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Selective Tuning (ST) presents a framework for modeling attention and in this work we show how it performs in covert visual search tasks by comparing its performance to human performance. Two implementations of ST have been developed. The Object Recognition Model recognizes and attends to simple objects formed by the conjunction of various features and the Motion Model recognizes and attends to motion patterns. The validity of the Object Recognition Model was first tested by successfully duplicating the results of Nagy and Sanchez. A second experiment was aimed at an evaluation of the model's performance against the observed continuum of search slopes for feature-conjunction searches of varying difficulty. The Motion Model was tested against two experiments dealing with searches in the visual motion domain. A simple odd-man-out search for counter-clockwise rotating octagons among identical clockwise rotating octagons produced linear increase in search time with the increase of set size. The second experiment was similar to one described by Thorton and Gilden. The results from both implementations agreed with the psychophysical data from the simulated experiments. We conclude that ST provides a valid explanatory mechanism for human covert visual search performance, an explanation going far beyond the conventional saliency map based explanations.

We describe an object recognition system based on symbolic contour graphs. The image to be analyzed is transformed into a graph with object corners as vertices and connecting contours as edges. Image corners are determined using a robust multiscale corner detector. Edges are constructed by line-following between corners based on evidence from the multiscale Gabor wavelet transform. Model matching is done by finding subgraph isomorphisms in the image graph. The complexity of the algorithm is reduced by labeling vertices and edges, whereby the choice of labels also makes the recognition system invariant under translation, rotation and scaling. We provide experimental evidence and theoretical arguments that the matching complexity is below O(#V³), and show that the system is competitive with other graph-based matching systems.

In this paper, we show the interest of the 3D discrete surface notion for the extraction of object contours. We introduce some notions related to surfaces of 18-and 26-connected objects in 3D discrete images, and a new sequential algorithm to extract the surface and contours of 26-connected objects. Then, we present a PRAM related algorithm to construct the successor function of the surface graph. The complexity of the algorithm is O(log N) for an N×N×N image, with N³ processors.

Good quality terrain models are becoming more and more important, as applications such as runoff modelling are being developed that demand better surface orientation information than is available from traditional interpolation techniques. A consequence is that poor-quality elevation grids must be massaged before they provide useable runoff models. Rather than using direct data acquisition, this project concentrated on using available contour data because, despite modern techniques, contour maps are still the most available form of elevation information. Recent work on the automatic reconstruction of curves from point samples, and the generation of medial axis transforms (skeletons) has greatly helped in expressing the spatial relationships between topographic sets of contours. With these techniques the insertion of skeleton points into a TIN model guarantees the elimination of all "flat triangles" where all three vertices have the same elevation. Additional assumptions about the local uniformity of slopes give us enough information to assign elevation values to these skeleton points. In addition, various interpolation techniques were compared using the enriched contour data. Examination of the quality and consistency of the resulting maps indicates the required properties of the interpolation method in order to produce terrain models with valid slopes. The result provides us with a surprisingly realistic model of the surface - that is, one that conforms well to our subjective interpretation of what a real landscape should look like.

The problem of reconstructing a three-dimensional object from parallel slices has application in computer vision and medicine. Here we explore a specific existence question: given two polygons in parallel planes, is it always possible to find a polyhedron that has those polygons as faces, and whose vertices are precisely the vertices of the two polygons? We answer this question in the negative by providing an example of two polygons that cannot be connected to form a simple polyhedron. One polygon is a triangle, the other a somewhat complicated shape with spiraling pockets.

This paper presents a new method for reconstructing piecewise linear surfaces from planar polygonal contours that branch. For non-branching contours, experience has shown that the piecewise linear surface of minimum surface area which connects a pair of contours often provides a good solution. The current algorithm extends this idea by searching for the surface of minimal area which connects two contours comprised of more than one polygon. Several examples that justify this heuristic are provided.

A topographic sheet hosts various morphological features that effectively describe the terrain. This multi-faced information content not only elevates human perception but also provides ample direction for research initiatives. Out of all possible attributes based on utility, contours have wide set of application. A contour is characterized by its coordinate system and most importantly, its elevation detail. Upon, successful attainment of these two attributes, creating a fully automatic 3D projection system may be achieved with relative ease. In contrast to the traditional manual approach, this research initiative puts forward a novel mechanism for automatically localizing contour and its attributes including coordinate pattern and elevation value in a referenced map. To accomplish the aforementioned objectives, the proposed mechanism relies on various image processing techniques based on morphological operations. Further, the extracted details can be used to project the contours in a 3D space. This projection is also called Digital Elevation Model (DEM). DEM is crucial for various applications such as Terrain Modeling, Hydrological Modeling, Path Optimization, to name a few. Automatically and accurately created DEM from topographic sheet could contribute a lot in many Geographical Information System (GIS) applications. This paper focuses mainly on elevation value localization associated with specific contour.

The modification of a standard character according to the features of an individual's handwriting endows the character with handwritten traits, a process called font personalization. In this paper, a novel approach is proposed to transform a full set of standard Chinese vectorized font, according to a writer's handwritten characteristics, into a personalized font possessing the writer's discernible features. Once installed in a computer, the personalized font set obtained can efficiently replace existing standard fonts to exhibit unique handwritten features.

In this paper, we show the interest of the 3D discrete surface notion for the extraction of object contours. We introduce some notions related to surfaces of 18-and 26-connected objects in 3D discrete images, and a new sequential algorithm to extract the surface and contours of 26-connected objects. Then, we present a PRAM related algorithm to construct the successor function of the surface graph. The complexity of the algorithm is O(log N) for an N × N × N image, with N³ processors.