Catálogo de publicaciones - libros

Auto-focusing is an indispensable function for imaging systems used in surveillance and object tracking. In this paper, we conduct a study of an image-based passive auto-focusing control for high magnification (>50×) systems using off-the-shelf telescopes and digital camcorders with applications to long range near-ground surveillance and face tracking. Considering both speed of convergence and robustness to image degradations induced by high system magnifications and long observation distances, we introduce an auto-focusing mechanism suitable for such applications, including hardware design and algorithm development. We focus on the derivation of the transition criteria following maximum likelihood (ML) estimation for the selection of adaptive step sizes and the use of sharpness measures for the proper evaluation of high magnification images. The efficiency of the proposed system is demonstrated in real-time auto-focusing and tracking of faces from distances of 50m~300m.

In trifocal transfer based novel view synthesis, matched pixels of both input views are projected in the novel view. The angle of view of this latest is usually narrow, i.e. the novel view is very close to input ones. In this paper we improve the method to get a large angle of view. A simplex approach is used to compute the model of the virtual views pose. This model allows the computation of the novel view at any desired angle of view. We also show that those results are very useful in micromanipulation tasks where transfer of edges is enough instead of the entire pixels of input views.

Diabetic retinopathy (DR) has been identified as a leading cause of blindness. One type of lesion, neovascularization (NV), indicates that the disease has entered a vision-threatening phase. Early detection of NV is thus clinically significant. Efforts have been devoted to use computer-aided analyses of digital retina images to detect DR. However, developing reliable NV detection algorithms requires large numbers of digital retinal images to test and refine approaches. Computer simulation of NV offers the potential of developing lesion detection algorithms without the need for large image databases of real pathology. In this paper, we propose a systematic approach to simulating NV. Specifically, we propose two algorithms based on fractal models to simulate the main structure of NV and an adaptive color generation method to assign photorealistic pixel values to the structure. Moreover, we develop an interactive system that provides instant visual feedback to support NV simulation guided by an ophthalmologist. This enables us to combine the low level algorithms with high-level human feedback to simulate realistic lesions. Experiments suggest that our method is able to produce simulated NVs that are indistinguishable from real lesions.

One of the most challenging and fundamental problems in computer vision is to reconstruct a surface model given a set of uncalibrated 2D images. Well-established Structure from Motion (SfM) algorithms often result in a sparse set of 3D surface points, but surface modelling based on sparse 3D points is not easy. In this paper, we present a new method to refine and optimise surface meshes using edge information in the 2D images. We design a meshing – edge point detection – re-meshing scheme that can gradually refine the surface mesh until it best fits the true physical surface of the object being modelled. Our method is tested on real images and satisfactory results are obtained.

In computer vision applications of robust estimation techniques, it is usually assumed that a large number of data samples are available. As a result, the finite sample bias of estimation processes has been overlooked. This is despite the fact that many asymptotically unbiased estimators have substantial bias in cases where a moderate number of data samples are available. Such cases are frequently encountered in computer vision practice, therefore, it is important to choose the right estimator for a given task by virtue of knowing its finite sample bias. This paper investigates the finite sample bias of robust scale estimation and analyses the finite sample performance of three modern robust scale estimators (Modified Statistical Scale Estimator, Residual Consensus estimator and Two-Step Scale Estimator) that have been used in computer vision applications. Simulations and real data experiments are used to verify the results.

We present a novel structure tensor for matrix-valued images. It allows for user defined parameters that add flexibility to a number of image processing algorithms for the segmentation and smoothing of tensor fields. We provide a thorough theoretical derivation of the new structure tensor, including a proof of the equivalence of its unweighted version to the existing structure tensor from the literature. Finally, we demonstrate its advantages for segmentation and smoothing, both on synthetic tensor fields and on real DT-MRI data.

We present a visualization system that helps designers conceptualise interactions in a virtual environment (VE). We use event-condition-action triads (triggersets) for specifying interactions, and provide multiple visualizations: sequence diagrams, floorplans and timelines. We present a two part study: sequencing VE interactions accurately and debugging mistakes. Subjects were divided into two groups: one received visualizations and triggersets and the other (a control group) received triggersets only. The visualization group described 72.5% of the sequence correctly on average, compared to 56.4% by the non-visualization group. The visualization group also detected more than twice as many errors as the control group. The visualization group worked well with multiple, linked windows to create an understanding of the design. Floorplans were most useful for an overview, timelines for understanding specific sequences and sequence diagrams for sequencing and finding mistakes.

Skeletons are often used as a framework for part-based shape analysis. This paper describes some useful strategies that can be employed to improve the performance of such shape matching algorithms. Four key strategies are proposed. The first is to incorporate ligature-sensitive information into the part decomposition and shape matching processes. The second is to treat part decomposition as a dynamic process in which the selection of the final decomposition of a shape is deferred until the shape matching stage. The third is the need to combine both local and global measures when computing shape dissimilarity. Finally, curvature error between skeletal segments must be weighted by the limb-width profile along the skeleton. Experimental results show that the incorporation of these strategies significantly improves the retrieval accuracy when applied to LEMS’s 99 and 216 silhouette database [10].

We present a novel fully-automatic approach for estimating an articulated skeleton of a moving subject and its motion from body marker trajectories that have been measured with an optical motion capture system. Our method does not require a priori information about the shape and proportions of the tracked subject, can be applied to arbitrary motion sequences, and renders dedicated initialization poses unnecessary. To serve this purpose, our algorithm first identifies individual rigid bodies by means of a variant of spectral clustering. Thereafter, it determines joint positions at each time step of motion through numerical optimization, reconstructs the skeleton topology, and finally enforces fixed bone length constraints. Through experiments, we demonstrate the robustness and efficiency of our algorithm and show that it outperforms related methods from the literature in terms of accuracy and speed.

In this paper we present a system that performs automatic gesture recognition. The system consists of two main components: (i) A unified technique for segmentation and tracking of face and hands using a skin detection algorithm along with handling occlusion between skin objects to keep track of the status of the occluded parts. This is realized by combining 3 useful features, namely, color, motion and position. (ii) A static and dynamic gesture recognition system. Static gesture recognition is achieved using a robust hand shape classification, based on PCA subspaces, that is invariant to scale along with small translation and rotation transformations. Combining hand shape classification with position information and using DHMMs allows us to accomplish dynamic gesture recognition.