Catálogo de publicaciones - libros

Academia and search engine industry followers consider personalization as the future of search engines, and this fact is well supported by the tremendous amount of research in this field. However the impact of technological advancement seems to be focused towards bringing more relevant results to the users - not the way it is usually presented to the users. User archives are useful resources which can be exploited more efficiently if reusability is promoted appropriately. In this paper, we present a theoretical framework which can sit on top of existing search technologies and deliver visually enhanced user experience and archival reusability. Contribution of this paper is two fold; first – visual interface for personal search engine setup, self-updating user interests, and session mapping based on modified spider graph; and secondly – enabling better archival reusability through user archival maps, session maps, interest specific maps and visual bookmarking.

Visualization is a technique used to facilitate the understanding of scientific results such as large data sets and maps. Provenance techniques can also aid in increasing the understanding and acceptance of scientific results by providing access to information about the sources and methods which were used to derive them. Visualization and provenance techniques, although rarely used in combination, may further increase scientists’ understanding of results since the scientists may be able to use a single tool to see and evaluate result derivation processes including any final or partial result. In this paper we introduce Probe-It!: a visualization tool for scientific provenance information that enables scientists to move the visualization focus from intermediate and final results to provenance back and forth. To evaluate the benefits of Probe-It!, in the context of maps, this paper presents a quantitative user study on how the tool was used by scientists to discriminate between quality results and results with known imperfections. The study demonstrates that only a very small percentage of the scientists tested can identify imperfections using maps without the help of knowledge provenance and that most scientists, whether GIS experts, subject matter experts (i.e., experts on gravity data maps) or not, can identify and explain several kinds of map imperfections when using maps together with knowledge provenance visualization.

Display systems with high quality and wide display screen can be used at fixed place due to big size and heavy weight. On the other hands, mobile systems have small display screen and thus decrease user-immersion because it is compact. In this paper, we resolve these drawbacks of established display systems by proposing a novel portable projection-based augmented reality (AR) system. The system uses a camera mounted on PDA and a small projector to measure characteristics of screen surface. We use geometric correction and radiometric compensation technique to project undistorted image in user viewpoint onto an arbitrary screen surface. Rather than float point operations, we use integer point operations to enhance system performance. Our proposed system not only supports mobility but also wide display screen. Usability of our system is verified through experimental results and user evaluation.

Beam projectors are widely used for large screen displays due to their high image quality at relatively low cost. Recently many works have been published extending the application area of beam projectors by employing non-ideal screens such as walls and ceilings. When the color of the non-conventional screen is not white, reflected color is shifted from the original. Therefore, we need to compensate for the color distortion. Past work has been limited to global gamut mapping where the mapping function is constant over a given color screen. The represented color range is the intersection of gamuts of various colored screen areas, which limits the usable gamut severely. We propose to use adaptive mapping function which depends on the screen color and the input image of each pixel. The proposed method attempts to employ the gamut at each screen position with a constraint that the mapping function changes smoothly over the screen to avoid abrupt color variation. Experiments with natural images show that our proposed method expands the gamut and enhances the color quality significantly.

This paper shows our work in progress towards an easy to use development environment for Mixed Reality (MR) Applications. We argue that development of MR applications is a collaboration between interaction designers who know about user requirements, and expert developers who know the intricacies of MR development. This collaboration should be supported by tools that aid both roles and ease their communication. We also argue that real MR development should allow easy migration from one hardware setup to another, since hardware greatly varies in these type of applications, and it is important to fit a solution to the particular user’s requirements and context. We show the foundational concepts in our work and current Integrated Development Environment (IDE) implementation. This work is based on InTml, a domain specific language for MR applications, and Eclipse, an open source, general purpose IDE.

This paper presents a system to monitor a space and detect intruders. Specifically, the system analyzes color video to determine if an intruder entered the space. The system compares any new items in a video frame to a collection of known items (e.g. pets) in order to allow known items to enter and leave the space. Simple trip-line systems using infrared sensors normally fail when a pet wanders into the path of a sensor. This paper details an adaptation of the mean shift algorithm (described by Comaniciu et al.) in RGB color space to discern between intruders and benign environment changes. A refinement to the histogram bin function used in the tracking algorithm is presented which increases the robustness of the algorithm.

In model-based object recognition and pose estimation, it is common for the set of extracted image features to be much larger than the set of object model features owing to clutter in the image. However, another class of recognition problems has a large model, but only a portion of the object is visible in the image, in which a small set of features can be extracted, most of which are salient. In this case, reducing the effective complexity of the object model is more important than the image clutter. We describe techniques to accomplish this by sampling the space of object positions. A subset of the object model is considered for each sampled pose. This reduces the complexity of the method from cubic to linear in the number of extracted features. We have integrated this technique into a system for recognizing craters on planetary bodies that operates in real-time.

Segmentation is a first and important step in video-based motion capture applications. A lack of constraints can make this process daunting and difficult to achieve. We propose a technique that makes use of an improved JSEG procedure in the context of markerless motion capture for performance evaluation of human beings in unconstrained environments. In the proposed algorithm a non-parametric clustering of image data is performed in order to produce homogenous colour-texture regions. The clusters are modified using soft –classifications and allow the J-Value segmentation to deal with smooth colour and lighting transitions. The regions are adapted using an original merging and video stack tracking algorithm.

Volumetric data rendering has become an important tool in various medical procedures as it allows the unbiased visualization of fine details of volumetric medical data (CT, MRI, fMRI). However, due to the large amount of computation involved, the rendering time increases dramatically as the size of the data set grows. This paper presents several acceleration techniques of volume rendering using general-purpose GPU. Some techniques enhance the rendering speed of software ray casting based on voxels’ opacity information, while the others improve traditional hardware-accelerated object-order volume rendering. Remarkable speedups are observed using the proposed GPU-based algorithm from experiments on routine medical data sets.

This paper proposes a new approach for structure based separation of image objects using fuzzy morphology. With set operators in fuzzy context, we apply an adaptive alpha-cut morphological processing for edge detection, image enhancement and segmentation. A Top-hat transform is first applied to the input image and the resulting image is thresholded to a binary form. The image is then thinned using hit-or-miss transform. Finally, m-connectivity is used to keep the desired number of connected pixels. The output image is overlayed on the original for enhanced boundaries. Experiments were performed using real images of aerial views, sign boards and biological objects. A comparison to other edge enhancement techniques like unsharp masking, sobel and laplacian filtering shows improved performance by the proposed technique.