Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Segmentation of color images based on the gravitational clustering concept

A new clustering algorithm derived from the Markovian model of the gravitational clustering concept is proposed that works in the RGB measurement space for color image. To enable the model to be applicable in image segmentation, the new algorithm imposes a clustering constraint at each clustering iteration to control and determine the formation of multiple clusters. Using such constraint to limit the attraction between clusters, a termination condition can be easily defined. The new clustering algorithm is evaluated objectively and subjectively on three different images against the K-means clustering algorithm, the recursive histogram clustering algorithm for color (also known as the multi-spectral thresholding), the Hedley-Yan algorithm, and the widely used seed-based region growing algorithm. From the evaluation, it is observed that the new algorithm exhibits the following characteristics: (1) its objective measurement figures are comparable with the best in this group of segmentation algorithms; (2) it generates smoother region boundaries; (3) the segmented boundaries align closely with the original boundaries; and (4) it forms a meaningful number of segmented regions. © 1998 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio

Using Computer Vision to Quantify Coral Reef Biodiversity

The preservation of the world’s oceans is crucial to human survival on this planet, yet we know too little to begin to understand anthropogenic impacts on marine life. This is especially true for coral reefs, which are the most diverse marine habitat per unit area (if not overall) as well as the most sensitive. To address this gap in knowledge, simple field devices called autonomous reef monitoring structures (ARMS) have been developed, which provide standardized samples of life from these complex ecosystems. ARMS have now become successful to the point that the amount of data collected through them has outstripped the capacity of research organizations to analyze through molecular methods. To facilitate these efforts, the present study explores the use of computer vision techniques to analyze the complex image data of these samples in order to extract useful information based on morphological (visual) characteristics of the collected organisms. Various techniques at varying levels of sophistry are surveyed for their suitability to the present problem. In the end, the more complex techniques are ruled out in the favor of basic image processing ones, of which three are tested: canny edge detection, color space transformations, and histogram equalization. While the first one does not directly yield useful results, the latter two turn out to be surprisingly effective, showing great promise as means to prepare data that more sophisticated techniques can be subsequently trained on. Future directions of investigation are recorded in detail, along with suggestions and relevant references, towards ultimately realizing an online analysis tool and repository for marine life that would accelerate related research and conservation efforts

Automatic extraction of the size of myocardial infarction in an experimental murine model

Tese de mestrado. Engenharia Biomédica. Universidade do Porto. Faculdade de Engenharia. 201

Learning Object Categories From Internet Image Searches

In this paper, we describe a simple approach to learning models of visual object categories from images gathered from Internet image search engines. The images for a given keyword are typically highly variable, with a large fraction being unrelated to the query term, and thus pose a challenging environment from which to learn. By training our models directly from Internet images, we remove the need to laboriously compile training data sets, required by most other recognition approaches-this opens up the possibility of learning object category models “on-the-fly.” We describe two simple approaches, derived from the probabilistic latent semantic analysis (pLSA) technique for text document analysis, that can be used to automatically learn object models from these data. We show two applications of the learned model: first, to rerank the images returned by the search engine, thus improving the quality of the search engine; and second, to recognize objects in other image data sets