Maria Petrou

Structural matching in computer vision using probabilistic relaxation

In this paper, we develop the theory of probabilistic relaxation for matching features extracted from 2D images, derive as limiting cases the various heuristic formulae used by researchers in matching problems, and state the conditions under which they apply, We successfully apply our theory to the problem of matching and recognizing aerial road network images based on road network models and to the problem of edge matching in a stereo pair. For this purpose, each line network is represented by an attributed relational graph where each node is a straight line segment characterized by certain attributes and related with every other node via a set of binary relations. >

Image Processing: Dealing with Texture

DESCRIPTION Self-contained text covering practical image processing methods and theory for image texture analysis. Techniques for the analysis of texture in digital images are essential to a range of applications in areas as diverse as robotics, defence, medicine and the geo-sciences. In biological vision, texture is an important cue allowing humans to discriminate objects. This is because the brain is able to decipher important variations in data at scales smaller than those of the viewed objects. In order to deal with texture in digital data, many techniques have been developed by image processing researchers.

Automatic watershed segmentation of randomly textured color images

A new method is proposed for processing randomly textured color images. The method is based on a bottom-up segmentation algorithm that takes into consideration both color and texture properties of the image. An LUV gradient is introduced, which provides both a color similarity measure and a basis for applying the watershed transform. The patches of watershed mosaic are merged according to their color contrast until a termination criterion is met. This criterion is based on the topology of the typical processed image. The resulting algorithm does not require any additional information, be it various thresholds, marker extraction rules, and suchlike, thus being suitable for automatic processing of color images. The algorithm is demonstrated within the framework of the problem of automatic granite inspection. The segmentation procedure has been found to be very robust, producing good results not only on granite images, but on the wide range of other noisy color images as well, subject to the termination criterion.

The Trace transform and its applications

The Trace transform proposed, a generalization of the Radon transform, consists of tracing an image with straight lines along which certain functionals of the image function are calculated. Different functionals that can be used may be invariant to different transformations of the image. The paper presents the properties the functionals must have in order to be useful in three different applications of the method: construction of invariant features to rotation, translation and scaling of the image, construction of sensitive features to the parameters of rotation, translation and scaling of the image, and construction of features that may correlate well with a certain phenomenon we wish to monitor.

The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows

We present an algorithm for separating the local gradient information and Lambertian color by using 4-source color photometric stereo in the presence of highlights and shadows. We assume that the surface reflectance can be approximated by the sum of a Lambertian and a specular component. The conventional photometric method is generalized for color images. Shadows and highlights in the input images are detected using either spectral or directional cues and excluded from the recovery process, thus giving more reliable estimates of local surface parameters.

Segmentation of color textures

This paper describes an approach to perceptual segmentation of color image textures. A multiscale representation of the texture image, generated by a multiband smoothing algorithm based on human psychophysical measurements of color appearance is used as the input. Initial segmentation is achieved by applying a clustering algorithm to the image at the coarsest level of smoothing. The segmented clusters are then restructured in order to isolate core clusters, i.e., patches in which the pixels are definitely associated with the same region. The image pixels representing the core clusters are used to form 3D color histograms which are then used for probabilistic assignment of all other pixels to the core clusters to form larger clusters and categorise the rest of the image. The process of setting up color histograms and probabilistic reassignment of the pixels to the clusters is then propagated through finer levels of smoothing until a full segmentation is achieved at the highest level of resolution.

Optimal edge detectors for ramp edges

It is argued that the best way to model an edge is by assuming all ideal mathematical function passed through a low-pass filter and and immersed in noise. Using techniques similar to those developed by J. Canny (1983, 1986) and L.A. Spacek (1986), optimal filters are derived for ramp edges of various slopes. The optimal nonrecursive filter for ideal step edges is then derived as a limiting case of the filters for ramp edges. Because there are no step edges in images, edge detection is improved when the ramp filter is used instead of the filters developed for step edges. For practical purposes, some convolution masks are given which can be used directly for edge detection without the need to go into the details of the subject. >

Affine invariant features from the trace transform

The trace transform is a generalization of the Radon transform that allows one to construct image features that are invariant to a chosen group of image transformations. In this paper, we propose a methodology and appropriate functionals that can be computed from the image function and which can be used to calculate features invariant to the group of affine transforms. We demonstrate the usefulness of the constructed image descriptors in retrieving images from an image database and compare it with relevant state-of-the-art object retrieval methods.

Application of a Bayesian network in a GIS based decision making system

In this paper we show how a Bayesian network of inference can be used with a GIS to combine information from different sources of data for classification. Data may include satellite sensor images, topographic maps, geological maps etc, each one with its own resolution and accuracy. We show how this uncertainty in the input data can be incorporated in the network and present various methods by which the conditional probability matrices used by the network can be constructed. We demonstrate our approach within the framework of the problem of assessing the risk of desertification of some burned forests in the Mediterranean region.

On the choice of the parameters for anisotropic diffusion in image processing

Anisotropic diffusion filtering is highly dependent on some crucial parameters, such as the conductance function, the gradient threshold parameter and the stopping time of the iterative process. The various alternative options at each stage of the algorithm are examined and evaluated and the best choice is selected. An automatic stopping criterion is proposed, that takes into consideration the quality of the preserved edges as opposed to just the level of smoothing achieved. The proposed scheme is evaluated with the help of real and simulated images, and compared with other state of the art schemes using objective criteria.