In this paper, we present a novel method which uses PSO combined LMedS and orthogonal array to improve the estimation of fundamental matrix. We first translate the fundamental matrix estimation problem into the PSO problem. Then, we use the orthogonal array and LMedS to make the initial particles for more robust. Our experiments show that the proposed ROPSO produces more accurate estimation of fundamental matrices than the traditional LMedS, and the ROPSO has higher probability to find the optimal solution than the LMedS.

Eye gaze tracking is a technique which is commonly used in human-computer interaction. By determining the eye gaze, the point-of-regard can be estimated by intersecting the gaze line and the target plane. A particular assumption is that irises are regarded as ellipses rather than circles in our approach. Besides, only one eye is required in the processes of the approach. The unique eye gaze can be computed via some image processing techniques such as edge detection and ellipse fitting and exploiting geometric properties of circles and ellipses in the 3D space. One of the key phases in our approach is the ellipse fitting process due to the noise of the image. Conventional algorithms of ellipse fitting are sensitive to noise. In this paper we have improved the randomized Hough transform to reduce the effect of noise. It is also confirmed to be robust by experimenting with real images. The estimation results of point-of-regard are illustrated and four distinct clusters can be separated robustly with both the influences of resolution and eyelids.

Due to degeneracy problems (e.g. corresponding point pairs are located on the same plane), it is difficult to estimate the fundamental matrix robustly and reliably using estimators even if without outliers in corresponding point pairs. In order to overcome this problem, two novel and fast methods to test the coplanarity of the corresponding point pairs are proposed. The first method is to use plane fitting. Without rectification, we formulate a plane equation to check the coplanarity of the selected corresponding point pairs. The second method employs the concept of a homography. In several experimental designs, experiments were used to test our proposed methods. According to our results, the performance of our two proposed methods is better than that of the traditional method. Furthermore, the method using homography is better than that using plane fitting. Finally, we apply our results to 3D reconstruction.

Two uncalibrated perspective images of a single rigid object/scene are related by the so-called epipolar geometry, which can be described by a 3x3 fundamental matrix. In the literature, there are lots of techniques for estimating the fundamental matrix. Because of degeneracies, however, the coplanar problem about selecting corresponding point pairs may let the estimation of fundamental matrices lead to wrong results. In this paper, two fast and robust algorithms, such as the plane fitting and homography algorithms are proposed to overcome the coplanar problem. The methods combining bucketing techniques with plane fitting and homography constraints are used to deal with some translational and rotational cases. In this paper, some experimental results are shown. According to our experimental results, the performance of our two proposed methods is better than that of the traditional method. Furthermore, the method using homography is better than that using plane fitting. Finally, we apply our results to 3D reconstruction.

To improve query efficiency, most image retrieval systems utilize clustering algorithms to build indices on image databases. In this paper, we analysis region-based image retrieval (RBIR) system query results, hypothesis test and Hubert's Gamma statistic for cluster validation. Our experiment results suggest that images originated from different categories do form clusters in the feature space and thus can be separated. We then analysis the performance of clustering algorithms including K-means, fuzzy C-mean, CA-clustering, density based spatial clustering of applications with noise (DBSCAN) and the proposed modified DBSCAN algorithm with a second merging phase. Our experiment results suggest that the proposed algorithm has clustering performance among the best algorithms. And the proposed modified DBSCAN algorithm with a second merging phase can avoid some of the drawbacks (e.g., k random initial cluster centers) in the original K-means algorithms.

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.

We propose an adaptive skin-detection method, which allows modelling and detection of the true skin-color pixels with significantly higher accuracy and flexibility than previous methods. In principle, the proposed approach 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. The Skin-Similar space often contains many non-skin pixels due to the inevitable overlap in the color space between skin pixels and some non-skin pixels under the generic skin-model. The objective of the second step is to reduce the false-positive rate by analyzing the image under consideration. Specifically, in the second step, a Gaussian Mixture Model (GMM), specific to the image under consideration and refined from its Skin-Similar space, is derived using the standard Expectation-Maximization (EM) algorithm. We then use a Support Vector Machine (SVM) classifier to identify the skin Gaussian from the trained GMM by incorporating spatial and shape information of the skin pixels. Moreover, we examine how the improvement on skin detection by this adaptive skin-model impacts the detection accuracy in the application of Objectionable Image Filtering. We further propose a two-level classification scheme based on hierarchical bagging to improve the accuracy. Results of extensive experiments on large databases demonstrate the effectiveness and benefits of our adaptive skin-model.

Using cameras for detecting hazardous or suspicious events has spurred new research for security concerns. To make such detection reliable, researchers trust overcome difficulties such as variation in camera capabilities, environmental factors. imbalances of positive and negative training data, and asymmetric costs of misclassifying events of different classes. Following up on the event-detection framework (Wu et al. (2003)) that we have proposed, we present in this paper the framework's two major components: invariant feature extraction and biased statistical inference. We report results of our experiments using the framework for detecting suspicious motion events in a parking lot.

The proliferation of objectionable information on the Internet has reached a level of serious concern. To empower end-users with the choice of blocking undesirable and offensive websites, we propose a multimodal information filter, named MORF. In this paper, we present MORF's core components: its confidence-based classifier, a Cross-bagging ensemble scheme, and multimodal classification algorithm. Empirical studies and initial statistics collected from the MORF filters deployed at sites in the U.S. and Asia show that MORF is both efficient and effective, due to our classification methods.

We present a framework for multi-camera video surveillance. The framework consists of three phases: detection, representation, and recognition. The detection phase handles multi-source spatio-temporal data fusion for efficiently and reliably extracting motion trajectories from video. The representation phase summarizes raw trajectory data to construct hierarchical, invariant, and content-rich descriptions of the motion events. Finally, the recognition phase deals with event classification and identification on the data descriptors. Because of space limits, we describe only briefly how we detect and represent events, but we provide in-depth treatment on the third phase: event recognition. For effective recognition, we devise a sequence-alignment kernel function to perform sequence data learning for identifying suspicious events. We show that when the positive training instances (i.e., suspicious events) are significantly outnumbered by the negative training instances (benign events), then SVMs (or any other learning methods) can suffer a high incidence of errors. To remedy this problem, we propose the kernel boundary alignment (KBA) algorithm to work with the sequence-alignment kernel. Through empirical study in a parking-lot surveillance setting, we show that our spatio-temporal fusion scheme and biased sequence-data learning method are highly effective in identifying suspicious events.

The ability to provide accurate and efficient result estimations of user queries is very important for the query optimizer in database systems. In this paper, we show that the traditional estimation techniques with data reduction points of view do not produce satisfiable estimation results if the query patterns are dynamically changing. We further show that to reduce query estimation error, instead of accurately capturing the data distribution, it is more effective to capture the user query patterns. In this paper, we propose query estimation techniques that can adapt to user query patterns for more accurate estimates of the size of selection or range queries over databases.

We describe the Perception-Based Image Retrieval (PBIR) system that we have built on our recently developed query-concept learning algorithms, MEGA and SVMActive. We show that MEGA and SVMActive can learn a complex image-query concept in a small number of user iterations (usually three to four) on a large, multi-category, high-dimensional image database.

Query size estimation is crucial for many database system components. In particular, query optimizers need efficient and accurate query size estimation when deciding among alternative query plans. In this paper we propose a novel sampling technique based on the golden rule of sampling, introduced by von Neumann in 1947, for estimating range queries. The proposed technique randomly samples the frequency domain using the cumulative frequency distribution and yields good estimates without any a priori knowledge of the actual underlying distribution of spatial objects. We show experimentally that the proposed sampling technique gives smaller approximation error than the Min-Skew histogram based and wavelet based approaches for both synthetic and real datasets. Moreover, the proposed technique can be easily extended for higher dimensional datasets.

Data cubes are designed to pro vide aggregate information that can be used to analyze the contents of databases and data w arehouses.A range query applies an aggregation operation (e.g., SUM, A VERAGE) over all selected cells in a data cube, where the selection is speci ed by providing ranges of values on dierent dimensions.Previous approaches to process the range sum query on data cubes (e.g., pre x sum) have l o w query costs but have high update costs.We use the discrete wavelet transformation to decompose a data cube into w avelet coecien ts of dierent resolutions.The resulting decomposed data cube can be used for progressiv eand appro ximatequery processing.This new approach can handle clustered and sparse data gracefully and can provide ecient performance for both queries and updates, which m a k es the wavelet decomposition technique suitable for dynamic data cube environments.

Similarity search in time-series databases has received significant attention lately. Popular techniques for efficient retrieval of time sequences in time-series databases has been to use Discrete Fourier Transform (DFT). Recently, the Discrete Wavelet Transform (DWT) has gained popular interest in database domain and several proposals have been made to replace DFT by DWT for similarity search over time-series databases. In this paper, we explore the feasibility of replacing DFT by DWT with a comprehensive analysis of the DFT and DWT as matching functions in time-series databases. Our results show that although the DWT based technique has several advantages, e.g., the DWT has complexity of O(N) whereas DFT is O(N log N ), DWT does not reduce relative matching error and does not increase query precision in similarity search as suggested by previous works [1]. We conclude that, by exploring the conjugate property of DFT in real domain, the DFT-based and DWT-based techniques yiel...

article Free Access Share on PowerBookmarks: a system for personalizable Web information organization, sharing, and management Authors: Wen-Syan Li C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CA C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CAView Profile , Quoc Vu C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CA C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CAView Profile , Edward Chang C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CA C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CAView Profile , Divyakant Agrawal View Profile , Kyoji Hirata View Profile , Sougata Mukherjea View Profile , Yi-Leh Wu View Profile , Corey Bufi View Profile , Chen-Chuan Kevin Chang View Profile , Yoshinori Hara C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CA C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CAView Profile , Reiko Ito 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, Japan 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, JapanView Profile , Yutaka Kimura 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, Japan 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, JapanView Profile , Kezuyuki Shimazu 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, Japan 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, JapanView Profile , Yukiyoshi Saito 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, Japan 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, JapanView Profile Authors Info & Claims ACM SIGMOD RecordVolume 28Issue 2June 1999 pp 565–567https://doi.org/10.1145/304181.304578Online:01 June 1999Publication History 22citation480DownloadsMetricsTotal Citations22Total Downloads480Last 12 Months6Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF