18702956
A Dynamic Hidden Markov Random Field Model for Foreground and Shadow Segmentation
This paper proposes a dynamic hidden Markov random field (DHMRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified in the novel dynamic probabilistic model that combines the hidden Markov model (HMM) and the Markov random field (MRF). An efficient approximate filtering algorithm is derived for the DHMRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and edge information: Moreover, models of background, shadow, and edge information are updated adoptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences
18961070
A probabilistic approach for foreground and shadow segmentation in monocular image sequences
This paper presents a novel method of foreground and shadow segmentation in monocular indoor image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. A maximum a posteriori-Markov random field estimation is used to boost the spatial connectivity of segmented regions.
60451548
Switching Hypothesized Measurements: A Dynamic Model with Applications to Occlusion Adaptive Joint Tracking
This paper proposes a dynamic model supporting multimodal state space probability distributions and presents the application of the model in dealing with visual occlusions when tracking multiple objects jointly. For a set of hypotheses, multiple measurements are acquired at each time instant. The model switches among a set of hypothesized measurements during the propagation. Two computationally efficient filtering algorithms are derived for online joint tracking. Both the occlusion relationship and state of the objects are recursively estimated from the history of measurement data. The switching hypothesized measurements (SHM) model is generally applicable to describe various dynamic processes with multiple alternative measurement methods.
15156880
Adaptive Foreground and Shadow Detection in Image Sequences
This paper presents a novel method of foreground segmentation that distinguishes moving objects from their moving cast shadows in monocular image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian belief network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. The notion of Markov random field is used to encourage the spatial connectivity of the segmented regions. The solution is obtained by maximizing the posterior possibility density of the segmentation field.
13971731
Video segmentation based on graphical models
This paper proposes a unified framework for spatiotemporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notions of distance transformation and Markov random field are used to express spatiotemporal constraints. Given consecutive frames, an optimization method is proposed to maximize the conditional probability density of the three fields in an iterative way. Experimental results show that the approach is robust and generates spatiotemporally coherent segmentation results.
8808466
Joint region tracking with switching hypothesized measurements
We propose a switching hypothesized measurements (SHM) model supporting multimodal probability distributions and present the application of the model in handling potential variability in visual environments when tracking multiple objects jointly. For a set of occlusion hypotheses, a frame is measured once under each hypothesis, resulting in a set of measurements at each time instant. A computationally efficient SHM filter is derived for online joint region tracking. Both occlusion relationships and states of the objects are recursively estimated from the history of hypothesized measurements. The reference image is updated adaptively to deal with appearance changes of the objects. The SHM model is generally applicable to various dynamic processes with multiple alternative measurement methods.
35134245
Single Table Access Using Multiple Indexes: Optimization, Execution, and Concurrency Control Techniques
Many data base management systems' query optimizers choose at most one index for accessing the records of a table in a given query, even though many indexes may exist on the table. In spite of the fact that there are some systems which use multiple indexes, very little has been published about the concurrency control or query optimization implications (e.g., deciding how many indexes to use) of using multiple indexes. This paper addresses these issues and presents solutions to the associated problems. Techniques are presented for the efficient handling of record ID lists, elimination of some locking, and determination of how many and which indexes to use. The techniques are adaptive in the sense that the execution strategies may be modified at run-time (e.g., not use some indexes which were to have been used), if the assumptions made at optimization-time (e.g., about selectivities) turn out to be wrong. Opportunities for exploiting parallelism are also identified. A subset of our ideas have been implemented in IBM's DB2 V2R2 relational data base management system.
15440093
Pattern discovery: a data driven approach to decision support
Decision support nowadays is more and more targeted to large scale complicated systems and domains. The success of a decision support system relies mainly on its capability of processing large amounts of data and efficiently extracting useful knowledge from the data, especially knowledge which is previously unknown to the decision makers. With a large scale system, traditional knowledge acquisition models become inefficient and/or more biased, due to the subjectivity of the experts or the pre-assumptions of certain ideas or algorithmic procedures. Today, with the rapid development of computer technologies, the capability of collecting data has been greatly advanced. Data becomes the most valuable resource for an organization. We present a fundamental framework toward intelligent decision support by analyzing a large amount of mixed-mode data (data with a mixture of continuous and categorical values) in order to bridge the subjectivity and objectivity of a decision support process. By considering significant associations of artifacts (events) inherent in the data as patterns, we define patterns as statistically significant associations among feature values represented by joint events or hypercells in the feature space. We then present an algorithm which automatically discovers statistically significant hypercells (patterns) based on: 1) a residual analysis, which tests the significance of the deviation when the occurrence of a hypercell differs from its expectation, and 2) an optimization formulation to enable recursive discovery. By discovering patterns from data sets based on such an objective measure, the nature of the problem domain will be revealed. The patterns can then be applied to solve specific problems as being interpreted or inferred with.
13286603
Privacy in Online Social Networking at Workplace
Employees using social network sites (SNS) at workplace is a fact. As companies are further embracing social media, how if at all does this practice affect the work dynamics? While privacy has been a hot topic in online social network research in general, there is little work investigating the privacy aspect of this practice at workplace. This paper aims at starting the groundwork towards filling the gap. Based on a review of existing literature in social networks and workplace studies, we hypothesize a number of potential privacy issues in this work practice and suggest future research directions in this area.
206733202
Foreground and Shadow Detection Based on Conditional Random Field
This paper presents a conditional random field (CRF) approach to integrate spatial and temporal constraints for moving object detection and cast shadow removal in image sequences. Interactions among both detection (foreground/background/shadow) labels and observed data are unified by a probabilistic framework based on the conditional random field, where the interaction strength can be adaptively adjusted in terms of data similarity of neighboring sites. Experimental results show that the proposed approach effectively fuses contextual dependencies in video sequences and significantly improves the accuracy of object detection.
20945105
Real-Time Moving Vehicle Detection With Cast Shadow Removal in Video Based on Conditional Random Field
This paper presents an approach of moving vehicle detection and cast shadow removal for video based traffic monitoring. Based on conditional random field, spatial and temporal dependencies in traffic scenes are formulated under a probabilistic discriminative framework, where contextual constraints during the detection process can be adaptively adjusted in terms of data-dependent neighborhood interaction. Computationally efficient algorithm has been developed to discriminate moving cast shadows and handle nonstationary background processes for real-time vehicle detection in video streams. Experimental results show that the proposed approach effectively fuses contextual dependencies and robustly detects moving vehicles under heavy shadows even in grayscale video.