Geert Caenen

Composite Texture Synthesis

Many textures require complex models to describe their intricate structures. Their modeling can be simplified if they are considered composites of simpler subtextures. After an initial, unsupervised segmentation of the composite texture into the subtextures, it can be described at two levels. One is a label map texture, which captures the layout of the different subtextures. The other consists of the different subtextures. This scheme has to be refined to also include mutual influences between textures, mainly found near their boundaries. The proposed composite texture model also includes these. The paper describes an improved implementation of this idea. Whereas in a previous implementation subtextures and their interactions were synthesized sequentially, this paper proposes a parallel implementation, which yields results of higher quality.

Panoramic, adaptive and reconfigurable interface for similarity search

We outline the architecture of a content-based image retrieval (CBIR)-interface that offers the user a graphical tool to create new features by showing (as opposed to telling!) the system what he means. It allows him to interactively classify images by dragging and dropping them into different piles and instructing the interface to come up with features that can mimic this classification. We show how logistic regression and Sammon projection can be used to supervise this search mode.

Logistic regression model for relevance feedback in content-based image retrieval

We introduce logistic regression to model the dependence between image-features and the relevance that is implicitly defined by user-feedback. We assume that while browsing, the user can single out images as either examples or counter-examples of the sort of picture he is looking for. Based on this information, the system will construct logistic regression models that generalize this relevance probability to all images in the database. This information is then used to iteratively bias the next sample from the database. Furthermore, the diagnostics that are an integral part of the regression procedure can be harnessed for adaptive feature selection by removing features that have low predictive power.

Show Me What You Mean! Pariss: A CBIR-Interface that Learns by Example

We outline the architecture of a CBIR-interface that allows the user to interactively classify images by dragging and dropping them into different piles and instructing the interface to come up with features that can mimic this classification. Logistic regression and Sammon projection are used to support this search mode.

Analysis of Human Locomotion based on Partial Measurements

A lot of computer vision applications have to deal with occlusions. In such settings only a subset of the features of interest can be observed, i.e. only incomplete or partial measurements are available. In this article we show how a learned statistical model can be used to make a prediction of the unknown (occluded) features. The probabilistic nature of the framework also allows to compute the remaining uncertainty given an incomplete observation. The resulting posterior probability distribution can then be used for inference. Additional unknowns such as alignment or scale are easily incorporated into the framework. Instead of computing the alignment in a preprocessing step, it is left as an additional uncertainty, similar to the uncertainty introduced by the missing values of the measurement. It is shown how the technique can be applied to the analysis of human loco-motion, when body parts are occluded. Experiments show how the unobserved body locations are predicted and how it can be inferred whether the measurements come from a running or walking sequence.

Perceptive user interface, a generic approach

This paper describes the development of a real-time perceptive user interface. Two cameras are used to detect a users head, eyes, hand, fingers and gestures. These cues are interpreted to control a user interface on a large screen. The result is a fully functional integrated system that processes roughly 7.5 frames per second on a Pentium IV system. The calibration of this setup is carried out through a few simple and intuitive routines, making the system adaptive and accessible to non-expert users. The minimal hardware requirements are two web-cams and a computer. The paper will describe how the user is observed (head, eye, hand and finger detection, gesture recognition), the 3D geometry involved, and the calibration steps necessary to set up the system.

Image segmentation based on statistically principled clustering

A statistically principled approach to 1-dimensional clustering was introduced by Pauwels abd Frederix (2000). In this approach clustering is achieved by finding the smoothest density that is statistically compatible with the observed data. In the current contribution we propose two solutions for the optimisation problem that is at the heart of this algorithm. The first solution is based on spline-functions, while the second hinges on an expansion of the density in terms of Gaussians. The latter is reminiscent of mixture-models but fundamentally different in its interpretation. Finally, we argue that 1-dimensional histogram segmentation yields a powerful local nonparametric cluster-validity criterion that can be used to check the quality of proposed clusterings in higher dimensions.

Maximum Response Filters for Texture Analysis

Current texture analysis focuses either on gathering correlations between image patches and filters, or on explicitly modeling the dependencies between pixels. Both strategies are unable to cope directly with changes in scale, or more general, in viewpoint and illumination. To accommodate to these extra variations, texture segmentation analyzes the texture over multiple scales and classification algorithms include multiple models for a single texture class. We propose a filter-based texture model that allows for a more compact texture representation, independent of viewpoint and illumination. This is achieved by locally optimizing the filter responses through a predefined set of transformations of the filter support. Results are shown for both texture classification and texture segmentation experiments.

Reconstruction of Subjective Surfaces from Occlusion Cues

In the Kanizsa figure, an illusory central area and its contours are perceived. Replacing the pacman inducers with other shapes can significantly influence this effect. Psychophysical studies indicate that the determination of depth is a task that our visual system constantly conducts. We hypothesized that the illusion is due to the modification of the image according to the higher level depth interpretation. This idea was implemented in a feedback model based on a surface completion scheme. The relative depths, with their signs reflecting the polarity of the image, were determined from junctions by convolution of Gaussian derivative based filters, while a diffusion equation reconstructed the surfaces. The feedback loop was established by converting this depth map to modify the lightness of the image. This model created a central surface and extended the contours from the inducers. Results on a variety of figures were consistent with psychophysical experiments.