Ralladores de yuca brava tallados en madera con incrustaciones de piedras de los indígenas Tukanos y Curripacos del Vaupés.

6 hojas : ilustraciones, fotografías a color.

Muestras de productos tejidos en moriche y cumare de los indígenas Tucano Guaviare.

19 hojas : ilustraciones, fotografías a color.

Muestras de fajas tejidas con hilos de colores vivos por los indígenas Wayúu Guajira.

26 hojas : ilustraciones, fotografías a color.

C 175 INST-D 2013. 356 Bolsos tejidos en fique, hilos y lana a colores en las técnicas de macramé, crochet y telar.

57 fotografías, cicuenta y cuatro a color.

C 181 INST-D 2013. 362 Muestras surtida de chinchorros tejidos en hilos de algodón y combinación de colores vivos (Casanare)

31 fotografías a color.

C 197 INST-D 2013. 378 Ilustraciones de procesos de urdimbre y trama con lana en telares verticales.

14 fotografías a color y en tono de grises.

C 198 INST-D 2013. 379 Ilustración de tejidos manuales con paja toquilla y en telar de mesa en Sandoná - Nariño.

11 fotografías a color.

C 230 INST-D 2013. 392 Productos de menaje de cocina y comedor elaborados en madera tallada y torneada.

24 fotografías, dieciocho a color.

C 233 INST-D 2013. 395 Muebles en madera de diversos géneros de función y combinación de materiales.

20 fotografías, nueve a color y once en tono de grises (los archivos digitales de algunas fotografías son de muy bajo pixelaje).

C 234 INST-D 2013. 396 Productos en totumo y calabazo del Casanare decorados con figuras labradas, pirograbadas y pintadas

26 fotografías a color (los archivos digitales de algunas fotografías presentan bajo nivel de pixelaje).

Bisutería de manillas tejidas y collares elaborados con ensarte de materiales vegetales : proyecto de indígenas desplazados a Bogotá, Bogotá, 2003.

17 hojas : ilustraciones, fotografías a color.

Camera Canvas: Image Editing Software for People with Disabilities

Poster is based on the following paper: C. Kwan and M. Betke. Camera Canvas: Image editing software for people with disabilities. In Proceedings of the 14th International Conference on Human Computer Interaction (HCI International 2011), Orlando, Florida, July 2011.

Estimating 3D Hand Pose from a Cluttered Image

A method is proposed that can generate a ranked list of plausible three-dimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of synthetic hand images. In contrast to previous approaches, the system can function in the presence of clutter, thanks to two novel clutter-tolerant indexing methods. First, a computationally efficient approximation of the image-to-model chamfer distance is obtained by embedding binary edge images into a high-dimensional Euclide an space. Second, a general-purpose, probabilistic line matching method identifies those line segment correspondences between model and input images that are the least likely to have occurred by chance. The performance of this clutter-tolerant approach is demonstrated in quantitative experiments with hundreds of real hand images.

Integrated Chest Image Analysis System "BU-MIA"

We introduce "BU-MIA," a Medical Image Analysis system that integrates various advanced chest image analysis methods for detection, estimation, segmentation, and registration. BU-MIA evaluates repeated computed tomography (CT) scans of the same patient to facilitate identification and evaluation of pulmonary nodules for interval growth. It provides a user-friendly graphical user interface with a number of interaction tools for development, evaluation, and validation of chest image analysis methods. The structures that BU-MIA processes include the thorax, lungs, and trachea, pulmonary structures, such as lobes, fissures, nodules, and vessels, and bones, such as sternum, vertebrae, and ribs.

World Wide Web Image Search Engines

We propose the development of a world wide web image search engine that crawls the web collecting information about the images it finds, computes the appropriate image decompositions and indices, and stores this extracted information for searches based on image content. Indexing and searching images need not require solving the image understanding problem. Instead, the general approach should be to provide an arsenal of image decompositions and discriminants that can be precomputed for images. At search time, users can select a weighted subset of these decompositions to be used for computing image similarity measurements. While this approach avoids the search-time-dependent problem of labeling what is important in images, it still holds several important problems that require further research in the area of query by image content. We briefly explore some of these problems as they pertain to shape.