We consider the problem of change estimation in a 2D random field. Our goal is to estimate the locations of changes when they occur. We propose a nonparametric change estimator which can be implemented efficiently. Co...
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We consider the problem of change estimation in a 2D random field. Our goal is to estimate the locations of changes when they occur. We propose a nonparametric change estimator which can be implemented efficiently. Consistency result is derived for the proposed estimator under certain conditions. We analyze the performance of the proposed algorithms under several new estimation criteria.
In this paper, we present a perception principles-guided video segmentation method, where statistical modeling and graph-theoretic approaches are combined in a multi-layer classification architecture. Various visual c...
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In this paper, we present a perception principles-guided video segmentation method, where statistical modeling and graph-theoretic approaches are combined in a multi-layer classification architecture. Various visual cues are effectively incorporated in a sequential segmentation process. Specifically, low-level pixel-wise features are used in the first layer where a joint spatio-temporal statistical modeling approach is used to construct entry-level visual units in space-time. In the second layer, all units are first classified into dynamic or static units based their motion magnitudes. Then dynamic units are further parsed into over-segmented moving regions that are connected in space and time, and a mid-level feature, motion trajectory, is extracted for each moving region. In the third layer, still and moving regions are merged into background and moving objects by a graph-based approach with different similarity metrics. The proposed algorithm employs both long-range motion information, i.e., trajectory, and short-range motion information, i.e., changedetection, to retain temporal continuity and spatial homogeneity of moving objects. The proposed multi-layer structure ensembles the joint spatio-temporal and cascade process of perception principles and support efficient and accurate object segmentation
Summary form only given. The problem of streaming data has gained importance in recent years because of advances in hardware technology. The ubiquitous presence of data streams in a number of practical domains has gen...
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Summary form only given. The problem of streaming data has gained importance in recent years because of advances in hardware technology. The ubiquitous presence of data streams in a number of practical domains has generated a lot of research in this area. Example applications include surveillance for terrorist attack, network monitoring for intrusion detection, and others. Problems such as data mining which have been widely studied for traditional data sets cannot be easily solved for the data stream domain. This is because the large volume of data arriving in a stream renders most algorithms to inefficient as most mining algorithms require multiple scans of data which is unrealistic for stream data. More importantly, the characteristics of the data stream can change over time and the evolving pattern needs to be captured. Furthermore, we also need to consider the problem of resource allocation in mining data streams. Due to the large volume and the high speed of streaming data, mining algorithms must cope with the effects of system overload. Thus, how to achieve optimum results under various resource constraints becomes a challenging task. In this talk, the author provides an overview, discusses the issues and focuses on how to mine evolving data streams and perform resource adaptive computation.
Iris is claimed to be one of the best biometrics. We have collected a large data set of iris images, intentionally sampling a range of quality broader than that used by current commercial iris recognition systems. We ...
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Iris is claimed to be one of the best biometrics. We have collected a large data set of iris images, intentionally sampling a range of quality broader than that used by current commercial iris recognition systems. We have re-implemented the Daugman-like iris recognition algorithm developed by Masek. We have also developed and implemented an improved iris segmentation and eyelid detection stage of the algorithm, and experimentally verified the improvement in recognition performance using the collected dataset. Compared to Masek's original segmentation approach, our improved segmentation algorithm leads to an increase of over 6% in the rank-one recognition rate.
This paper deals with a new multi-lane markings detection and tracking system. The proposed system uses multiple cameras positioned differently in order to reduce different kind of perturbations, such as light sensiti...
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This paper deals with a new multi-lane markings detection and tracking system. The proposed system uses multiple cameras positioned differently in order to reduce different kind of perturbations, such as light sensitivity. The algorithm combines robust Kalman filtering and association based on belief theory to achieve multi-object tracking. Thus, the system provides the ability to track lane markings without any assumption on their number. It also proposes a new lane change management. To study this new system, the algorithm has been implemented on an embedded computer equipped with multiple cameras. We present experimental results obtained on a track. These results allow us to show important advantages of this new system and its robustness by comparing it to a classical system.
To improve the anomaly intrusion detection system using system calls, this study focuses on neuro-fuzzy learning using the Soundex algorithm which is designed to change feature selection and variable length data into ...
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To improve the anomaly intrusion detection system using system calls, this study focuses on neuro-fuzzy learning using the Soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern. That is, by changing variable length sequential system call data into a fixed length behavior pattern using the Soundex algorithm, this study conducted backpropagation neural networks with fuzzy membership function. The neuro-fuzzy and N-gram techniques are applied for anomaly intrusion detection of system calls using sendmail data of UNM to demonstrate its performance.
Due to the popularity of the Internet and the powerful computing capability of computers, efficient processing/retrieval of multimedia data has become an important issue. In this paper, we propose a fast video retriev...
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ISBN:
(纸本)0780388747
Due to the popularity of the Internet and the powerful computing capability of computers, efficient processing/retrieval of multimedia data has become an important issue. In this paper, we propose a fast video retrieval algorithm that bases its search core on the statistics of object motion. The algorithm starts with extracting object motions from a shot and then transforms/quantizes them into the form of probability distributions. By choosing the shot that has the largest entropy value among the constituent shots of an unknown query video clip, we execute the first stage video search. By comparing two shots with different lengths, their corresponding motion probability distributions are compared by a discrete Bhattacharyya distance which is designed to measure the similarity between any two distribution functions. In the second stage, we add an adjacent shot (either preceding or subsequent) to perform a finer comparison. Experimental results demonstrate that our fast video retrieval algorithm is powerful in terms of accuracy and efficiency.
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