Catálogo de publicaciones - libros

This paper proposes an automatic approach to detecting objects appearing in front of planar background. A planar homography is estimated with high accuracy in an off-line initialization phase. Given a pair of binocular images, we apply the estimated homography to one of the images, and then compute a similarity map between the transformed image and the other. Normalized cross-correlation is used in the computation of the similarity map to measure the similarity between neighborhoods of overlapping pixels. Normalized cross-correlation measure is superior to absolute difference in alleviating the influence of image noise and small mis-alignment caused by imperfect homography estimation. The similarity map with pixel intensities ranging between 0 and 1 leads to an easy detection of out-of-plane objects because the values of pixels corresponding to planar background are close to 1. Tracking could be incorporated with our out-of-plane object detection method to further improve robustness in live video applications. This approach has been used in tracking people and demonstrated reliable performance.

3D images provide more information to human than their 2D counterparts and have many applications in entertainment, scientific data visualization, etc. The ability to generate accurate 3D dynamic scene and 3D movie from uncalibrated cameras is a challenge. We propose a systematic approach to stereo image/video generation. With our proposed approach, a realistic 3D scene can be created via either a single uncalibrated moving camera or two synchronized cameras. 3D video can also be generated through multiple synchronized video streams. Our approach first uses a Gabor filter bank to extract image features. Second, we develop an improved Elastic Graph Matching method to perform reliable image registration from multi-view images or video frames. Third, a fast and efficient image rectification method based on multi-view geometry is presented to create stereo image pairs. Extensive tests using real images collected from widely separated cameras were performed to test our proposed approach.

Immersive virtual environments with life-like interaction capabilities have very demanding requirements including high precision and processing speed. These issues raise many challenges for computer vision-based motion estimation algorithms. In this study, we consider the problem of hand tracking using multiple cameras and estimating its 3D global pose (i.e., position and orientation of the palm). Our interest is in developing an accurate and robust algorithm to be employed in an immersive virtual training environment, called ”Virtual GloveboX” (VGX) [1], which is currently under development at NASA Ames. In this context, we present a marker-based, hand tracking and 3D global pose estimation algorithm that operates in a controlled, multi-camera, environment built to track the user’s hand inside VGX. The key idea of the proposed algorithm is tracking the 3D position and orientation of an elliptical marker placed on the dorsal part of the hand using model-based tracking approaches and active camera selection. It should be noted that, the use of markers is well justified in the context of our application since VGX naturally allows for the use of gloves without disrupting the fidelity of the interaction. Our experimental results and comparisons illustrate that the proposed approach is more accurate and robust than related approaches. A byproduct of our multi-camera ellipse tracking algorithm is that, with only minor modifications, the same algorithm can be used to automatically re-calibrate (i.e., fine-tune) the extrinsic parameters of a multi-camera system leading to more accurate pose estimates.

This paper describes an idea for determining self-organization using visual land marks. The critical geometric dimensions of a pentagon are used here to locate the relative position of the mobile robot with respect to the pattern. This method has the advantages of simplicity and flexibility. This pentagon is also provided with a unique identification, using invariant features and colors that enable the system to find the absolute location of the patterns. This algorithm determines both the correspondence between observed landmarks and a stored sequence, computes the absolute location of the observer using those correspondences, and calculates relative position from a pentagon using its five vertices. The algorithm has been implemented and tested. In several trials it computes location accurate to within 5.4 centimeters in less than 0.3 second.

This paper describes a novel method for the industrial inspection of ophthalmic contact lenses in a time constrained production line environment. We discuss the background to this problem, look at previous solutions and relevant allied work before describing our system. An overview of the system is given together with detailed descriptions of the algorithms used to perform the image processing, classification and inspection system. We conclude with a preliminary assessment of the system performance and discuss future work needed to complete the system.

Large human motion databases contain variants of natural motions that are valuable for animation generation and synthesis. But retrieving visually similar motions is still a difficult and time-consuming problem. This paper provides methods for identifying visually and numerically similar motions in a large database given a query of motion segment. We propose an efficient indexing strategy that represents the motions compactly through a preprocessing. This representation scales down the range of searching the database. Motions in this range are possible candidates of the final matches. For detailed comparisons between the query and the candidates, we propose an algorithm that compares the motions’ curves swiftly. Our methods can apply to large human motion databases and achieve high performance and accuracy compared with previous work. We present experimental results on testing a database of about 2.9 million frames, or about 27 hours of motions played at 30 Hz.

In the past, it is difficult to simulate light shafts effect in real-time. One major reason is the high computational expense to perform the physically-accurate computation of atmosphere scattering. Another is due to the limitation of computer resource, especially lack of power and programmability in the graphic hardware. Recently, with the advent of more powerful graphic card in standard PC platform and the development of programmable stages in the graphic pipeline, a lot of computational expensive algorithms are made available in modern commercial games. In this paper, we propose a novel method of rendering light shafts with atmospheric scattering based on actual physical phenomena. The proposed method utilizes hardware frame buffer object and a mesh refinement pattern to achieve photorealistic effect at high frame rate.

This paper presents a computational model of stylistic similarity between human motions that is statistically derived from a comprehensive collection of captured, stylistically similar motion pairs. In this model, a set of hypersurfaces learned by single-class SVM and kernel PCA characterize the region occupied by stylistically similar motion pairs in the space of all possible pairs. The proposed model is further applied to a system for adapting an existing clip of human motion to a new environment, where stylistic distortion is avoided by enforcing stylistic similarity of the synthesized motion to the existing motion. The effectiveness of the system has been verified by 18 distinct adaptations, which produced walking, jumping, and running motions that exhibit the intended styles as well as the intended contact configurations.

Display technologies that support multiple independent views of the same co-located 3D visualization volume make new forms of collaboration possible. In this field of research, until now most efforts have focused on technical solutions and their applications. The main contribution of this paper is the results from a study comparing integral and partitioned 3D content in a head coupled stereoscopic environment through independent views of a shared 3D visualization.

In our study we used a geospatial task that was solved by ten pairs of collaborating individuals (dyads). We measured task performance by time and error rate for the dyads in two main conditions: a) an integral visualization that presented a map in the display surface and four layers at different depths below the display surface to each of the observers, and b) a partitioned visualization, where two mutually exclusive subsets of the layers were presented to each of the observers together with the map in the display surface.

The results from the study showed significant differences in regard to performance times between the two conditions. Task performance was significantly better in the condition with layer partitioning. Partitioned visualizations can thus, at least in some cases, improve performance in tasks requiring collaboration between users.

One fundamental step for image-related research is to obtain an accurate segmentation. Among the available techniques, the active contour algorithm has emerged as an efficient approach towards image segmentation. By progressively adjusting a reference curve using combination of external and internal force computed from the image, feature edges can be identified. The Gradient Vector Flow (GVF) is one efficient external force calculation for the active contour and a GPU-centric implementation of the algorithm is presented in this paper. Since the internal SIMD architecture of the GPU enables parallel computing, General Purpose GPU (GPGPU) based processing can be applied to improve the speed of the GVF active contour for large images. Results of our experiments show the potential of GPGPU in the area of image segmentation and the potential of the GPU as a powerful co-processor to traditional CPU computational tasks.