A new approach for rotated face detection
Human face detection has always been an important problem for face recognition and face tracking. Though considerable attempts have been made to detect and localize faces, these approaches have made assumptions that restrict their extension to more general cases. In this paper we design a novel method to detect a rotated face on the basis of image edge information. Considering the efficiency problem, we propose two key techniques in our approach: first, three points based RHT is applied to detect an ellipse; moreover, we speedup the template matching procedure through calculating the orientation histogram distribution and locating the symmetry axis of a rotated face. In the end, we will validate our idea by several experiment results.

title: Using visual features for anti-spam filtering
abstract: Unsolicited commercial email (UCE), also known as spam, has been a major problem on the Internet. In the past, researchers have addressed this problem as a text classification or categorization problem. However, as spammers' techniques continue to evolve and the genre of email content becomes more and more diverse, text-based anti-spam approaches alone are no longer sufficient. In this paper, we propose a novel anti-spam system which utilizes visual clues, in addition to text information in the email body, to determine whether a message is spam. We analyze a large collection of spam emails containing images and identify a number of useful visual features for this application. We then propose using one-class support vector machines (SVM) as the underlying base classifier for anti-spam filtering. The experimental results demonstrate that the proposed system can add significant filtering power to the existing text-based anti-spam filters.

title: Adaptive learning of an accurate skin-color model
abstract: Due to variations of lighting conditions, camera hardware settings, and the range of skin coloration among human beings, a pre-defined skin-color model cannot accurately capture the wide distribution of skin colors in individual images. In this paper, we propose an adaptive skin-detection method, which allows modeling true skin-color distribution with significantly higher accuracy and flexibility than other methods attain. In principle, the proposed method follows a two-step process. For a given image, we first perform a rough skin classification using a generic skin model, which defines the skin-similar space. In the second step, a Gaussian mixture model (GMM), specific to the image under consideration and refined from the skin-similar space, is derived using the standard expectation-maximization (EM) algorithm. Then, we use an SVM (support vector machine) classifier to identify the skin Gaussian from the trained GMM (which contains two Gaussian components) by incorporating spatial and shape information of the skin pixels. This adaptive method can be applied to both still images and video applications. Results of extensive experiments performed on live video sequences and large image databases have demonstrated the effectiveness and benefits of the proposed model.

title: Hierarchical model based human motion tracking
abstract: Image sequence based tracking is the pivotal technique of human motion. In the paper, we first propose an appropriate human model and color model. Second, two approaches are proposed aiming at two different levels of color-block information including the boundary and the inner-area: the extraction of the boundary algorithm is based on the Robert operator and the clustering algorithm is based on self-adaptation. Third, we unite two regions, which are processed by different approaches. This step counteracts the ambiguity of obtaining information from each level. Finally, after the rough area of the color-block is obtained, the boundary of the color-block is determined by calculating the histogram of the X,Y coordinate of every point on the boundary of the block. Experimental results are presented.

title: Multimodal fusion using learned text concepts for image categorization
abstract: Conventional image categorization techniques primarily rely on low-level visual cues. In this paper, we describe a multimodal fusion scheme which improves the image classification accuracy by incorporating the information derived from the embedded texts detected in the image under classification. Specific to each image category, a text concept is first learned from a set of labeled texts in images of the target category using Multiple Instance Learning . For an image under classification which contains multiple detected text lines, we calculate a weighted Euclidian distance between each text line and the learned text concept of the target category. Subsequently, the minimum distance, along with low-level visual cues, are jointly used as the features for SVM-based classification. Experiments on a challenging image database demonstrate that the proposed fusion framework achieves a higher accuracy than the state-of-art methods for image classification.

title: A unified adaptive approach to accurate skin detection
abstract: Due to variations of lighting conditions and camera hardware settings and the existence of many ethnic people with a wide range of skin colors, a generic skin model is often inadequate to accurately capture the skin distribution for individual images. In this paper, we propose an adaptive skin detection framework, which allows modeling title skin distribution with significantly higher accuracy and flexibility. First, an adaptive skin model, specific to the image under consideration and refined from the skin-similar space, is derived using a Gaussian mixture model (GMM) and standard expectation maximization (EM) algorithm. Then, we develop a support vector machine (SVM) classifier to identify the skin Gaussian from the trained GMM (with two Gaussian components) by incorporating spatial and shape information of skin pixels. Extensive experimental results performed on large image databases have demonstrated the effectiveness and benefits of the proposed approach.

title: Towards efficient and scalable speech compression schemes for robust speech recognition applications
abstract: This paper presents a scheme for distributed automatic speech recognition. A hidden Markov model (HMM)-based speech recognition system with a mel frequency cepstral coefficients (MFCC) front end was used in the evaluation. The goal was to achieve good recognition performance while compressing the MFCC feature vectors. Compression rates and recognition performance for both a digit and an alphabet database are reported. Compared to a scheme of recognizing speech encoded by low bit rate encoders, and previously reported schemes, our method can achieve good recognition performance with bit rates lower than 1 kbps, using low encoding complexity. The encoding algorithms developed are scalable, allowing bit rate and recognition performance trade-offs, and can be combined with unequal error protection or prioritization to allow graceful degradation of performance in the presence of channel errors.

title: Propagation of Geometric Tolerance Zones in 3D
abstract: This paper considers error propagation in three dimensional geometric constructions using a geometric approach. First, we present definitions and constructions of tolerance zones for various fundamental elements in Euclidean space. Then, we study in detail the propagation of errors during several geometric computations, including the the distance between two skew lines, reflections, projections, and rotations, and we derive new tolerance zones from the old ones.

title: Query optimization in multidatabase systems
abstract: A multidatabase system (MDBS) integrates information from autonomous local databases managed by heterogeneous database management systems (DBMS) in a distributed environment. For a query involving more than one database, global query optimization should be performed to achieve good overall system performance. The significant differences between an MDBS and a traditional distributed database system (DDBS) make query optimization in the former more challenging than in the latter. Challenges for query optimization in an MDBS are discussed in this paper. A two-phase optimization approach for processing a query in an MDBS is proposed. Several global query optimization techniques suitable for an MDBS, such as semantic query optimization, query optimization via probing queries, parametric query optimization and adaptive query optimization, are suggested. The architecture of a global query optimizer incorporating these techniques is designed.

title: Dynamic indexing for multidimensional non-ordered discrete data spaces using a data-partitioning approach
abstract: Similarity searches in multidimensional Non-ordered Discrete Data Spaces (NDDS) are becoming increasingly important for application areas such as bioinformatics, biometrics, data mining and E-commerce. Efficient similarity searches require robust indexing techniques. Unfortunately, existing indexing methods developed for multidimensional (ordered) Continuous Data Spaces (CDS) such as the R-tree cannot be directly applied to an NDDS. This is because some essential geometric concepts/properties such as the minimum bounding region and the area of a region in a CDS are no longer valid in an NDDS. Other indexing methods based on metric spaces such as the M-tree and the Slim-trees are too general to effectively utilize the special characteristics of NDDSs, resulting in nonoptimized performance. In this article, we propose a new dynamic data-partitioning-based indexing technique, called the ND-tree, to support efficient similarity searches in an NDDS. The key idea is to extend the relevant geometric concepts as well as some indexing strategies used in CDSs to NDDSs. Efficient algorithms for ND-tree construction and techniques to solve relevant issues such as handling dimensions with different alphabets in an NDDS are presented. Our experimental results on synthetic data and real genome sequence data demonstrate that the ND-tree outperforms the linear scan, the M-tree and the Slim-trees for similarity searches in multidimensional NDDSs. A theoretical model is also developed to predict the performance of the ND-tree for random data.

title: The ND-Tree: A Dynamic Indexing Technique for Multidimensional Non-ordered Discrete Data Spaces
abstract: Similarity searches in multidimensional Nonordered Discrete Data Spaces (NDDS) are becoming increasingly important for application areas such as genome sequence databases. Existing indexing methods developed for multidimensional (ordered) Continuous Data Spaces (CDS) such as R-tree cannot be directly applied to an NDDS. This is because some essential geometric concepts/properties such as the minimum bounding region and the area of a region in a CDS are no longer valid in an NDDS. On the other hand, indexing methods based on metric spaces such as M-tree are too general to effectively utilize the data distribution characteristics in an NDDS. Therefore, their retrieval performance is not optimized. To support efficient similarity searches in an NDDS, we propose a new dynamic indexing technique, called the ND-tree. The key idea is to extend the relevant geometric concepts as well as some indexing strategies used in CDSs to NDDSs. Efficient algorithms for ND-tree construction are presented. Our experimental results on synthetic and genomic sequence data demonstrate that the performance of the ND-tree is significantly better than that of the linear scan and M-tree in high dimensional NDDSs.