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Optimal search is a major challenge for wrapper-based attribute reduction. Rough sets have been used with much success, but current hill-climbing rough set approaches to attribute reduction are insufficient for finding optimal solutions. In this paper, we propose an innovative use of an intelligent optimisation method, namely the flower search algorithm (FSA), with rough sets for attribute reduction. FSA is a relatively recent computational intelligence algorithm, which is inspired by the pollination process of flowers. For many applications, the attribute space, besides being very large, is also rough with many different local minima which makes it difficult to converge towards an optimal solution. FSA can adaptively search the attribute space for optimal attribute combinations that maximise a given fitness function, with the fitness function used in our work being rough set-based classification. Experimental results on various benchmark datasets from the UCI repository confirm our technique to perform well in comparison with competing methods.

Camera model identification has been attracting a lot of attention lately, as a powerful forensic method. With the promising breakthroughs in the artificial intelligence applications, such systems were revisited to increase the expected accuracy or to solve the still persisting deadlocks. One of the most still-to-be-solved dilemmas is the image manipulations effect on the overall accuracy of the identification systems. A huge degradation in the performance is noticed, when images are post-processed using commonly used methods as compression, scaling and contrast enhancement. Using the state of the art Convolutional Neural Network (CNN) architecture proposed by Bayar et al to estimate the manipulation parameters, and dedicated feature extractor models to estimate the source camera. Multiplexers are used to shift the input image between the dedicated models through the output of the CNNs. Our proposed methods significantly outperform state of the art methods in the literature, especially in case of heavy compression and down sampling. The images used for testing were extracted from 10 different cameras, including different models from the same manufacturer. Different devices were used to investigate the methodology robustness. Moreover, such generic approach could revolutionary change the whole design methodology for camera model identification systems.

Camera model identification has been attracting a lot of attention lately, as a powerful forensic method. With the promising breakthroughs in the artificial intelligence applications, such systems were revisited to increase the expected accuracy or to solve the still persisting deadlocks. One of the most still-to-be-solved dilemmas is the image manipulations effect on the overall accuracy of the identification systems. A huge degradation in the performance is noticed, when images are post-processed using commonly used methods as compression, scaling and contrast enhancement. Using the state of the art Convolutional Neural Network (CNN) architecture proposed by Bayar et al to estimate the manipulation parameters, and dedicated feature extractor models to estimate the source camera. Multiplexers are used to shift the input image between the dedicated models through the output of the CNNs. Our proposed methods significantly outperform state of the art methods in the literature, especially in case of heavy compression and down sampling. The images used for testing were extracted from 10 different cameras, including different models from the same manufacturer. Different devices were used to investigate the methodology robustness. Moreover, such generic approach could revolutionary change the whole design methodology for camera model identification systems.

Feature sets are always dependent, redundant and noisy in almost all application domains. These problems in The data always declined the performance of any given classifier as it make it difficult for the training phase to converge effectively and it affect also the running time for classification at operation and training time. In this work a system for feature selection based on multi-objective gray wolf optimization is proposed. The existing methods for feature selection either depend on the data description; filter-based methods, or depend on the classifier used; wrapper approaches. These two main approaches lakes of good performance and data description in the same system. In this work gray wolf optimization; a swarm-based optimization method, was employed to search the space of features to find optimal feature subset that both achieve data description with minor redundancy and keeps classification performance. At the early stages of optimization gray wolf uses filter-based principles to find a set of solutions with minor redundancy described by mutual information. At later stages of optimization wrapper approach is employed guided by classifier performance to further enhance the obtained solutions towards better classification performance. The proposed method is assessed against different common searching methods such as particle swarm optimization and genetic algorithm and also was assessed against different single objective systems. The proposed system achieves an advance over other searching methods and over the other single objective methods by testing over different UCI data sets and achieve much robustness and stability.

Flower pollination algorithm (FPA) optimization is a new evolutionary computation technique that inspired from the pollination process of flowers. In this paper, a model for multi-objective feature selection based on flower pollination algorithm (FPA) optimization hybrid with rough set is proposed. The proposed model exploits the capabilities of filter-based feature selection and wrapper-based feature selection. Filter-based approach can be described as data oriented methods that not directly related to classification performance. Wrapper-based approach is more related to classification performance but it does not face redundancy and dependency among the selected feature set. Therefore, we proposed a multi-objective fitness function that uses FPA to the find optimal feature subset. The multi-objective fitness function enhances classification performance and guarantees minimum redundancy among selected features. At begin of the optimization process, fitness function uses mutual information among feature as a goal for optimization. While at some later time and using the same population, the fitness function is switched to be more classifier dependent and hence exploits rough-set classifier as a guide to classification performance. The proposed model was tested on eight datasets form UCI data repository and proves advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA).

Multi-Layer Perceptron (MLP) is one of the Feed-Forward Neural Networks (FFNNs) types. Searching for weights and biases in MLP is important to achieve minimum training error. In this paper, Moth-Flame Optimizer (MFO) is used to train Multi-Layer Perceptron (MLP). MFO-MLP is used to search for the weights and biases of the MLP to achieve minimum error and high classification rate. Five standard classification datasets are utilized to evaluate the performance of the proposed method. Moreover, three function-approximation datasets are used to test the performance of the proposed method. The proposed method (i.e. MFO-MLP) is compared with four well-known optimization algorithms, namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Evolution Strategy (ES). The experimental results prove that the MFO algorithm is very competitive, solves the local optima problem, and it achieves a high accuracy.

This paper presents a new feature selection technique based on rough sets and bat algorithm (BA). BA is attractive for feature selection in that bats will discover best feature combinations as they fly within the feature subset space. Compared with GAs, BA does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. A fitness function based on rough-sets is designed as a target for the optimization. The used fitness function incorporates both the classification accuracy and number of selected features and hence balances the classification performance and reduction size. This paper make use of four initialisation strategies for starting the optimization and studies its effect on bat performance. The used initialization reflects forward and backward feature selection and combination of both. Experimentation is carried out using UCI data sets which compares the proposed algorithm with a GA-based and PSO approaches for feature reduction based on rough-set algorithms. The results on different data sets shows that bat algorithm is efficient for rough set-based feature selection. The used rough-set based fitness function ensures better classification result keeping also minor feature size.

This paper introduces anew free-form surface representation scheme for the purpose of fast and accurate registration and matching. Accurate registration of surfaces is a common task in computer vision. The proposed representation scheme captures the surface curvature information (seen from certain points) and produces images, called "surface signatures," at these points. Matching signatures of different surfaces enables the recovery of the transformation parameters between these surfaces. We propose using template matching to compare the signature images. To enable partial matching, another criterion, the overlap ratio is used. This representation scheme can be used as a global representation of the surface as well as a local one and performs near real-time registration. We show that the signature representation can be used to recover scaling transformation as well as matching objects in 3D scenes in the presence of clutter and occlusion. Applications presented include: free-form object matching, multimodal medical volumes registration, and dental teeth reconstruction from intraoral images.

Opti-Select is an Interactive Multi-objective feature analysis and optimization tool for software product lines configuration and feature models optimization based on an innovative UIL (User-In-the-loop) idea. In this tool, the experience of system analysts and stakeholders are merged with optimization techniques and algorithms.Opti-Select interactive tool is an integrated set of techniques providing step by step feature model and attribute configuration, selecting and excluding features, solution set optimization, and user interaction utilities that can all together reach satisfactory set of solutions that fits stakeholder preferences.

This article presents a feature selection and classification system for 2D brain tumors from Magnetic resonance imaging (MRI) images. The proposed feature selection and classification approach consists of four main phases. Firstly, clustering phase that applies the K-means clustering algorithm on 2D brain tumors slices. Secondly, feature extraction phase that extracts the optimum feature subset via using the brightness and circularity ratio. Thirdly, reduct generation phase that uses rough set based on power set tree algorithm to choose the reduct. Finally, classification phase that applies Multilayer Perceptron Neural Network algorithm on the reduct. Experimental results showed that the proposed classification approach achieved a high recognition rate compared to other classifiers including Naive Bayes, AD-tree and BF-tree.

In this paper we present a fuzzy system based hyperspectral classifier for automatic target identification. The system is based on partitioning the spectral band space into clusters using a modified fuzzy C-Means clustering algorithm. Classification of each pixel is then carried out by calculating its fuzzy membership in each cluster. The results showed that the fuzzy hyperspectral classifier is successful in target identification using materials spectrum. Also it provides a fuzzy identification value that can be used later on in the decision-making stage of automatic target recognition (ATR) systems. ” 1999 Elsevier Science B.V. All rights reserved.

Several quality evaluation studies have been performed for video-plus-depth coding systems. In these studies, however, the distortions in the synthesized views have been quantified in experimental setups where both the texture and depth videos are compressed. Nevertheless, there are several factors that affect the quality of the synthesized view. Incorporating more than one source of distortion in the study could be misleading; one source of distortion could mask (or be masked by) the effect of other sources of distortion. In this paper, we conduct a quality evaluation study that aims to assess the distortions introduced by the view synthesis procedure and depth map compression in multiview-video-plus-depth coding systems. We report important findings that many of the existing studies have overlooked, yet are essential to the reliability of quality evaluation. In particular, we show that the view synthesis reference software yields high distortions that mask those due to depth map compression, when the distortion is measured by average luma peak signal-to-noise ratio. In addition, we show what quality metric to use in order to reliably quantify the effect of depth map compression on view synthesis quality. Experimental results that support these findings are provided for both synthetic and real multiview-video-plus-depth sequences.

In multiplexed computational imaging schemes, high-resolution images are reconstructed by fusing the information in multiple low-resolution images detected by a two-dimensional array of low-resolution image sensors. The reconstruction procedure assumes a mathematical model for the imaging process that could have generated the low-resolution observations from an unknown high-resolution image. In practical settings, the parameters of the mathematical imaging model are known only approximately and are typically estimated before the reconstruction procedure takes place. Violations to the assumed model, such as inaccurate knowledge of the field of view of the imagers, erroneous estimation of the model parameters, and/or accidental scene or environmental changes can be detrimental to the reconstruction quality, even if they are small in number. We present an adaptive algorithm for robust reconstruction of high-resolution images in multiplexed computational imaging architectures. Using robust M-estimators and incorporating a similarity measure, the proposed scheme adopts an adaptive estimation strategy that effectively deals with violations to the assumed imaging model. Comparisons with nonadaptive reconstruction techniques demonstrate the superior performance of the proposed algorithm in terms of reconstruction quality and robustness.

This paper introduces a new color image superresolution algorithm in an adaptive, robust M-estimation framework. Using a robust error norm in the objective function, and adapting the estimation process to each of the low-resolution frames, the proposed method effectively suppresses the outliers due to violations of the assumed observation model, and results in color superresolution estimates with crisp details and no color artifacts, without the use of regularization. Experiments on both synthetic and real sequences demonstrate the superior performance over using the L2 and L1 error norms in the objective function.

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 comparative study of bounded-influence M-estimators in the context of multi-frame super-resolution reconstruction. The objectives of this study are to compare these estimators in terms of robustness, and to highlight the associated tradeoff between robustness and edge preservation (crispness) in the presence of registration errors and motion outliers.

In this paper, a new fuzzy logic-based lossy predictive coding system for gray-scale still image compression is developed. The proposed coder employs a recently introduced adaptive fuzzy prediction methodology in the predictor design. In addition, it adopts a novel fuzzy gradient-adaptive quantization scheme. The proposed coding technique possesses superior performance over its non-fuzzy counterparts especially at low bit quantization. This is due to the inherent adaptivity in the fuzzy prediction methodology as well as the gradient-adaptive quantization scheme. Simulation results are provided to demonstrate the efficient performance of the proposed fuzzy predictive coding system.

The paper introduces an adaptive technique for 3D object identification and recognition in 3D scanned scenes. This technique uses neural learning of the 3D free-form surface representation of the object in study. This representation scheme captures the 3D curvature information of any free-form surface and encodes it into a 2D image corresponding to a certain point on the surface. This image represents a "surface signature" because it is unique for this point and is independent of the object translation or orientation in space.

A new adaptive, perceptual image coding system in the framework of fuzzy logic is proposed. The image is decomposed into 8/spl times/8 blocks that are then subjected to the discrete cosine transform (DCT). The image DCT blocks are then classified using the fuzzy c-medians (FCMED) clustering algorithm into four fuzzy classes. Coding bits are then adaptively and perceptually allocated to the DCT coefficients in each class. DCT coefficients are then normalized, optimally quantized, coded, and transmitted, accompanied by their overhead information over a channel. The performance of the proposed system is evaluated using the peak signal-to-noise ratio (PSNR) measure and a just noticeable distortion (JND) based perceptual measure.

In this paper, a new fuzzy logic-based edge detection technique is proposed, in which the drawbacks of the conventional gradient-based techniques are efficiently overcome. Using the relation of the probability partition and the fuzzy 2-partition of the image gradient, the best gradient-threshold is automatically and efficiently selected. The selection algorithm is based on the condition for the entropy to reach a minimum value, since our aim is to find the best compact image representation through edges. The excellent performance of the proposed technique is exercisable through simulation results on a set of test images. It is shown how the extracted, enhanced and purified edges provide an efficient edge-representation of images.

We present a concept for 3D free-form object recognition using a surface representation scheme. This representation scheme captures the 3D curvature information of any free-form surface and encodes it into a 2D image corresponding to a certain point on the surface. This image is unique for this point and is independent from the object translation or orientation in space. For this reason we called this image the surface point signature (SPS). Using specially designed neural networks; the SPS images are used in the matching and recognition of 3D objects in a 3D scanned scene.

This paper presents a framework for integrating multiple sensory data, sparse range data and dense depth maps from shape from shading in order to improve the 3D reconstruction of visible surfaces of 3D objects. The integration process is based on propagating the error difference between the two data sets by fitting a surface to that difference and using it to correct the visible surface obtained from shape from shading. A feedforward neural network is used to fit a surface to the sparse data. We also study the use of the extended Kalman filter for supervised learning and compare it with the backpropagation algorithm. A performance analysis is done to obtain the best neural network architecture and learning algorithm. 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.

Summary form only given. We present the application of a novel algorithm for adaptive fuzzy segmentation of MRI data, estimation of intensity inhomogeneities in MRI volumes, and partial volume estimation using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. The new algorithm also solves the partial volume (PV) effect. Due to the partial volume effect, boundary transitions from one tissue to another can occupy more than one voxel. In most researches this has been treated as an artifact that affect the classification of PV voxels into the right cluster. However, this effect can not be neglected when accurate volumetric measurements are brought to focus. The Modified FCM (MFCM) algorithm is shown to have the ability to overcome the noise sensitivity problem in segmentation without changing the voxel values thus keeping the whole information at the edges for accurate PV estimation.

This paper presents a new method for integrating different low level vision modules, stereo and shape from shading, in order to improve the 3D reconstruction of visible surfaces of objects from intensity images. The integration process is based on correcting the 3D visible surface obtained from shape from shading using the sparse depth measurements from the stereo module by fitting a surface into the difference between the two data sets. A feedforward neural network is used to fit a surface to the error difference. An extended Kalman filter algorithm is used for the network learning. 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.

Presents a fast, six degrees of freedom, registration technique to accurately locate the position and orientation of medical volumes (e.g. CT, MRI) with respect to each other for the same patient. The technique uses surface registration and maximization of mutual information. We have developed a novel technique for surface registration which produces highly accurate results when registering two different volumes of the same individual generated from the same modality, such as preoperative MR and intraoperative MR volumes. In case surface registration is not able to accurately register the different volumes, the result is enhanced by multi-modal volume registration. The gain of this combination is to have an accurate alignment and to reduce time needed for registration. For the multi-modal volume registration, the maximization of mutual information (MI) as a matching criterion is used based on genetic algorithms (GA) as a search engine. Our results demonstrate that our registration technique allows for fast, accurate, robust and completely automatic registration of multimodality medical volumes.

In this paper, we present a novel algorithm for adaptive fuzzy segmentation of MRI data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.

In this paper a new adaptive fuzzy predictive coding system is introduced. The proposed coder employs the adaptive fuzzy prediction methodology developed in [Tian-Hu Yu, 1998]. This results in better prediction of smooth as well as edge regions. In addition, the proposed coder adopts a novel fuzzy gradient-adaptive quantization scheme that switches between three well-designed nonuniform quantizers depending on the local gradient of the pixel to be coded. This, in turn, leads to reduced quantization errors in both smooth and edge regions and consequently higher perceptual quality of reconstructed images is achieved.

This research deals with developing automatic classification algorithms for identifying images of F-actin distribution in endothelial cells with different treatment of agonists. Parametric and non-parametric classification techniques were investigated such as statistical and artificial neural network classifiers. First and second order features were extracted from the images. Among the statistical classification techniques are the Bayes approach and the K-nearest neighbor (K-NN). For the neural network approach, we used the multilayer feedforward and the functional link network. All of these techniques provide adequate results with the neural methods performing with higher accuracy reaching above 97% classification.

A novel integrated system is developed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video camera. A modified Shape from Shading (SFS) technique using perspective projection and camera calibration is then used to extract accurate 3D information from a sequence of 2D images of the jaw. A novel technique for 3D data registration using Grid Closest Point (GCP) transform and genetic algorithms (GA) is used to register the output of the SFS stage. Triangulization is then performed, and a solid 3D model is obtained via a rapid prototype machine. The overall purpose of this research is to develop a model-based vision system for orthodontics that will replace traditional approaches and can be used in diagnosis, treatment planning, surgical simulation and implant purposes.

A novel approach has been developed for fast registration of two sets of 3-D curves or surfaces. The technique is an extension of Besl and Mackay's (1992) iterative closest point (ICP) algorithm. This technique solves the computational complexity associated with the ICP algorithm by applying a novel grid closest point (GCP) transform and a genetic algorithm to minimize the cost function. A detailed description of the algorithm is presented along with a comparison of its performance versus several registration techniques. Two applications are presented in this paper. In the first, the algorithm is used to register 2-D head contours extracted from CT/MRI data to correct for possible mis-alignment caused by motion artifacts during scanning. In the second, the algorithm is used to register 3-D segments of the human jaw obtained using the shape from shading technique. Registration using the GCP/GA technique is found to be significantly faster and of comparable accuracy than two popular techniques in the computer vision and medical imaging literature.

Presents a system for computer assisted measurements of important orthodontic parameters. The system uses a modular approach to extract and recognize objects drawn from a visual world. This approach is implemented on the extraction of teeth from a 3D jaw model. The contribution of this work is part of a computer system that will replace manual methods used currently in orthodontics.

This paper presents a new concept for 3D coding of free-form surfaces. The proposed coding technique uses the surface signature representation scheme. This representation scheme captures the 3D curvature information of many free-form surface and encodes it into a 2D image corresponding to a certain point on the surface. This image is unique for this point and is independent of the the object translation or orientation in space. For this reason this image is called "surface point signature" (SPS). Using SPS in 3D coding has many applications, such as in 3D compression, 3D pose estimation and 3D object recognition.

Intra-operative MRI (iMRI) is a new technology that allows near real time updates of scans during a surgical procedure under the magnet. From an initial comprehensive scan, 3D volumes are obtained. During the surgery, only the portion of the organ under surgery needs to be re-scan (region of interest-ROI) in order to guide the progress of the surgery, and validate the procedure. We describe a registration approach which can be used for iMRI applications. Registration is performed through a hybrid of the surface point signature approach (SPS) and the maximization of the mutual information (MI) criterion. We demonstrate the approach on some real data.

A modified fuzzy Sobel method for edge detection and enhancement is proposed. This method is a modification of the fuzzy Sobel method proposed by Kuo, Lee and Liu see (IEEE Conference on Fuzzy Systems, p.1069-74, 1997). The proposed method overcomes the drawbacks of the conventional gradient methods for edge detection such as Prewitt and Sobel methods. It automatically obtains four threshold values, and apply fuzzy reasoning for edge enhancement. The edges extracted by this method are very clear and provides better representation for image edges and object contours.

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.

This paper presents a model-based vision system for dentistry that will assist in diagnosis, treatment planning, and surgical simulation. Dentistry requires an accurate three-dimensional (3-D) representation of the teeth and jaws for diagnostic and treatment purposes. The proposed integrated computer vision system constructs a 3-D model of the patient's dental occlusion using an intraoral video camera. A modified shape from shading (SFS) technique, using perspective projection and camera calibration, extracts the 3-D information from a sequence of two-dimensional (2-D) images of the jaw. Data fusion of range data and 3-D registration techniques develop the complete jaw model. Triangulation is then performed, and a solid 3-D model is reconstructed. The system performance is investigated using ground truth data, and the results show acceptable reconstruction accuracy.

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.

The paper introduces a new free-form surface representation scheme for the purpose of fast and accurate registration and matching. Accurate registration of surfaces is a common task in computer vision. The proposed representation scheme captures the surface curvature information seen from certain points and produces images called surface signatures at these points. Matching signatures of different surfaces enables the recovery of the transformation parameters between these surfaces. We propose to use template matching to compare the signature images. To enable partial matching, another criterion, the overlap ratio, is used. This representation scheme can be used as a global representation of the surface as well as a local one and performs near real time registration. We show that the signature representation can be used to match objects in 3D scenes in the presence of clutter and occlusion. Applications presented include free-form object matching, multimodal medical volume registration and dental teeth reconstruction from intra-oral images.

In this paper, we present a novel algorithm for adaptive fuzzy segmentation of MRI data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labeling. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.

This paper presents a new concept for 3-D free-form surface registration and object recognition using a new surface representation scheme. This representation scheme captures the 3-D curvature information of any free-form surface and encodes it into a 2-D image corresponding to a certain point on the surface. This image is unique for this point and is independent from the object translation or orientation in space. For this reason we called this image “Surface Point 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. We applied this technique in object registration, multimodal medical image registration and the recognition of multiple objects in a 3-D scene.

This paper presents a model-based vision system for dentistry that will assist in diagnosis, treatment planning and surgical simulation. Dentistry requires the accurate 3-D representation of the teeth and jaws for diagnostic and treatment purposes. The proposed integrated computer vision system reconstructs a 3-D model of the patient’s dental occlusion using an intra-oral video camera. A modified shape from shading (SFS) technique, using perspective projection and camera calibration, extracts the 3-D information from a sequence of 2-D images of the jaw. Data fusion and 3-D registration techniques develop the complete jaw model. Triangulization is then performed, and a solid 3-D model is obtained via rapid prototyping. The system performance is investigated using ground truth data, and the results show sub-millimeter reconstruction accuracy. The system is shown to be robust in terms of speed and accuracy compared to current practices.

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.

Commonly, attributes in data sets are originally correlated, noisy and redundant. Thus, attribute reduction is a challenging task as it substantially affects the overall classification accuracy. In this research, a system for attribute reduction was proposed using correlation-based filter model for attribute reduction. The cuckoo search (CS) optimization algorithm was utilized to search the attribute space with minimum correlation among selected attributes. Then, the initially selected solutions, guaranteed to have minor correlation, are candidates for further improvement towards the classification accuracy fitness function. The performance of the proposed system has been tested via implementing it using various data sets. Also, its performance have has been compared against other common attribute reduction algorithms. Experimental results showed that the proposed multi-objective CS system has outperformed the typical single-objective CS optimizer as well as outperforming both the particle swarm optimization (PSO) and genetic algorithm (GA) optimization algorithms.

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.