The detection of significant local reflectional symmetry in grey level images is considered. Prior segmentation is not assumed, and it is intended that the results could be used for guiding visual attention and for providing side information to segmentation algorithms. A local measure of reflectional symmetry that transforms the symmetry detection problem to a global optimization problem is defined. Reflectional symmetry detection becomes equivalent to finding the global maximum of a complicated multimodal function parameterized by the location of the center of the supporting region, its size, and the orientation of the symmetry axis. Unlike previous approaches, time consuming exhaustive search is avoided. A global optimization algorithm for solving the problem is presented. It is related to genetic algorithms and to adaptive random search techniques. The efficiency of the suggested algorithm is experimentally demonstrated. Just one thousand evaluations of the local symmetry measure are typically needed in order to locate the dominant symmetry in natural test images.

With recent improvements in methods for the acquisition and rendering of 3D models, the need for retrieval of models from large repositories of 3D shapes has gained prominence in the graphics and vision communities. A variety of methods have been proposed that enable the efficient querying of model repositories for a desired 3D shape. Many of these methods use a 3D model as a query and attempt to retrieve models from the database that have a similar shape.

Face recognition has primarily focused on recognizing and matching face images against large, controlled databases of frontal views. Many of these techniques perform well against databases that have been collected from a reduced set of viewpoints, under controlled lighting, and are normalized for scale. Acquisition of these databases, however, particularly in unconstrained environments, remains a challenge. We present a real-time technique to automatically acquire a mugshot database from a video surveillance network. Mugshot extraction is a twofold problem. First, faces are detected and tracked in all cameras of the network. Face targets are analyzed to determine which frames represent actual mugshots capable of supporting subsequent matching and recognition. Next, mugshot candidates are evaluated based on their ability to improve the quality of the incrementally constructed database. We introduce a database quality measure, which assigns high value to mugshots of previously unseen subjects or mugshots that do not decrease separability of existing clusters. The quality measure is discounted for mugshots that are redundant or increase the intra-cluster spread. Results demonstrate that automatic acquisition of a high-quality database from a twelve-camera network is feasible. The quality of these databases is demonstrated using traditional methods to accurately match faces against the acquired database.

A feature based retrieval scheme for microscopic images of diatoms in an image database is presented in this report. Diatoms are unicellular algae found in water and other places wherever there is humidity and enough light for photo synthesis. Several methods for feature extraction are described and experimental results on real diatom images are presented. The proposed feature based retrieval scheme is based on symmetry measures, geometric properties, moment invariants, Fourier descriptors and simple textural features. Based on this features the image database is divided into classes using a decision tree based classification approach. We have evaluated the discriminant power of the features and show experimental results on a diatom image database. CR Categories and Subject Descriptors: I.2.1 Artificial Intelligence]: Applications and Expert Systems; I.5.4 [Pattern Recognition]: Applications. General Terms: Algorithms. Additional Key Words: Content based image retrieval, Dia...

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Acquired images often present missing, degraded or occluded parts. Inpainting techniques try to infer lacking information, usually from valid information nearby. This work introduces a new method to complete missing parts from an image using structural information of the image. Since natural and human-made objects present several symmetries, the image structure is described in terms of axial symmetries, and extrapolating the symmetries of the valid parts completes the missing ones. In particular, this allows inferring both the edges and the textures. 1

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