Catálogo de publicaciones - libros

A novel active-contour method is presented and applied to pose refinement and tracking. The main innovation is that no ”features” are detected at any stage: contours are simply assumed to remove statistical dependencies between pixels on opposite sides of the contour. This assumption, together with a simple model of shape variability of the geometric models, leads to the application of an EM method for maximizing the likelihood of pose parameters. In addition, a dynamical model of the system leads to the application of a Kalman filter. The method is demonstrated by tracking motor vehicles with 3-D models.

We present a system that is capable of segmenting, detecting and tracking multiple people in a cluttered scene using multiple synchronized cameras located far from each other. The system improves upon existing systems in many ways including: (1) We do not assume that a foreground connected component belongs to only one object; rather, we segment the views taking into account color models for the objects and the background. This helps us to not only separate foreground regions belonging to different objects, but to also obtain better background regions than traditional background subtraction methods (as it uses foreground color models in the algorithm). (2) It is fully automatic and does not require any manual input or initializations of any kind. (3) Instead of taking decisions about object detection and tracking from a single view or camera pair, we collect evidences from each pair and combine the evidence to obtain a decision in the end. This helps us to obtain much better detection and tracking as opposed to traditional systems.

Several innovations help us tackle the problem. The first is the introduction of a region-based stereo algorithm that is capable of finding 3D points inside an object if we know the regions belonging to the object in two views. No exact point matching is required. This is especially useful in wide baseline camera systems where exact point matching is very difficult due to self-occlusion and a substantial change in viewpoint. The second contribution is the development of a scheme for setting priors for use in segmentation of a view using bayesian classification. The scheme, which assumes knowledge of approximate shape and location of objects, dynamically assigns priors for different objects at each pixel so that occlusion information is encoded in the priors. The third contribution is a scheme for combining evidences gathered from different camera pairs using occlusion analysis so as to obtain a globally optimum detection and tracking of objects.

The system has been tested using different density of people in the scene which helps us to determine the number of cameras required for a particular density of people.

In this paper, we study a family of analytical probability models for images within the spectral representation framework. First the input image is decomposed using a bank of filters, and probability models are imposed on the filter outputs (or spectral components). A two-parameter analytical form, called a , derived based on a generator model, is used to model the marginal probabilities of these spectral components. The Bessel K parameters can be estimated efficiently from the filtered images and extensive simulations using video, infrared, and range images have demonstrated Bessel K form’s fit to the observed histograms. The effectiveness of Bessel K forms is also demonstrated through texture modeling and synthesis. In contrast to numeric-based dimension reduction representations, which are derived purely based on numerical methods, the Bessel K representations are derived based on object representations and this enables us to establish relationships between the Bessel parameters and certain characteristics of the imaged objects. We have derived a pseudometric on the image space to quantify image similarities/differences using an analytical expression for -metric on the set of Bessel K forms. We have applied the Bessel K representation to texture modeling and synthesis, clutter classification, pruning of hypotheses for object recognition, and object classification. Results show that Bessel K representation captures important image features, suggesting its role in building efficient image understanding paradigms and systems.

This paper presents a theory for constructing and computing velocity-adapted scale-space filters for spatio-temporal image data. Starting from basic criteria in terms of time-causality, time-recursivity, locality and adaptivity with respect to motion estimates, a family of spatio-temporal recursive filters is proposed and analysed. An important property of the proposed family of smoothing kernels is that the spatio-temporal covariance matrices of the discrete kernels obey similar transformation properties under Galilean transformations as for continuous smoothing kernels on continuous domains. Moreover, the proposed theory provides an efficient way to compute and generate non-separable scale-space representations without need for explicit external warping mechanisms or keeping extended temporal buffers of the past. The approach can thus be seen as a natural extension of recursive scale-space filters from pure temporal data to spatio-temporal domains.

The problem of establishing image-to-image correspondences is fundamental in computer vision. Recently, several wide baseline matching algorithms capable of handling large changes of viewpoint have appeared. By computing feature values from image data, these algorithms mainly use appearance as a cue for matching. Topological information, i.e. spatial relations between features, has also been used, but not nearly to the same extent as appearance. In this paper, we incorporate topological constraints into an existing matching algorithm [] which matches image intensity profiles between interest points. We show that the algorithm can be improved by exploiting the constraint that the intensity profiles around each interest point should be cyclically ordered. String matching techniques allows for an efficient implementation of the ordering constraint. Experiments with real data indicate that the modified algorithm indeed gives superior results to the original one. The method of enforcing the spatial constraints is not limited to the presented case, but can be used on any algorithm where interest point correspondences are sought.

We present techniques for improving the speed of robust motion estimation based on random sampling of image features. Starting from Torr and Zisserman’s MLESAC algorithm, we address some of the problems posed from both practical and theoretical standpoints and in doing so allow the random search to be replaced by a guided search. Guidance of the search is based on readily-available information which is usually discarded, but can significantly reduce the search time. This guided-sampling algorithm is further specialised for tracking of multiple motions, for which results are presented.

We derive the decomposition of the anisotropic Gaussian in a one dimensional Gauss filter in the -direction followed by a one dimensional filter in a non-orthogonal direction . So also the anisotropic Gaussian can be decomposed by dimension. This appears to be extremely efficient from a computing perspective. An implementation scheme for normal convolution and for recursive filtering is proposed. Also directed derivative filters are demonstrated.

For the recursive implementation, filtering an 512 × 512 image is performed within 65 msec, independent of the standard deviations and orientation of the filter. Accuracy of the filters is still reasonable when compared to truncation error or recursive approximation error.

The anisotropic Gaussian filtering method allows fast calculation of edge and ridge maps, with high spatial and angular accuracy. For tracking applications, the normal anisotropic convolution scheme is more advantageous, with applications in the detection of dashed lines in engineering drawings. The recursive implementation is more attractive in feature detection applications, for instance in affine invariant edge and ridge detection in computer vision. The proposed computational filtering method enables the practical applicability of orientation scale-space analysis.

The propose of this paper is to introduce a new regularization formulation for inverse problems in computer vision and image processing that allows one to reconstruct second order piecewise smooth images, that is, images consisting of an assembly of regions with almost constant value, almost constant slope or almost constant curvature. This formulation is based on the idea of using potential functions that correspond to springs or thin plates with an adaptive rest condition. Efficient algorithms for computing the solution, and examples illustrating the performance of this scheme, compared with other known regularization schemes are presented as well.

This paper presents a novel approach for detecting affine invariant interest points. Our method can deal with significant affine transformations including large scale changes. Such transformations introduce significant changes in the point location as well as in the scale and the shape of the neighbourhood of an interest point. Our approach allows to solve for these problems simultaneously. It is based on three key ideas: 1) The second moment matrix computed in a point can be used to normalize a region in an affine invariant way (skew and stretch). 2) The scale of the local structure is indicated by local extrema of normalized derivatives over scale. 3) An affine-adapted Harris detector determines the location of interest points. A multi-scale version of this detector is used for initialization. An iterative algorithm then modifies location, scale and neighbourhood of each point and converges to affine invariant points. For matching and recognition, the image is characterized by a set of affine invariant points; the affine transformation associated with each point allows the computation of an affine invariant descriptor which is also invariant to affine illumination changes. A quantitative comparison of our detector with existing ones shows a significant improvement in the presence of large affine deformations. Experimental results for wide baseline matching show an excellent performance in the presence of large perspective transformations including significant scale changes. Results for recognition are very good for a database with more than 5000 images.

In order to investigate the deep structure of Gaussian scale space images, one needs to understand the behaviour of critical points under the influence of parameter-driven blurring. During this evolution two different types of special points are encountered, the so-called scale space saddles and the catastrophe points, the latter describing the pairwise annihilation and creation of critical points. The mathematical framework of catastrophe theory is used to model non-generic events that might occur due to e.g. local symmetries in the image. It is shown how this knowledge can be exploited in conjunction with the scale space saddle points, yielding a scale space hierarchy tree that can be used for segmentation. Furthermore the relevance of creations of pairs of critical points with respect to the hierarchy is discussed. We clarify the theory with an artificial image and a simulated MR image.