Roland Kwitt

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In this paper, we investigate a novel joint statistical model for subband coefficient magnitudes of the dual-tree complex wavelet transform, which is then coupled to a Bayesian framework for content-based image retrieval. The joint model allows to capture the association among transform coefficients of the same decomposition scale and different color(More)
This paper contemplates the framework of probabilistic image retrieval in the wavelet domain from a computational point of view. We not only focus on achieving high retrieval rates, but also discuss possible performance bottlenecks which might prevent practical application. We propose a novel retrieval approach which is motivated by previous research work(More)
Topological data analysis offers a rich source of valuable information to study vision problems. Yet, so far we lack a theoretically sound connection to popular kernel-based learning techniques, such as kernel SVMs or kernel PCA. In this work, we establish such a connection by designing a multi-scale kernel for persistence diagrams, a stable summary(More)
This article aims at lightweight, blind detection of additive spread-spectrum watermarks in the DWT domain. We focus on two host signal noise models and two types of hypothesis tests for watermark detection. As a crucial point of our work we take a closer look at the computational requirements of watermark detectors. This involves the computation of the(More)
Various techniques have been developed for an automated classification of endoscopic images. Besides the classical methods for endoscopic image capturing, new methods like the modified immersion technique have been devised and are in use. The impact of specific image capturing techniques for feature extraction and classification in automated diagnosis is(More)
In this work, we present a texture-image retrieval approach, which is based on the idea of measuring the Kullback-Leibler divergence between the marginal distributions of complex wavelet coefficient magnitudes. We employ Kingsbury's dual-tree complex wavelet transform for image decomposition and propose to model the detail subband coefficient magnitudes by(More)
We present a Copula-based statistical model of complex wavelet coefficient magnitudes for color texture image retrieval. Our model is based on two-parameter Weibull distributions and a multivariate Student t Copula. For similarity measurement we employ a Monte- Carlo approach to approximate the Kullback-Leibler divergence between two models. The(More)
In this paper, we propose a set of new image features for the classification of zoom-endoscopy images. The feature extraction step is based on fitting a two-parameter Weibull distribution to the wavelet coefficient magnitudes of sub-bands obtained from a complex wavelet transform variant. We show, that the shape and scale parameter possess more(More)