Catálogo de publicaciones - libros

This paper presents landmark based global self-localization of autonomous mobile robots in a known but highly dynamic environment. The algorithm is based on range estimation to naturally occurring distinct features as it is not possible to modify the environment with special navigational aids. These features are sparse in our application domain and are frequently occluded by other robots. To enable the robot to estimate its absolute position with respect to a single landmark it is equipped with dead-reckoning sensors in addition to the stereo vision system mounted on a rotating head. The pivoted stereo vision system of the robot enables it to measure range and use bi/trilateration based methods as they require fewer landmarks compared to angle based triangulation. Further reduction of landmarks is achieved when robot orientation is estimated independently. Simulation results are presented which illustrate the performance of our algorithm.

An Iterative Estimation Algorithm (IEA) of 3D transformations between two objects is presented in this paper. Skeletons of the 3D objects are extracted using a fully parallel thinning technique, feature point pairs (land markers) are extracted from skeletons automatically with a heuristic rule, and a least squares method and an iterative approach are applied to estimate the 3D transformation matrix. The algorithm has three advantages. First of all, no initial transformation matrix is needed. Secondly, user interaction is not required for identifying the land markers. Thirdly, the time complexity of this algorithm is polynomial. Experiments show that this method works quite well with high accuracy when the translations and rotation angles are small, even when noise exists in the data.

Four-dimensional (4D) visualization of medical data, which entails the addition of time as the fourth dimension to 3D data, is fast gaining ground as a tool for diagnosis and surgical planning by medical practitioners. However, current medical image acquisition techniques do not support high-resolution 4D capture. Instead, multiple 3D datasets are acquired and a temporal relation is computed between these datasets in order to align them in time. In past work we presented a method of temporal alignment of MRI datasets to generate high-resolution medical data, which can be extended to 4D visualization. In this work, we present the details of our temporal alignment algorithm and also present comparative analysis in order to highlight the advantages of our method.

We exploit the combination of a virtual world containing physically-interacting 4D objects with a multimodal haptics-driven user-interface model; the goal is to facilitate the development of accurate cognitive models enabling the visualization of 4D space. Our primary test domain supports tactile interaction with physically colliding and deformable curves and surfaces embedded in 4D, an important and challenging subject area of classical topology. We implement intricate interactions involving 4D curves and surfaces by haptically manipulating 3D projections of these objects.

In this paper, an intelligent and automatic moving object edge detection algorithm is proposed, based on heat flow analogy. This algorithm starts with anisotropic heat diffusion in the spatial domain to remove noise and sharpen region boundaries for the purpose of obtaining high quality edge data. Then, isotropic heat diffusion is applied in the temporal domain to calculate the total amount of heat flow. The moving edges are represented as the total amount of heat flow out from the reference frame. The overall process is completed by non-maxima suppression and hysteresis thresholding to obtain binary moving edges. Evaluation results indicate that this approach has advantages in handling noise in the temporal domain because of the averaging inherent of isotropic heat flow. Results also show that this technique can detect moving edges in image sequences.

We describe our preliminary research on integrating MRI video with a 3D surface scan of a face. Our approach first extracts contours of a video by using snakes [4, 5]; then the outline structure of the video is matched with a close matching contour on the 3D face structure. The matching and alignment of the two representations uses curvature representations along with some simple heuristics about the relative locations of the facial features, such as nose and chin.

Even though techniques like video fluoroscopy [8] can create high quality images, it subjects patients to high volumes of radiation, and cannot be used to monitor patients over short time intervals. Our alternative combines MRI video with 3D facial structure to improve visualization for medical professionals. The MRI video was created in our related research [7] by registering multiple MRI sequences of swallowing.

High-level structural information about macromolecules is now being organized into databases. One of the common ways of storing information in such databases is in the form of three-dimensional (3D) electron microscopic (EM) maps, which are 3D arrays of real numbers obtained by a reconstruction algorithm from EM projection data. We propose and demonstrate a method of automatically constructing, from any 3D EM map, a topological descriptor (which we call a history tree) that is amenable to automatic comparison.

In this paper, we propose a novel framework which can fuse multiple user selected features in different direct volume rendered images into a comprehensive image according to users’ preference. The framework relies on three techniques, i.e., , , and . In this framework, we transform the fusing problem to an optimization problem with a novel energy function which is based on user voting and image similarity. The optimization problem can then be solved by the genetic algorithm. Experimental results on some real volume data demonstrate the effectiveness of our framework.

In Uncalibrated Photometric Stereo (UPS), the surface normals and light sources are determined up to a group of ambiguous Generalized Bas-Relief (GBR) transformations. However, it has been shown by previous works to be rather troublesome to solve these ambiguities. In this paper, a framework of Binocular Uncalibrated Photometric Stereo (B-UPS) is given for accurate stereo matching for lambertian and non-lambertian objects. It is also shown that the problem of 3D reconstruction with UPS is converted into that of stereo matching with B-UPS. By this conversion, the intractable GBR transformations can be bypassed. In B-UPS, the Orientation-Consistency cue (OC) [1] for distant-lighting condition and Local-Orientation-Consistency (LOC) cue for non-distant lighting condition are used together for stereo matching, where the combination of both cues is made possible by a planar-area detection method based on a pseudo-normal-map segmentation scheme. Excellent matching and reconstruction results for objects with constant and spatial-varying BRDF demonstrate the superiority of B-UPS.

This paper introduces a method that presents a number of characteristics of threads in a discussion forum through graphical illustrations. This technique brings together visual components, such as dimension, color, intensity, and position to present multiple aspects of a thread including the amount of information, popularity, activities, comparative value, and tenure of the thread. This high visual abstraction of threads allows us to display a large number of threads showing overall properties of the contents on a limited screen space. These proposed visualization techniques will assist the user to filtering noisy threads effectively from threads having important features. We have conducted an experimental study, which compares the effectiveness of the developed visual interface to a traditional text-based interface. The experimental study has shown that the user’s search speed and accuracy in finding noticeable threads from a huge collection of threads has improved significantly by using the visual navigation tool.