Igor Berezhnoy

Bio: Igor Berezhnoy is an academic researcher from Philips. The author has contributed to research in topics: Painting & Audio signal processing. The author has an hindex of 8, co-authored 10 publications receiving 429 citations. Previous affiliations of Igor Berezhnoy include Maastricht University.

Image processing for artist identification

Abstract: A survey of the literature reveals that image processing tools aimed at supplementing the art historian's toolbox are currently in the earliest stages of development. To jump-start the development of such methods, the Van Gogh and Kroller-Muller museums in The Netherlands agreed to make a data set of 101 high-resolution gray-scale scans of paintings within their collections available to groups of image processing researchers from several different universities. This article describes the approaches to brushwork analysis and artist identification developed by three research groups, within the framework of this data set.

A method and apparatus for providing information about the source of a sound via an audio device

Abstract: A method and apparatus for providing information about the source of a sound via an audio device is described. An ambient sound is detected (200) and specific sounds are identified in the detected ambient sound (202). Information about the source of the identified specific sounds is determined (204). An operational control characteristic of a generated audio stream rendered by an audio device is changed (206) and information about the source is provided to the audio device upon detection of said identified specific sounds (208).

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Image retrieval: Ideas, influences, and trends of the new age

Abstract: We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

Task-Driven Dictionary Learning

Abstract: Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks. In this context, learning the dictionary amounts to solving a large-scale matrix factorization problem, which can be done efficiently with classical optimization tools. The same approach has also been used for learning features from data for other purposes, e.g., image classification, but tuning the dictionary in a supervised way for these tasks has proven to be more difficult. In this paper, we present a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and present an efficient algorithm for solving the corresponding optimization problem. Experiments on handwritten digit classification, digital art identification, nonlinear inverse image problems, and compressed sensing demonstrate that our approach is effective in large-scale settings, and is well suited to supervised and semi-supervised classification, as well as regression tasks for data that admit sparse representations.

Aesthetics and Emotions in Images

Abstract: In this tutorial, we define and discuss key aspects of the problem of computational inference of aesthetics and emotion from images. We begin with a background discussion on philosophy, photography, paintings, visual arts, and psychology. This is followed by introduction of a set of key computational problems that the research community has been striving to solve and the computational framework required for solving them. We also describe data sets available for performing assessment and outline several real-world applications where research in this domain can be employed. A significant number of papers that have attempted to solve problems in aesthetics and emotion inference are surveyed in this tutorial. We also discuss future directions that researchers can pursue and make a strong case for seriously attempting to solve problems in this research domain.

Image processing for artist identification

Abstract: A survey of the literature reveals that image processing tools aimed at supplementing the art historian's toolbox are currently in the earliest stages of development. To jump-start the development of such methods, the Van Gogh and Kroller-Muller museums in The Netherlands agreed to make a data set of 101 high-resolution gray-scale scans of paintings within their collections available to groups of image processing researchers from several different universities. This article describes the approaches to brushwork analysis and artist identification developed by three research groups, within the framework of this data set.