of the topic offered here is one that comes primarily from work done in the field of multichannel (color) image processing. Hence, many of the techniques and works cited here relate to image processing with the emphasis placed primarily on filtering algorithms based on fuzzy concepts, multidimensional scaling, and order statistics-based designs. It should be noted, however, that multichannel signal processing is a very broad field and thus contains many other approaches that have been developed from different perspectives, such as transform domain filtering, classical least-square approaches, neural networks, and stochastic methods, just to name a few. We present a general formulation based on fuzzy concepts, which allows the use of adaptive weights in the filtering structure, and we discuss different filter designs. The strong potential of fuzzy adaptive filters for multichannel signal applications, such as color image processing, is illustrated with several examples.

A Novel Vector-Based Approach to Color Image Retrieval Using a Vector Angular-Based Distance Measure

Color is the characteristic which is most used for image indexing and retrieval. Due to its simplicity, the color histogram remains the most commonly used method for this task. However, the lack of good perceptual histogram similarity measures, the global color content of histograms, and the erroneous retrieval results due to gamma nonlinearity, call for improved methods. We present a new scheme which implements a recursive HSV-space segmentation technique to identify perceptually prominent color areas. The average color vector of these extracted areas are then used to build the image indices, requiring very little storage. Our retrieval is performed by implementing a combination distance measure, based on the vector angle between two vectors. Our system provides accurate retrieval results and high retrieval rate. It allows for queries based on single or multiple colors and, in addition, it allows for certain colors to be excluded in the query. This flexibility is due to our distance measure and the multidimensional query space in which the retrieval ranking of the database images is determined. Furthermore, our scheme proves to be very resistant to gamma nonlinearity providing robust retrieval results for a wide range of gamma nonlinearity values, which proves to be of great importance since, in general, the image acquisition source is unknown.

Color image processing using adaptive multichannel filters

New adaptive filters for color image processing are introduced and analyzed. The proposed adaptive methodology constitutes a unifying and powerful framework for multichannel signal processing. Using the proposed methodology, color image filtering problems are treated from a global viewpoint that readily yields and unifies previous, seemingly unrelated, results. The new filters utilize Bayesian techniques and nonparametric methodologies to adapt to local data in the color image. The principles behind the new filters are explained in detail. Simulation studies indicate that the new filters are computationally attractive and have excellent performance.

Distance measures for color image retrieval

We address the issue of image database retrieval based on color using various vector distance metrics. Our system is based on color segmentation where only a few representative color vectors are extracted from each image and used as image indices. These vectors are then used with vector distance measures to determine similarity between a query color and a database image. We test numerous popular vector distance measures in our system and find that directional measures provide the most accurate and perceptually relevant retrievals.

Color image processing using adaptive vector directional filters

A new class of filters for multichannel image processing is introduced and analyzed. This class constitutes a generalization of vector directional filters. The proposed filters use fuzzy transformations of the angles among the different vectors to adapt to local data in the image. The principle behind the new filters is explained and comparisons with other popular nonlinear filters are provided. The specific case of color image processing is studied as an important example of multichannel image processing. Simulation results indicate that the new filters offer some flexbility and have excellent performance.

An adaptive nearest neighbor multichannel filter

This paper addresses the problem of noise attenuation for multichannel data. The proposed filter utilizes adaptively determined data-dependent coefficients based on a novel distance measure which combines vector directional with vector magnitude filtering. The special case of color image processing is studied as an important example of multichannel signal processing.

Fuzzy adaptive filters for multichannel image processing

Abstract New filter classes for multichannel image processing are introduced and analyzed. The proposed methodology constitutes a unifying and powerful framework for multichannel image processing. The new filters use fuzzy membership functions based on different distance measures among the image vectors to adapt to local data in the image. The principle behind the new nonlinear filters is explained in detail. Using the proposed methodology multichannel nonlinear problems are treated from a global viewpoint that readily yields and unifies previous, seemingly unrelated results. The new approach provides insight into the nature of the filtering process and the structure of the underlying nonlinear operator. The special case of color image processing is studied as an important example of multichannel image processing. Simulation results indicate that the new filters are computationally attractive and have excellent performance.

Content-based retrieval of logo and trademarks in unconstrained color image databases using Color Edge Gradient Co-occurrence Histograms

In this paper, we present an algorithm that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme on compound color objects, for the retrieval of logos and trademarks in unconstrained color image databases. We introduce more accurate information to the CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, as compared to the simple color pixel difference classification of edges seen with the CECH. Our proposed method is thus reliant on edge gradient information, and so we call it the Color Edge Gradient Co-occurrence Histogram (CEGCH). We also introduce a color quantization method based in the hue-saturation-value (HSV) color space, illustrating that it is a more suitable scheme of quantization for image retrieval, compared to the color quantization scheme introduced with the CECH. Experimental results demonstrate that the CEGCH and the HSV color quantization scheme is insensitive to scaling, rotation, and partial deformations, and outperforms the use of the CECH in image retrieval, with higher precision and recall. We also perform experiments on a closely related algorithm based on the Color Co-occurrence Histogram (CCH) and demonstrate that our algorithm is also superior in comparison, with higher precision and recall.

Nonlinear Filtering of Non-Gaussian Noise

This paper introduces a new nonlinear filter for a discrete time, linear system which is observed in additive non-Gaussian measurement noise. The new filter is recursive, computationally efficient and has significantly improved performance over other linear and nonlinear schemes. The problem of narrowband interference suppression in additive noise is considered as an important example of non-Gaussian noise filtering. It is shown that the new filter outperforms currently used approaches and at the same time offers simplicity in the design.