For reliable human-robot interaction, the robot must know the person's action in order to plan the appropriate way to interact or assist the person. As part of the pre-processing stage of action recognition, the robot also needs to recognize the various body parts and posture of the person. But estimation of posture and body parts is challenging due to the articulated nature of the human body and the huge intra-class variations. To address this challenge, we propose two schemes using Hierarchical-ELM (H-ELM) for posture detection into either upright or non-upright posture. In the first scheme, we follow a whole body detector approach, where a H-ELM classifier is trained on several whole body postures. In the second scheme, we follow a body part detection approach, where separate H-ELM classifiers are detected for each body part. Using the detected body parts a final decision is made on the posture of the person. We have conducted several experiments to compare the performance of both approaches under different scenarios like view angle changes, occlusion etc. Our experimental results show that body part H-ELM based posture detection works better than other proposed framework even in the presence of occlusion.

Body part segmentation and detection in videos is a useful analysis for many computer vision tasks such as action recognition and video search. Conventional methods mainly focus on body part detection assuming upright posture of the human body. Recently, a body part detection framework was proposed to include non-upright postures. This method consists of 2 parts, initial segmentation and computation of body part likelihood score for each segment. In this paper, we propose improvements to this approach. Firstly, we propose a novel motion based body part segmentation using kinematic features to identify segments which undergo similar motion in the video based on a consistency or error measure. Secondly, we replace the Extreme Learning Machine classifier in the original work with deep learning to investigate it's performance. For accurate detection, deep learning requires a lot of training data and it has so far been used only in high resolution images. Here we apply deep learning for body part detection in low resolution cases. We conduct experiments to study and analyse the effect of the improvements proposed.

Multi-people tracking has been studied for decades. It is already applied in many computer vision tasks. In this paper, the proposed framework performs frame to frame tracking and follows tracking-by-detection approach. Saliency detection is introduced to enhance multi-people tracking. It is performed on two layers in this method. Salient parts inside the human patch denote representative regions of the target, while parts around the target capture context information. Short term tracking of salient parts is applied along with data association. When data association with detections fails, the supporting models on-line learned are used to indicate the locations of targets based on the tracking results of salient parts. A Bayesian based method is used for mutual occlusion reasoning. Experiments are carried out on several public datasets to evaluate the proposed method. The experimental results show the promising performance of the proposed method compared with state-of-the-art works.

Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications, such as human-computer interaction, automated tutoring systems, image and video retrieval, smart environments, and driver warning systems. Traditionally, facial emotion recognition systems have been evaluated on laboratory controlled data, which is not representative of the environment faced in real-world applications. To robustly recognize the facial emotions in real-world natural situations, this paper proposes an approach called extreme sparse learning, which has the ability to jointly learn a dictionary (set of basis) and a nonlinear classification model. The proposed approach combines the discriminative power of extreme learning machine with the reconstruction property of sparse representation to enable accurate classification when presented with noisy signals and imperfect data recorded in natural settings. In addition, this paper presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able to achieve the state-of-the-art recognition accuracy on both acted and spontaneous facial emotion databases.

In the past 5 years, people re-identification has been a popular topic as an application using computer vision techniques. Among the models used for people re-identification, soft-biometric traits based models have great potential due to the semantic meaning and robust performance they have. In this paper, we will exploit the performance of supervised learning based method on the detection of three soft-biometric traits: Glasses, Cap and Clothes Pattern. Simple features like edge and frequency are extracted from sample images and used for learning. Two supervised learning methods — Support Vector Machine (SVM) and Extreme Learning Machine (ELM) are employed and compared. Different normalization methods are compared as well. Experiments are carried out on images from FERET dataset and images collected online, and discussion is provided.

This paper presents a new approach for human hair length detection. In contrast to others, the proposed method is able to segment hairs from different views of human heads even with low resolution. Faces are not necessary to be visible in the images, as no face detection is needed in our method. Firstly, it conducts background subtraction to detect foreground objects and then detects human heads with a trained human head detector. Next, histogram analysis is carried out on the detected head region to segment hair region with K-mean clustering. Finally, hair length is determined by performing line scanning on the segmented hair region. The main advantages of the proposed method are able to handle the cases of (1) view variation and (2) low resolution or small human body appearance in the image. It is specially designed for surveillance system. The detected human hair length can be used as an invariant feature to be embedded into a human appearance model which can be employed for human detection, indexing and searching in multi cameras network system. This method aims at the fast processing speed and is able to achieve 7ms for hair length identification in a head patch detected. The experiments show the improvements in terms of speed and accuracy.

Given a video sequence, the task of action recognition is to identify the most similar action among the action sequences learned by the system. Such human action recognition is based on evidence gathered from videos. It has wide application including surveillance, video indexing, biometrics, telehealth, and human-computer interaction. Vision-based human action recognition is affected by several challenges due to view changes, occlusion, variation in execution rate, anthropometry, camera motion, and background clutter. In this survey, we provide an overview of the existing methods based on their ability to handle these challenges as well as how these methods can be generalized and their ability to detect abnormal actions. Such systematic classification will help researchers to identify the suitable methods available to address each of the challenges faced and their limitations. In addition, we also identify the publicly available datasets and the challenges posed by them. From this survey, we draw conclusions regarding how well a challenge has been solved, and we identify potential research areas that require further work.

Occlusion is one of the major problems that object tracking faces in a clustered environment. In this paper, a tracking method which can deal with partial occlusion is proposed. There are two novelties in this paper: (1) using SURF keypoints to represent the object, key-points are evaluated and online learned by Random Ferns. (2) Bi-model is proposed to store key-points from object and surrounding background. In each frame, key-points inside or around the object bounding box will be assigned labels by matching with points stored in the Bi-model. These labeled points will be further used for improving the tracking accuracy and learning of Random Ferns. Long-term tracking is achieved by combining detection and tracking together. Experiments on videos with occlusion conditions show that the proposed method has good performance on tracking partial occluded objects, compared to some of the state-of-art methods.

Most face recognition systems require faces to be detected and localized a priori. In this paper, an approach to simultaneously detect and localize multiple faces having arbitrary views and different scales is proposed. The main contribution of this paper is the introduction of a face constellation, which enables multiview face detection and localization. In contrast to other multiview approaches that require many manually labeled images for training, the proposed face constellation requires only a single reference image of a face containing two manually indicated reference points for initialization. Subsequent training face images from arbitrary views are automatically added to the constellation (registered to the reference image) based on finding the correspondences between distinctive local features. Thus, the key advantage of the proposed scheme is the minimal manual intervention required to train the face constellation. We also propose an approach to identify distinctive correspondence points between pairs of face images in the presence of a large amount of false matches. To detect and localize multiple faces with arbitrary views, we then propose a probabilistic classifier-based formulation to evaluate whether a local feature cluster corresponds to a face. Experimental results conducted on the FERET, CMU, and FDDB datasets show that our proposed approach has better performance compared to the state-of-the-art approaches for detecting faces with arbitrary pose.

Although facial expression plays an important role in human interaction, automated facial expression analysis is still a challenging task. This paper presents a novel facial descriptor based on Phase Congruency (PC) and Local Binary Pattern (LBP) for facial expression recognition. The proposed descriptor, named Binary Pattern of Phase Congruency (BPPC), is an oriented and multi-scale local descriptor that is able to encode various patterns of face images. It is constructed by applying LBP on the oriented PC images. We evaluated the proposed method using the Cohn-Kanade (CK+) database. In our experiment, we achieved an overall detection rate of 93.83% for the six basic emotions. This shows the effectiveness of the proposed method.

The Local Binary pattern (LBP) is a well-known feature and has been widely used for human identification. However, the amount of information extracted is limited which reduces the LBP discriminative power. Recently, some enhancements have been proposed by adding preprocessing stages or considering more neighbor pixels to enrich the extracted feature. In this paper, we propose Uniformly-sampled Thresholds for LBP (UTLBP) operator that increases the richness of information derived from the LBP feature. It outperforms other features in various probe sets of the large CAS-PEAL database for face recognition. Moreover, we collected a database of 25 families to verify the superiority of the proposed feature in the family verification. Results show that using the UTLBP, the total error in face recognition and family verification is reduced up to 8% and 3% respectively comparing to the state of the art LBP. It improves the missing family member verification performance up to 3% where, contrary to expectation, increasing the LBP operator radius worsens the performance by 2%.

According to the level of information provided in images, segmentation techniques can be categorized into two groups. One is region-labeling, which obeys the intensity-based classification methods. Although modeling the tissue intensity is straightforward by applying local statistical methods and spatial dependencies, the results might suffer from noise and incomplete data. The second group of techniques applies active contour models, in which the objective is to find the optimal partition of the image domain using a closed or open curve by using prior constraints on the shape variation. However, estimating optimal curve is intractable due to the incomplete observation data. This paper extends a previously reported joint active contour model for medical image segmentation in a new Expectation-Maximization (EM) framework, wherein the evolution curve is constrained not only by a shape-based statistical model but also by applying a hidden variable model from the image observation. In this approach, the hidden variable model is defined by the local voxel labeling computed from its likelihood function, depended on the image functions and the prior anatomical knowledge. Comparative results on segmenting putamen and caudate shapes in MR brain images confirmed both robustness and accuracy of the proposed curve evolution algorithm.

Gabor Wavelets are widely used to extract facial features since they are robust against illumination and pose changes. Due to the limitation in computational power, the common practice is to down-sample the face image to reduce number of Gabor features generated. As not all of the generated Gabor features are necessary, the main objective of this paper is to develop an efficient removal scheme of redundant filters in order to employ images with large dimension in face data processing. In particular, Genetic Algorithm is used to provide a computational and fast selection of feature ensemble. The base classifiers are trained by the AdaBoost algorithm with the Gabor feature set extracted from each single Gabor filter. By employing the joint diversity, Genetic Algorithm is then applied to select the most discriminate ensemble of classifiers followed by the optimum decision making rule on the classifiers outputs. The proposed approach is implemented in the family classification problem which has large intra-group variation. This method also allows us to select more discriminate filters from higher scales and finer orientations for those families with very young children to improve the performance with the same complexity and calculation load of the conventional Gabor wavelet set.

In this paper, a new coupled implicit shape-based segmentation algorithm is proposed for medical image segmentation. In the method, both region-based and statistical model-based curve evolution algorithms are jointly used to match the object in a new input image. Compared to the previous method that solely uses statistical shape models, our new algorithm is able to match the boundaries of the object shapes more accurately and at the same time, it maintains similar robustness since the same shape prior information is used to regularize the object shapes. Experiments on segmenting the ventricle frontal horn and putamen shapes in MR brain images confirm that the proposed algorithm yields more accurate segmentation results.

Fingerprint recognition is the most popular biometrics but the current fingerprint authentication system can still be easily circumvented by fake fingers. In this paper, we propose a novel method to detect fake finger using tactile sensation. It relies on an additional ability of a live finger, the sense of touch. Such tactile perception capability is absent in the dead or a totally fake finger. Based on this concept, an electrotactile display system capable of presenting tactile pattern is developed. This is then correlated with the pattern shown visually to the user. The system does not produce any visible, auditory or other clues and as such, its strength is only dependent on the number of visible patterns available for the user to select. Preliminary result obtained from the prototype system shows that the proposed approach is indeed able to detect gelatin fake finger worn over live fingers, even when the gelatin is only 1mm thick.

A new deformable model called active b-snake model (ABM) is presented for object boundary extraction. First, an affine-invariant landmark point assignment strategy is proposed to avoid manually assign landmarks. Second, an adaptive control point insertion algorithm is used to enhance the flexibility of B-Snake to describe complex shape. Third, for modeling the shape distribution and appearance characteristics of landmark points in the training samples, a statistical framework is embedded into ABM. Finally, the minimum mean square error (MMSE) approach with control point insertion provides fine deformation to the desired object boundaries. Experimental results show that ABM can be used to achieve more accurate object extraction.

This paper presents a new affine-invariant matching algorithm based on B-Spline modeling, which solves the problem of the non-uniqueness of B-Spline in curve matching. This method first smoothes the B-Spline curve by increasing the degree of the curve. It is followed by a reduction of the curve degree using the Least Square Error (LSE) approach to construct the Curvature Scale Space (CSS) image. CSS matching is then carried out. Our method combines the advantages of B-Spline that are continuous curve representation and the robustness of CSS matching with respect to noise and affine transformation. It avoids the need for other matching algorithms that have to use the re-sampled points on the curve. Thus, the curve matching error is reduced. The proposed algorithm has been tested by matching similar shapes from a prototype database. The experimental results showed the robustness and accuracy of the proposed method in B-Spline curve matching.

Fisher linear discriminant (FLD) is a popular method for feature extraction in face recognition. However, It often suffers from the small sample size, bias and overfitting problems when dealing with the high dimensional face image data. In this paper, a framework of ensemble learning for diversified Fisher linear discriminant (EnL - DFLD) is proposed to improve the current FLD based face recognition algorithms. Firstly, the classifier ensemble in EnL - DFLD is composed of a set of diversified component FLD classifiers, which are selected intentionally by computing the diversity between the candidate component classifiers. Secondly, the candidate component classifiers are constructed by coupling the random subspace and adaboost methods, and it can also be shown that such a coupling scheme will result in more suitable component classifiers so as to increase the generalization performance of EnL - DFLD. Experiments on two common face databases verify the superiority of the proposed EnL - DFLD over the state-of-the-art algorithms in recognition accuracy

A 3D partitioned active shape model (PASM) is proposed in this paper to address the problems of 3D active shape models (ASM) brought by small training sets, which is usually the case in 3D applications. When numbers of training samples are limited, 3D ASMs tend to be restrictive, because the plausible area/allowable region spanned by relatively few eigenvectors cannot capture the full range of shape variability. 3D PASMs overcome this limitation by using a partitioned representation of ASM. Given a point distribution model, the mean mesh is partitioned into a group of small tiles. The statistical priors of tiles are estimated by applying principal component analysis to each tile, and the priors serve as constraints for corresponding tiles during deformations. To avoid the shape inconsistency introduced by the independent estimations between tiles, samples and deformed model points are projected as curves in one hyperspace, instead of point clouds in several hyperspaces. The deformed model points are then fitted into the allowable region of the model using a curve alignment scheme. The experiments on 3D human brain MRIs show that the 3D PASMs segment objects more accurately and are more robust to noise and low contrast in images than two other current active shape models. Furthermore, a study for the PASM's sensitivity to different initializations shows that PASMs perform stable when initialization positions change

In this paper, a framework of Discriminant Low-dimensional Subspace Analysis (DLSA) method is proposed to deal with the Small Sample Size (SSS) problem in face recognition area. Firstly, it is rigorously proven that the null space of the total covariance matrix, S t, is useless for recognition. Therefore, a framework of Fisher discriminant analysis in a low-dimensional space is developed by projecting all the samples onto the range space of S t. Two algorithms are proposed in this framework, i.e., Unified Linear Discriminant Analysis (ULDA) and Modified Linear Discriminant Analysis (MLDA). The ULDA extracts discriminant information from three subspaces of this lowdimensional space. The MLDA adopts a modified Fisher criterion which can avoid the singularity problem in conventional LDA. Experimental results on a large combined database have demonstrated that the proposed ULDA and MLDA can both achieve better performance than the other state-of-the-art LDA-based algorithms in recognition accuracy. 1

To solve the Small Sample Size (SSS) problem, the recent linear discriminant analysis using the 2D matrix-based data representation model has demonstrated its superiority over that using the conventional vector-based data representation model in face recognition [7]. But the explicit reason why the matrix-based model is better than vectorized model has not been given until now. In this paper, a framework of Generalized 2D Fisher Discriminant Analysis (G2DFDA) is proposed. Three contributions are included in this framework: 1) the essence of these ’2D ’ methods is analyzed and their relationships with conventional ’1D ’ methods are given, 2) a Bilateral and 3) a Kernel-based 2D Fisher Discriminant Analysis methods are proposed. Extensive experiment results show its excellent performance. 1

A novel framework called 2D Fisher discriminant analysis (2D-FDA) is proposed to deal with the small sample size (SSS) problem in conventional one-dimensional linear discriminant analysis (1D-LDA). Different from the 1D-LDA based approaches, 2D-FDA is based on 2D image matrices rather than column vectors so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage arising in this way is that the SSS problem does not exist any more because the between-class and within-class scatter matrices constructed in 2D-FDA are both of full-rank. This framework contains unilateral and bilateral 2D-FDA. It is applied to face recognition where only few training images exist for each subject. Both the unilateral and bilateral 2D-FDA achieve excellent performance on two public databases: ORL database and Yale face database B.

This paper addresses the small sample size (SSS) problem in linear discriminant analysis (LDA) utilizing a so called 2D Fisher discriminant analysis (2D-FDA) algorithm. As opposed to traditional LDA-based approaches, 2D-FDA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector before feature extraction. The between-class scatter and the within-class scatter is constructed using the original image matrices. The advantage arising in this way is that the SSS problem existing in traditional linear discriminant analysis does not occur any more. To test the performance of 2D-FDA with small number of training samples, a series of experiments are conducted on two public databases: ORL and Yale face database B. In both two trials, the 2D-FDA outperforms the other linear subspace methods when there are only very limited training images for each subject.

A new method, called 3D growing deformable B-surface model, is proposed for object detection which works in 3D space directly. First, the coarse boundary of the subject is extracted. The 3D external force field of the subject is generated based on this coarse boundary using modified GVF (gradient vector flow). After the initialization of a surface patch, growing B-surface model starts to deform it to locate the boundary of the object. Next, this surface patch is anchored to the surface of the subject and a new surface patch grows up based it. This process is repeated until a closed surface of the subject is obtained. 3D growing deformable B-surface model overcomes the difficulty that comes from analyzing 3D volume image slice by slice. And the computation load of B-surface is reduced since the internal force is not necessary in every iteration deformation step. Next, the geometric information on every surface point can be calculated easily. And it has the ability Io achieve high compression ratio (the ratio of data to parameters) by presenting the whole surface with only a relatively small number of control points. Growing B-surface model simplifies the initialization step of the surface model.

This paper presents a non-linear automated point distribution model building method (NAMB). In this algorithm, each training sample is registered to all other training samples by a non-linear corresponder. When landmarks are chosen by regarding all shapes of samples, a point distribution model (PDM) is constructed. The contribution of this paper is an accurate non-linear points correspondence detection algorithm and a framework that preserves shape details of training samples. In the experiment, the NAMB is compared with the manual model construction and other automated method to construct PDMs. The comparison illustrated that the NAMB is faster than manual method. At the same time, NAMB is more capable than the other automated method in preserving shape details.

This paper presents a novel algorithm for 2D planar curve recognition based on B-spline modeling. It combines the advantages of the B-spline which are continuous curve representation and affine invariant, and the robustness of the CSS (curvature scale space) matching with respect to noise and affine transformation. It solves the problem of the nonuniqueness of the B-spline in curve matching. A new algorithm, which contains degree increase and reduction, is proposed for smoothing the B-spline and constructing the CSS image. The proposed algorithm has been tested and good performance has been obtained.

A B-snake model using statistics information for segmenting 2D objects from medical images is presented in this paper. Based on our previous research work, a statistical model is proposed for our B-snake model, in order to use available priori knowledge about the object shape being studied. This method allows the deformation of B-snake to be influenced primarily by the most reliable matches. Experimental results show that our method is robust and accurate in object contour extraction in medical images.

This paper presents a novel application of the Bayesian shape model (BSM) for facial feature extraction. First, a full-face model is designed to describe the shape of a face, and the PCA is used to estimate the shape variance of the face model. Then, the BSM is applied to match and extract the face patch from input face images. Finally, using the face model, the extracted face patches are easily warped or normalized to a standard view. Applications of this facial feature extraction algorithm include face recognition, face video coding and retrieval, face animation and multimedia.

In this paper, we presented a structure-adaptive B-snake model for segmenting the complex structures in medical images. A strategy of automatic control-point insertion, adaptive to the structure of the studied object, has been proposed. Furthermore, a method of minimum mean square energy (MMSE) is developed to iteratively estimate the position of those control points in the B-snake model. By applying the proposed structure-adaptive B-snake model to medical images, we show that our method is robust and accurate in object contour extraction.

A new algorithm is proposed to extract the facial features and estimate the control points for facial image warping using the principle component analysis (PCA) based statistic face model. In this algorithm, first a full-face model consisting the contour points and the control points is built. Based on a number of manually marked training samples, the prior distribution of the full-face model can be obtained by using the PCA. Given an input face image, first the contour points are obtained by using the Bayesian shape model (BSM), and then the control points are estimated from the contour points. Finally, the extracted face path is normalized using the piece-wise affine triangle warping algorithm. Experimental results illustrate the effectiveness of the proposed algorithm.

This paper presents a methodology for extracting road edge and lane information for smart and intelligent navigation of vehicles. The range information provided by a fast laser range-measuring device is processed by an extended Kalman filter to extract the road edge or curb information. The resultant road edge information is used to aid in the extraction of the lane boundary from a CCD camera image. The Hough transform is used to extract the candidate of lane boundary edges, and the most probable lane boundary is determined by using an active line model and minimizing an appropriate energy function. Experimental results are presented to demonstrate the effectiveness of the combined laser and vision strategy for road-edge and lane boundary detection.

This paper proposed a novel semi-local feature set, which is proved to be affine invariant. The proposed feature set can be local or global based on the mask parameter selected. Small mask indicates local features, which are more precise and not sensitive to occlusion problems. Large mask indicates global features, which are more robust and less sensitive to noise. Using this feature set a correspondence based velocity estimation algorithm is developed. This motion estimation method can be used in motion detection, motion segmentation, and motion based recognition.

A deformable template model for object extraction is proposed based on the fuzzy alignment algorithm (FAA). This object matching algorithm is partitioned into two iterative processes, the first is to estimate the pose relationship (point correspondence and transform parameters) between the current template and the prototype using FAA, the second is to adjust the current template under the exertion of internal energy and external energy functions. An affine-invariant internal energy function of the deformable template is utilized to deal with the transformation of the templates between different domains. Comparative studies with G-Snake model demonstrate the effectiveness of the proposed algorithm and show that it outperforms G-Snake in matching objects with large shearing of shapes.

We formulate the problem of detecting reflectional symmetries as the problem of minimising an asymmetric energy term. The local minima of the asymmetric energy correspond to the reflectional symmetric axes of an object. Typical experiments show the correctness and the robustness of the proposed technique.

A theorem linking reflectional symmetry and rotational symmetry of the 2D images has been established. This theorem shows that, for a rotationally symmetric image with K/spl ges/1 fold(s) (K-RSI), its number of reflection-symmetric axes must be either K or zero. To the authors' knowledge no previous studies have shown the constraint relationship between reflectional symmetry and rotational symmetry. Demonstrations on some typical images have shown the exactness of our theorem.

An affine-invariant feature vector, which captures local and semi-local features, has been used in the detection of skewed symmetries. Here, the problem of symmetry axes detection has been formulated as a line detection problem, with known orientations within a local similarity matrix computed for a shape. Moreover, our technique allows all the local reflection-symmetries within an object to be detected. Experiments on detecting skewed symmetries of self-symmetric objects and generalized objects, under noise and occlusions, have demonstrated the effectiveness of this method.

An affine-invariant deformable contour model for object matching, called affine-invariant eigensnake (AI-ES), is presented in the Bayesian framework. In AI-ES, the prior distribution of object shapes is estimated and utilized to constrain the prototype contour, which is dynamically adjustable in the matching process. Also, an affine-invariant internal energy is presented to define the global and local shape deformation of the contours between the shape domain and the image domain. Experiments on real object matching show that the proposed method is more robust and insensitive to the positions, viewpoints, and large deformations of object shapes, than the active shape model (ASM) and the AI-snake model.

The paper proposes a novel eigenvector approach for pose and correspondence estimation between the feature points of two images or two point patterns under affine transformation. In the method, the proximity matrices, which record the normalized area features extracted from the two point sets are utilized to calculate the modes of each point set and the corresponding feature vectors. Then the point correspondence can be obtained by calculating the correlation of the feature vectors. To reduce the computation time, the idea of the principal component analysis (PCA) is adopted, which considers only the principal eigenvectors corresponding to the larger eigenvalues of each proximity matrix. As compared with the traditional eigenvector algorithm proposed by L.S. Shapiro and J.M. Brady (1992), the proposed algorithm is demonstrated to be more effective in estimating the point correspondence and the relevant parameters of affine transformation.

This paper presents a unified method for detecting both reflection-symmetry and rotation-symmetry of 2D images based on an identical set of features, i.e., the first three nonzero generalized complex (GC) moments. This method is theoretically guaranteed to detect all the axes of symmetries of every 2D image, if more nonzero GC moments are used in the feature set. Furthermore, we establish the relationship between reflectional symmetry and rotational symmetry in an image, which can be used to check the correctness of symmetry detection. This method has been demonstrated experimentally using more than 200 images.

In this paper, we propose an energy formulation for homomorphic graph matching by the Hopfield network and a Lyapunov indirect method-based learning approach to adaptively learn the constraint parameter in the energy function. The adaptation scheme eliminates the need to specify the constraint parameter empirically and generates valid and better quality mappings than the analog Hopfield network with a fixed constraint parameter. The proposed Hopfield network with constraint parameter adaptation is applied to match silhouette images of keys and results are presented.

The purpose of this paper is to demonstrate how a structural matching approach can be used to perform effective rotational invariant fingerprint identification. In this approach, each of the extracted features is correlated with five of its nearest neighbouring features to form a local feature group for a first-stage matching. After that, the feature with the highest match is used as a central feature whereby all the other features are correlated to form a global feature group for a second-stage matching. The correlation between the features is in terms of distance and relative angle. This approach actually makes the matching method rotational invariant. A substantial amount of testing was carried out and it shows that this matching technique is capable of matching the four basic fingerprint patterns with an average matching time of 4 seconds on a 66 Mhz, 486 DX personal computer.

Higher degree implicit polynomials can be used conveniently for describing 2-D curves and 3-D surfaces. Implicit polynomials are known to possess few Euclidean and affine invariants. However, the instability of fitting algorithms and errors in the estimation of invariants due to occlusion are some of the major issues concerning their usefulness. We proposed a tensor based approach for obtaining the invariants and pose estimation using higher degree implicit polynomials. We demonstrated that the relative values and signs of the invariants from higher degree polynomials are not sensitive to small levels of occlusion and it can be used for recognition and pose estimation of free-form objects.

This paper presents a hybrid approach towards accomplishing the task of range image segmentation. It tends to combine region growing and one-dimensional orthogonal polynomial fitting techniques for the detection of edges in range images. Edges detected by such a method possesses good localisation property. This aids to steer the region growing process towards accomplishing accurate border partitioning. In addition, the incorporation of region growing process eliminates internal micro edges and provides for missing border reconstruction as it is able to detect weak edges. It is believed that the edge and segmentation maps produced, when conveyed to the higher level recognition process, may prove valuable not only for CAD based modeling purposes but may also aid to provide descriptive syntax to the object identification process.

With the growing importance of the robotic manipulators in the area of industrial automation, there is an increase in demand for their position accuracy. To control such robotic systems effectively, more complex forms of control than the simple constant gain linear feedback control is required. Two adaptive control schemes, namely the adaptive PID controller and the self-tuning controller, are implemented in the system. To provide for fast control actions in the robotic system, a real-time approach is adopted. Such operation requires large computing power in a relatively short period of time. A digital signal processor (DSP) whose architecture is optimized to process sampled data at high rate is well suited to this purpose. Its architecture is designed to exploit the repetitive nature of signal processing by pipelining the data flow for extra speed. The TMS320C25 DSP chip is chosen for this purpose. From extensive experimental testing, it is shown that the position accuracy of the robotic manipulator using the given adaptive control schemes is within the 0.5 mm range.>

This paper presents a path planning algorithm, using a rule-based expert system concept, for real-time control of a robotic manipulator. The algorithm is able to generate three different paths to enable the manipulator to avoid an obstacle and then to select the shortest or the least-segmented path to reach the end point. This algorithm requires very little computation time as compared to some techniques. A graphical presentation of the result is also provided on-screen together with a 3D display to enhance the user's visualization of the results obtained. Simulation results showed that with the input data, the algorithm is able to generate the appropriate path and compute the necessary link angles to effect a movement of the links of the manipulator based on the generated path data.>