R. van den Boomgaard

Color invariance

This paper presents the measurement of colored object reflectance, under different, general assumptions regarding the imaging conditions. We exploit the Gaussian scale-space paradigm for color images to define a framework for the robust measurement of object reflectance from color images. Object reflectance is derived from a physical reflectance model based on the Kubelka-Munk theory for colorant layers. Illumination and geometrical invariant properties are derived from the reflectance model. Invariance and discriminative power of the color invariants is experimentally investigated, showing the invariants to be successful in discounting shadow, illumination, highlights, and noise. Extensive experiments show the different invariants to be highly discriminative, while maintaining invariance properties. The presented framework for color measurement is well-founded in the physics of color as well as in measurement science. Hence, the proposed invariants are considered more adequate for the measurement of invariant color features than existing methods.

Watersnakes: energy-driven watershed segmentation

The watershed algorithm from mathematical morphology is powerful for segmentation. However, it does not allow incorporation of a priori information as segmentation methods that are based on energy minimization. In particular, there is no control of the smoothness of the segmentation result. In this paper, we show how to represent watershed segmentation as an energy minimization problem using the distance-based definition of the watershed line. A priori considerations about smoothness can then be imposed by adding the contour length to the energy function. This leads to a new segmentation method called watersnakes, integrating the strengths of watershed segmentation and energy based segmentation. Experimental results show that, when the original watershed segmentation has noisy boundaries or wrong limbs attached to the object of interest, the proposed method overcomes those drawbacks and yields a better segmentation.

Occlusion robust adaptive template tracking

We propose a new method for tracking rigid objects in image sequences using template matching. A Kalman filter is used to make the template adapt to changes in object orientation or illumination. This approach is novel since the Kalman filter has been used in tracking mainly for smoothing the object trajectory. The performance of the Kalman filter is further improved by employing a robust and adaptive filtering algorithm. Special attention is paid to occlusion handling.

Local mode filtering

Linear filters have two major drawbacks. First, edges in the image are smoothed with increasing filter size. Second, by extending the filters to multi-channel data, correlation between the channels is lost. Only a few researchers have explored the possibilities of mode filtering to overcome these problems. Mode filtering is motivated from both a local histogram with tonal scale and a robust statistics point of view. The tonal scale is proved to be equal to the scale of the error norm function within the robust statistics framework. Instead of the more commonly studied global mode, our focus is on the local mode. It preserves edges and details and is easily extensible to multi-channel data. A generalization of the spatial Gaussian filtering to a spatial and tonal Gaussian filter is used to iterate to the local mode. Results on color images include successful noise attenuation while preserving edges and detail by local mode filtering.Linear filters have two major drawbacks. First, edges in the image are smoothed with increasing filter size. Second, by extending the filters to multi-channel data, correlation between the channels is lost. Only a few researchers have explored the possibilities of mode filtering to overcome these problems. Mode filtering is motivated from both a local histogram with tonal scale and a robust statistics point of view. The tonal scale is proved to be equal to the scale of the error norm function within the robust statistics framework. Instead of the more commonly studied global mode, our focus is on the local mode. It preserves edges and details and is easily extensible to multi-channel data. A generalization of the spatial Gaussian filtering to a spatial and tonal Gaussian filter is used to iterate to the local mode. Results on color images include successful noise attenuation while preserving edges and detail by local mode filtering.

Tracking nonparameterized object contours in video

We propose a new method for contour tracking in video. The inverted distance transform of the edge map is used as an edge indicator function for contour detection. Using the concept of topographical distance, the watershed segmentation can be formulated as a minimization. This new viewpoint gives a way to combine the results of the watershed algorithm on different surfaces. In particular, our algorithm determines the contour as a combination of the current edge map and the contour, predicted from the tracking result in the previous frame. We also show that the problem of background clutter can be relaxed by taking the object motion into account. The compensation with object motion allows to detect and remove spurious edges in background. The experimental results confirm the expected advantages of the proposed method over the existing approaches.

On the equivalence of local-mode finding, robust estimation and mean-shift analysis as used in early vision tasks

In this paper we show the equivalence of three techniques used in image processing: local-mode finding, robust-estimation and mean-shift analysis. The computational common element in all these image operators is the spatial-tonal normalized convolution, an image operator that generalizes the bilateral filter.In this paper we show the equivalence of three techniques used in image processing: local-mode finding, robust-estimation and mean-shift analysis. The computational common element in all these image operators is the spatial-tonal normalized convolution, an image operator that generalizes the bilateral filter.

Hyperdocument generation using OCR and icon detection

In this contribution we consider the construction of hyperdocuments; converting scanned paper documents into electronic hypertext. Hyperlink creation is automated by analyzing the structure and content of the scanned document. The focus is on hyperlinks between the text and labels in a picture. A number of tools for such hyperlink detection are described. Practical results are presented.

Dense motion estimation using regularization constraints on local parametric models

This paper presents a method for dense optical flow estimation in which the motion field within patches that result from an initial intensity segmentation is parametrized with models of different order. We propose a novel formulation which introduces regularization constraints between the model parameters of neighboring patches. In this way, we provide the additional constraints for very small patches and for patches whose intensity variation cannot sufficiently constrain the estimation of their motion parameters. In order to preserve motion discontinuities, we use robust functions as a regularization mean. We adopt a three-frame approach and control the balance between the backward and forward constraints by a real-valued direction field on which regularization constraints are applied. An iterative deterministic relaxation method is employed in order to solve the corresponding optimization problem. Experimental results show that the proposed method deals successfully with motions large in magnitude, motion discontinuities, and produces accurate piecewise-smooth motion fields.

The morphological structure of images

The authors investigate the use of mathematical morphology to construct scale-spaces. These scale-spaces are based on differential equations, which are solved by morphological operators, describing the evolution of images in scale-space.<<ETX>>

A Morphological Multi-Scale Gradient for Color Image Segmentation

Segmentation and edge detection are key points in image analysis. Mathematical morphology employs the watershed transform to obtain the edges of the objects in an image. Usually, the watershed is significantly influenced by the morphological gradient. Furthermore, the direct segmentation of the gradient by the watershed transform results in an extreme oversegmentation. In this paper, we propose a morphological approach to compute the multiscale gradient applied to color images. The main property of this technique, established on color morphology, is that it does not split the color channels in contrast to other methods in the literature. The experiments have shown that the suggested technique enhances the segmentation results generating more precise watershed lines.