We propose a simple and fast method for tangent estimation of digital curves. This geometric-based method uses a small local region for tangent estimation and has a definite upper bound error for continuous as well as digital conics, i.e., circles, ellipses, parabolas, and hyperbolas. Explicit expressions of the upper bounds for continuous and digitized curves are derived, which can also be applied to nonconic curves. Our approach is benchmarked against 72 contemporary tangent estimation methods and demonstrates good performance for conic, nonconic, and noisy curves. In addition, we demonstrate a good multigrid and isotropic performance and low computational complexity of O(1) and better performance than most methods in terms of maximum and average errors in tangent computation for a large variety of digital curves.

In this paper, we propose an implicit silhouette based (template based) method of recognizing activities in video. The novelty of the proposed method is that it extracts features from the negative space to form action templates while other implicit methods employed positive spaces to construct action templates. Extracting features from negative space facilitates the system to extract simple, yet effective features to describe each action. These features are robust to deformed actions due to complex boundary variations, partial occlusions, non-rigid deformation and small shadow. Moreover, other template based methods need to apply additional processing to reduce the dimensionality of the extracted features, but negative space templates do not require dimensionality reduction since it describes actions with low-dimensional features. The proposed method improves its processing time while achieving comparable accuracies with other state of the art methods.

Most dominant point detection methods require heuristically chosen control parameters. One of the commonly used control parameter is maximum deviation. This paper uses a theoretical bound of the maximum deviation of pixels obtained by digitization of a line segment for constructing a general framework to make most dominant point detection methods non-parametric. The derived analytical bound of the maximum deviation can be used as a natural bench mark for the line fitting algorithms and thus dominant point detection methods can be made parameter-independent and non-heuristic. Most methods can easily incorporate the bound. This is demonstrated using three categorically different dominant point detection methods. Such non-parametric approach retains the characteristics of the digital curve while providing good fitting performance and compression ratio for all the three methods using a variety of digital, non-digital, and noisy curves.

We present a contour based approach to object recognition in real-world images. Contours are represented by generic shape primitives of line segments and ellipses. These primitives offer substantial flexibility to model complex shapes. We pair connected primitives as shape tokens, and learn category specific combinations of shape tokens. We do not restrict combinations to have a fixed number of tokens, but allow each combination to flexibly evolve to best represent a category. This, coupled with the generic nature of primitives, enables a variety of discriminative shape structures of a category to be learned. We compare our approach with related methods and state-of-the-art contour based approaches on two demanding datasets across 17 categories. Highly competitive results are obtained. In particular, on the challenging Weizmann horse dataset, we attain improved image classification and object detection results over the best contour based results published so far.

We propose a novel region-based method to recognize human actions by analyzing regions surrounding the human body, termed as negative space according to art theory, whereas other region-based approaches work with silhouette of the human body. We find that negative space provides sufficient information to describe each pose. It can also overcome some limitations of silhouette based methods such as leaks or holes in the silhouette. Each negative space can be approximately represented by simple shapes, resulting in computationally inexpensive feature description that supports fast and accurate action recognition. The proposed system has obtained 100% accuracy on the Weizmann human action dataset and is found more robust with respect to partial occlusion, shadow, noisy segmentation and non-rigid deformation of actions than other methods.

The precision/uncertainty duality has been long known in the context of Hough transform, where a shape in an image cannot be fit precisely using the Hough transform without compromising the certainty or reliability of the fitting. This paper mathematically shows that such duality also exists while using the least squares based method. This paper also proposes a method to quantify the reliability of a fit. Further, based on the proposed measure of reliability, an optimization scheme to strike a balance between the precision and reliability is suggested. Though the mathematical formulations deal with only straight line, considering it as the simplest and basic geometric primitive, it is argued that such duality exists for any shape fitting and applies to any shape fitting method.

We propose a new class of descriptors which exhibits the ability to yield meaningful structural descriptions of objects. These descriptors are constructed from two types of image primitives: quadrangles and ellipses. The primitives are extracted from an image based on human cognitive psychology and model local parts of objects. Experiments reveal that these primitives densely cover objects in images. In this regard, structural information of an object can be comprehensively described by these primitives. It is found that a combination of simple spatial relationships between primitives plus a small set of geometrical attributes provide rich and accurate local structural descriptions of objects. Category level object detection of four-legged animals, bicycles, and cars images is demonstrated under scaling, moderate viewpoint variations, and background clutter. Promising results are achieved.

A novel and automated technique for learning human perspective context (HPC) from a scene is proposed in this paper. It is found that two models are required to describe HPC for camera tilt angle ranging from 0° to 50°. From a scene, the tilt angle can be inferred from the observed human shapes and head/foot positions. Afterward, a novel ME-DT (Model Estimation - Data Tuning) algorithm is proposed to learn human perspective context from live data of various degrees of uncertainties. The uncertainties may come from the variations of human individual heights and poses, and segmentation/recognition errors. ME-DT not only estimates the model parameters from the training data but also tunes the data to achieve a better head-foot correlation. The human perspective context provides a feasible constraint on the scales, positions, and orientations of humans in the scene. Applying this constraint to the HOG human detection, great reduction of the detection windows and improved performances have been obtained compared to conventional methods.

We present an ellipse detector that continually pools lower level information of the edge pixels together to achieve robust detection of the ellipses present in the image. In addition, the parameters of the detected ellipses are continually refined using a close loop system driven by Gestalt psychology. We highlight that we do not rely on the geometrical properties of the ellipses to detect the ellipses. In this aspect, our algorithm is well suited to detect partially occluded ellipses in the image. Experiments on real and synthetic images demonstrate the robustness of our algorithm in which both complete and incomplete ellipses can be detected. In particular, experimental results show that the mean detection accuracy of our algorithm surpasses 92% even with around 90% outliers in the images. This detection performance is superior to that achieved by the robust regression, least squares and the hough transform based ellipse detectors.

Mug shot photography has been used to identify criminals by the police for more than a century. However, the common scenario of face recognition using frontal and side-view mug shots as gallery remains largely uninvestigated in computerized face recognition across pose. This paper presents a novel appearance-based approach using frontal and sideface images to handle pose variations in face recognition, which has great potential in forensic and security applications involving police mugshot databases. Virtual views in different poses are generated in two steps: 1) shape modelling and 2) texture synthesis. In the shape modelling step, a multilevel variation minimization approach is applied to generate personalized 3-D face shapes. In the texture synthesis step, face surface properties are analyzed and virtual views in arbitrary viewing conditions are rendered, taking diffuse and specular reflections into account. Appearance-based face recognition is performed with the augmentation of synthesized virtual views covering possible viewing angles to recognize probe views in arbitrary conditions. The encouraging experimental results demonstrated that the proposed approach by using frontal and side-view images is a feasible and effective solution to recognizing rotated faces, which can lead to a better and practical use of existing forensic databases in computerized human face-recognition applications.

The main advantage of using the Hough Transform to detect ellipses is its robustness against missing data points. However, the storage and computational requirements of the Hough Transform preclude practical applications. Although there are many modifications to the Hough Transform, these modifications still demand significant storage requirement. In this paper, we present a novel ellipse detection algorithm which retains the original advantages of the Hough Transform while minimizing the storage and computation complexity. More specifically, we use an accumulator that is only one dimensional. As such, our algorithm is more effective in terms of storage requirement. In addition, our algorithm can be easily parallelized to achieve good execution time. Experimental results on both synthetic and real images demonstrate the robustness and effectiveness of our algorithm in which both complete and incomplete ellipses can be extracted.

Palmistry is an ancient type of art and is trusted by generations of people. Our system aims at introducing this ancient science to the modern technology. Instead of traditional face-to-face palm reading, our online fortune telling system is where the users submit their hand images online and get the predictions immediately. The major line patterns in palmistry are summarized to become the models for online fortune telling through palm reading. The images are converted to Hough space to extract principal lines. This is a novel approach to the application of the Hough transform for feature extraction of palmprint. A principal line detection mechanism in transformed space is also proposed based on a flooding process, which is motivated by Rainfalling Watershed segmentation algorithm. The local neighbourhood information of principal lines is used as a means to measure similarity as well. The experiment results show that we achieve low time complexity with high accuracy.

A novel local structural approach, which is a sequel to our previous work, is proposed in this paper for object retrieval in a cluttered and occluded environment without identifying the outlines of an object. It works by first extracting consistent and structurally unique local neighborhood from inputs or models and then voting on the optimal matches employing dynamic programming and a novel hypercube-based indexing structure. The proposed concepts have been tested on a database with thousands of images and compared with the six nearest-neighbors shape description with superior results.

This work proposes a line-based Hough transform method, which extracts global features for coarse-level filtering in two-stage palmprint identification system. This is a novel approach to the application of the Hough transform for feature extraction of palmprint. A principal line detection mechanism in transformed space is also proposed based on a flooding process, which is motivated by rainfalling watershed segmentation algorithm. The local neighbourhood information of principal lines is used as a means to measure similarity as well. It works by first extracting consistent and structurally unique local neighbourhood information from inputs or models, and then voting on the optimal matches. It performs speedy interpretation of input images and retrieval of structurally similar models from large database according to the input. The local information extracted from position and orientation of individual line is used for further fine-level identification. Line-based Hausdorff distance (LHD) algorithm is applied for local line matching. Experiments had been conducted on a palmprint database collected by Hong Kong Polytechnic University. Promising results are achieved

In today's world, biometrics system is used almost everywhere for the security and personal recognition. The palmprint is one of the most reliable physiological characteristics that can be used to distinguish between individuals. Palmprint classification provides an important indexing mechanism in a palmprint database. An accurate and consistent classification can greatly reduce palmprint matching time for a large database. We propose a palmprint classification algorithm which is able to classify palmprints into ten evenly-distributed categories (1, 2, 3, 4, 6, A, B, C, D, and E). The algorithm uses a novel representation and is based on a two-stage classifler to make a classification.

One of the key remaining problems in face recognition is that of handling the variability in appearance due to changes in pose. One strategy is to synthesize virtual face views from real views. In this paper, a novel 3D face shape-modeling algorithm, Multilevel Quadratic Variation Minimization (MQVM), is proposed. Our method makes sole use of two orthogonal real views of a face, i.e., the frontal and profile views. By applying quadratic variation minimization iteratively in a coarse-to-fine hierarchy of control lattices, the MQVM algorithm can generate C²-smooth 3D face surfaces. Then realistic virtual face views can be synthesized by rotating the 3D models. The algorithm works properly on sparse constraint points and large images. It is much more efficient than single-level quadratic variation minimization. The modeling results suggest the validity of the MQVM algorithm for 3D face modeling and 2D face view synthesis under different poses.

The automatic recognition of facial expression presents a significant challenge to the pattern analysis and man-machine interaction research community. Recognition from a single static image is particularly a difficult task. In this paper, we present a methodology for facial expression recognition from a single static image using line-based caricatures. The recognition process is completely automatic. It also addresses the computational expensive problem and is thus suitable for real-time applications. The proposed approach uses structural and geometrical features of a user sketched expression model to match the line edge map (LEM) descriptor of an input face image. A disparity measure that is robust to expression variations is defined. The effectiveness of the proposed technique has been evaluated and promising results are obtained. This work has proven the proposed idea that facial expressions can be characterized and recognized by caricatures.

Face recognition is one of the most suitable biometric methods for supporting systems that require high security. The success of a face recognition system involves more than just the comparison algorithm. Face data acquisition is the first step towards developing face recognition systems. The accuracy of a face recognition system greatly depends on the face data acquired. This paper proposes the head-nodding method for face capturing using motion analysis. By analyzing the head-nodding motion of the user, the head-nodding method captures face images with consistent orientation to support face verification systems. In this paper, the design and implementation of the head-nodding method is presented. Preliminary experimental results are also presented.

One of the key remaining problems in face recognition is that of handling the variability in appearance due to changes in pose. One strategy is to synthesize virtual face views from real views. In this paper, a new 3D face shape modeling algorithm - orthogonal reverse projection model (ORPM) is presented. Our method makes only use of two orthogonal real view of a face, i.e., the frontal and profile views. To obtain the 3D shape information of the face, a modified feature-based 2D image warping technique is employed to establish point correspondence between the frontal and profile views. Then the 3D face shape model is generated by orthogonal reverse projection using the corresponding point pairs. The virtual views can be synthesized by simply rotating the 3D face model. The modeling results suggest the validity of the ORPM algorithm for 3D face modeling and 2D face image synthesizing under different poses.

The profile view of a face provides a complementary structure that is not seen in the frontal view. The classification system combining both frontal and profile views of faces can improve the classification accuracy. And it would be more foolproof because it is difficult to fool the profile face identification by a mask. This paper proposes a new face recognition approach, which can be applied on both frontal and profile faces, to build a robust combined multiple view face identification system. The recognition employs a novel facial corner coding and matching method, and integrates the outline and interior facial parts in the profile matching. The proposed multiview modified Hausdorff distance fuses multiple views of faces to achieve an improved system performance.

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.

We propose a novel technique for robust change detection based upon the integration of intensity and texture differences between two frames. A new accurate texture difference measure based on the relations between gradient vectors is proposed. The mathematical analysis shows that the measure is robust with respect to noise and illumination changes. Two ways to integrate the intensity and texture differences have been developed. The first combines the two measures adaptively according to the weightage of texture evidence, while the second does it optimally with additional constraint of smoothness. The parameters of the algorithm are selected automatically based on a statistic analysis. An algorithm is developed for fast implementation. The computational complexity analysis indicates that the proposed technique can run in real-time. The experiment results are evaluated both visually and quantitatively. They show that by exploiting both intensity and texture differences for change detection, one can obtain much better segmentation results than using the intensity or structure difference alone.

The paper proposes a novel technique for robust change detection based upon the integration of intensity and texture differences between two frames. A new texture difference measure based on the relations between gradient vectors is described. The robustness of the measure with respect to noise and illumination changes has been analyzed. Two ways to integrate the intensity and texture differences are proposed. The first combines two measures according to the weightage of texture evidence, while the second takes into additional constraint of smoothness. The parameters of the algorithm are selected automatically. The computational complexity analysis indicates that the proposed technique can run in real-time. Experimental results show that by exploiting both intensity and texture differences for change detection, one can obtain much better segmentation results than using the intensity or structure difference alone.

Linear layout is a novel concept that exploits the geometric structure of documents or shapes for tasks such as representation and identification. The layout of characteristic features such as lines, blocks of text or dominant points is converted from two-dimensional to one-dimensional space. The features are then quantized and arranged into a linear string representation. The recognition process makes use of the longest common subsequence string to compute the similarity between two documents. The conversion and the string operations make possible a robust system that tolerates noise, deformation and segmentation inconsistencies such as missing and added objects. Encouraging experimental results have been obtained to highlight the potential of the proposed concept and technique.

Business forms are "linear" documents which can be accurately described by a one-dimensional data structure. This paper proposes a novel approach for form identification using strings. This application can be used as a basis for extension to other "linear" documents such as logos or line drawings. A set of known blank forms is stored in a database and incoming forms are automatically matched to one of these. In addition, forms which are not in the database can also be detected. A novel and simple method is used for matching by considering a distinctive "signature" for each document. This takes the shape of a string which describes the elements present on the form. Included are the location and size of lines, corners and blocks of text, quantised as discrete symbols. A specially adapted and efficient string edit distance calculation is then applied for matching. Unregistered forms can be detected by examining the unmatched elements between two strings. This novel string format makes it possible to extend the conventional one-dimensional representation possibilities of strings to a richer "one-and-a-half dimensional" structure and requires no training.

A classifier is proposed in this paper to extract structural information from business forms. The classifier is built upon existing techniques and takes advantage of the highly structured nature of forms, containing lines, boxes and text. Improvements are made to widen the scope of the classifier to handle some unexpected cases from real images. A syntactic representation is built from the detected features using their positions and lengths. The information recorded in this representation is independent of scale and displacement. A filled in form can then be compared to prerecorded blank forms to see which of these fits the best. Encouraging experimental results have been obtained.

First Sight, a vision system in labeling the outline of a moving human body, is proposed in this paper. The emphasis of First Sight is on the analysis of motion information gathered solely from the outline of a moving human object. Two main processes are implemented in First Sight. The first process uses a novel technique to extract the outline of a moving human body from an image sequence. The second process, which employs a new human body model, interprets the outline and produces a labeled two-dimensional human body stick figure for each frame of the image sequence. Extensive knowledge of the structure, shape, and posture of the human body is used in the model. The experimental results of applying the technique on unedited image sequences with self-occlusions and missing boundary lines are encouraging.>