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Video encoding algorithms require processing of data arranged in blocks of pixels. For efficient computation, pixel blocks are expected to be stored contiguously in memory, and within each block, pixels are to be arranged in a raster scan (left to right, top to bottom order). Since data captured from the video port is linearly arranged in memory (one line after the other), it is necessary to arrange the data in the two-dimensional form before processing for encoding. A common approach to achieve the two-dimensional arrangement is through optimized functions (in C or Assembly) to arrange the captured data, which is stored in an intermediate buffer, into an input buffer from which it is ready for encoding. However, this approach has two main drawbacks. First, a portion of the CPU MHZ budget is consumed only on data arrangement. Second, an intermediate data buffer is required to hold the data before the arrangement into the input buffer takes place, and hence increasing the memory requirements. In this paper, a memory and MHZ efficient EDMA transfer scheme is introduced for simultaneous data transfer and two-dimensional arrangement from the video port to the DSP memory. The proposed scheme is described in details for TI TMS320DM642TM.© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper introduces a new image super-resolution algorithm in an adaptive, robust M-estimation framework. Super-resolution reconstruction is formulated as an optimization (minimization) problem whose objective function is based on a robust error norm. The effectiveness of the proposed scheme lies in the selection of a specific class of robust M-estimators, redescending M-estimators , and the incorporation of a similarity measure to adapt the estimation process to each of the low-resolution frames. Such a choice helps in dealing with violations to the assumed imaging model that could have generated the low-resolution frames from the unknown high-resolution one. The proposed approach effectively suppresses the outliers without the use of regularization in the objective function, and results in high-resolution images with crisp details and no artifacts. Experiments on both synthetic and real sequences demonstrate the superior performance over methods based on the L 2 and L 1 in the objective function.

This paper introduces a new transform, called the signature transform, to concisely represent fee-from 3D objects. The signature transform is based on a free-form surface representation called the surface signature. The surface signature captures some information about a 3D surface, as viewed from a special point called the anchor, such as the curvature, distance from anchor point,....etc. The surface signature stores this information in the form of a 2D image called the surface signature image. The signature transform uses different variations of the surface signature as viewed from selected landmark points. The selection of anchor points is crucial to the success of the signature transform an approach for selecting landmark points based on curvature value will be presented. The signature transform can then be used as a form of a progressive compression of objects that will allow the view and manipulation of the 3D object even if all the compression data are not received. Unlike the previously existing progressive compression techniques, the signature transform does not require receiving the data in special order nor does it have key frames in the representation.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Latin and Chinese OCR systems have been studied extensively in the literature. Yet little work was performed for Arabic character recognition. This is due to the technical challenges found in the Arabic text. Due to its cursive nature, a powerful and stable text segmentation is needed. Also; features capturing the characteristics of the rich Arabic character representation are needed to build the Arabic OCR. In this paper a novel segmentation technique which is font and size independent is introduced. This technique can segment the cursive written text line even if the line suffers from small skewness. The technique is not sensitive to the location of the centerline of the text line and can segment different font sizes and type (for different character sets) occurring on the same line. Features extraction is considered one of the most important phases of the text reading system. Ideally, the features extracted from a character image should capture the essential characteristics of this character that are independent of the font type and size. In such ideal case, the classifier stores a single prototype per character. However, it is practically challenging to find such ideal set of features. In this paper, a set of features that reflect the topological aspects of Arabia characters is proposed. These proposed features integrated with a topological matching technique introduce an Arabic text reading system that is semi Omni.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper presents a new concept for 3-D free-form surface registration and object recognition using a new surfacerepresentation scheme. This representation scheme captures the 3-D curvature information of any free-form surfaceand encodes it into a 2-D image corresponding to a certain point on the surface. This image is unique for this pointand is independent from the object translation or orientation in space. For this reason we called this image "SurfacePoint Signature" (SPS) . This scheme can be used as a global representation of the surface as well as a local one and also in a scale independent surface matching. It performs faster registration than existing registration approaches. 1. INTRODUCTION The registration process is an integral part of computer and robot vision systems and still presents a topic of highinterest in both fields. The importance of the registration problem in general comes from the fact that it is found indifferent applications including surface matching,1 3-D medical imaging,2 pose estimation,3 object recognition46and data fusion.7'8

This paper presents a model-based vision system for dentistry that will replace traditional approaches used in diagnosis, treatment planning and surgical simulation. Dentistry requires accurate 3D representation of the teeth and jaws for many diagnostic and treatment purposes. For example orthodontic treatment involves the application of force systems to teeth over time to correct malocclusion. In order to evaluate tooth movement progress, the orthodontists monitors this movement by means of visual inspection, intraoral measurements, fabrication of plastic models, photographs and radiographs, a process which is both costly and time consuming. In this paper an integrate system has been developed to record the patient's occlusion using computer vision. Data is acquired with an intraoral video camera. A modified shape from shading (SFS) technique, using perspective projection and camera calibration, is used to extract accurate 3D information from a sequence of 2D images of the jaw. A new technique for 3D data registration, using a Grid Closest Point transform and genetic algorithms, is used to register the SFS output. Triangulization is then performed, and a solid 3D model is obtained via a rapid prototype machine.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Orthodontics is the branch of dentistry concerned with the study of growth of the craniofacial complex. The detection and correction of malocclusion and other dental abnormalities is one of the most important and critical phases of orthodontic diagnosis. This paper introduces a system that can assist in automatic orthodontics diagnosis. The system can be used to classify skeletal and dental malocclusion from a limited number of measurements. This system is not intended to deal with several cases but is aimed at cases more likely to be encountered in epidemiological studies. Prior to the measurement of the orthodontics parameters, the position of the teeth in the jaw model must be detected. A new free-form surface representation is adopted for the efficient and accurate segmentation and separation of teeth from a scanned jaw model. THe new representation encodes the curvature and surface normal information into a 2D image. Image segmentation tools are then sued to extract structures of high/low curvature. By iteratively removing these structures, individual teeth surfaces are obtained.

Recently multisensor data fusion has proven its necessity for computer vision and robotics applications. 3D scene reconstruction and model building have been greatly improved in systems that employ multiple sensors and/or multiple cues data fusion/integration. In this paper, we present a framework for integrating registered multiple sensory data, sparse range data from laser range finders and dense depth maps of shape from shading from intensity images, for improving the 3D reconstruction of visible surfaces of 3D objects. Two methods are used for data integration and surface reconstruction. In the first method, data are integrated using a local error propagation algorithm, which we have developed in this paper. In the second method, the integration process is carried out using a feedforward neural networks with backpropagation learning rule. It is found that the integration of sparse depth measurements has greatly enhanced the 3D visible surface obtained from shape from shading in terms of metric measurements. We also review the current research in the area of multisensor/multicue data fusion for 3D object reconstructions.

A novel approach is proposed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video cameras. The technique utilizes shape from shading to extract 3D information from 2D views of the jaw, and a novel technique for 3D data registration using genetic algorithms. The resulting 3D model can be used for diagnosis, treatment planning, and implant purposes. The overall purpose of this research is to develop a model-based vision system for orthodontics to replace traditional approaches. This system will be flexible, accurate, and will reduce the cost of orthodontic treatments.