Whoi-Yul Kim

Bio: Whoi-Yul Kim is an academic researcher from Hanyang University. The author has contributed to research in topics: Image retrieval & Image processing. The author has an hindex of 26, co-authored 186 publications receiving 2782 citations. Previous affiliations of Whoi-Yul Kim include SK Hynix & Electronics and Telecommunications Research Institute.

A region-based shape descriptor using Zernike moments

Abstract: In order to retrieve an image from a large image database, the descriptor should be invariant to scale and rotation. It must also have enough discriminating power and immunity to noise for retrieval from a large image database. The Zernike moment descriptor has many desirable properties such as rotation invariance, robustness to noise, expression efficiency, fast computation and multi-level representation for describing the shapes of patterns. In this paper, we show that the Zernike moment can be used as an effective descriptor of global shape of an image in a large image database. The experimental results conducted on a database of about 6,000 images in terms of exact matching under various transformations and the similarity-based retrieval show that the proposed shape descriptor is very effective in representing shapes.

Hydrochromic conjugated polymers for human sweat pore mapping

Abstract: Hydrochromic materials have been actively investigated in the context of humidity sensing and measuring water contents in organic solvents. Here we report a sensor system that undergoes a brilliant blue-to-red colour transition as well as 'Turn-On' fluorescence upon exposure to water. Introduction of a hygroscopic element into a supramolecularly assembled polydiacetylene results in a hydrochromic conjugated polymer that is rapidly responsive (<20 μs), spin-coatable and inkjet-compatible. Importantly, the hydrochromic sensor is found to be suitable for mapping human sweat pores. The exceedingly small quantities (sub-nanolitre) of water secreted from sweat pores are sufficient to promote an instantaneous colorimetric transition of the polymer. As a result, the sensor can be used to construct a precise map of active sweat pores on fingertips. The sensor technology, developed in this study, has the potential of serving as new method for fingerprint analysis and for the clinical diagnosis of malfunctioning sweat pores.

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 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.