2798845
Modeling and querying uncertain spatial information for situational awareness applications
Situational awareness (SA) applications monitor the real world and the entities therein to support tasks such as rapid decision-making, reasoning, and analysis. Raw input about unfolding events may arrive from variety of sources in the form of sensor data, video streams, human observations, and so on, from which events of interest are extracted. Location is one of the most important attributes of events, useful for a variety of SA tasks. In this paper, we propose an approach to model and represent (potentially uncertain) event locations described by human reporters in the form of free text. We analyze several types of spatial queries of interest in SA applications. Our experimental evaluation demonstrates the effectiveness of our approach.
9332901
On-Demand Information Portals for Disaster Situations
This paper describes our work on developing technology for rapidly assembling information portals that provide integrated access to and analysis of information from multiple sources in the case of any disaster. Many recent disasters (the S.E. Asian Tsunamis, the London subway bombings, the Katrina hurricane, to name a few) have demonstrated that a lot of valuable information becomes available in the hours and days immediately following the disaster, and such information is indeed valuable to disaster managers or even citizens in their response. In this paper we describe our work on developing information portals for disasters in general; we describe many key information processing capabilities and challenges that we consider important in such portals and also describe our approach to developing such capabilities.
9927329
CAMAS: a citizen awareness system for crisis mitigation
1 ITR-Rescue, 5251 California Avenue, Suite 210, Irvine CA 92612-2815, Phone: 949-824-1147 http://www.itr-rescue.org 2 Union Bank Building, 400 Oceangate, Suite 1050, Long Beach CA 90802 Phone: 562-628-1675, http://imagecatinc.com ABSTRACT This demo paper provides a brief description of the intuition and design philosophy of CAMAS, one of the main testbeds being developed in the context of the RESCUE (Responding to the Unexpected) project [9]. The goal of our work is to enhance the mitigation capabilities of first responders in the event of a crisis by dramatically transforming their ability to collect, store, analyze, interpret, share and disseminate data. CAMAS specifically is a system designed to allow a variety of users, including the average citizen to report incidents and potentially hazardous situations, analyze and evaluate these reports and notify appropriate personnel for further action. The multidisciplinary approach incorporates a variety of information technologies: networks; distributed systems; databases; image and video processing; GIS; and machine learning, together with subjective information obtained through social science. Besides providing an overview of the CAMAS architecture, we describe the demonstration platform and specific experiments that will illustrate issues of event extraction, ranking, access control and visualization.
1599574
Interactive Refinement of Filtering Queries on Streaming Intelligence Data
Intelligence analysis involves routinely monitoring and correlating large amount of data streaming from multiple sources. In order to detect important patterns, the analyst normally needs to look at data gathered over a certain time window. Given the size of data and rate at which it arrives, it is usually impossible to manually process every record or case. Instead, automated filtering (classification) mechanisms are employed to identify information relevant to the analyst’s task. In this paper, we present a novel system framework called FREESIA (Filter REfinement Engine for Streaming InformAtion) to effectively generate, utilize and update filtering queries on streaming data.
1141862
Integrating Similarity Retrieval and Skyline Exploration Via Relevance Feedback
Similarity retrieval have been widely used in many practical search applications. A similarity query model can be viewed as a logical combination of a set of similarity predicates. A user can initialize a query model, but model parameters or the model itself may be inadequately specified. As a result, a retrieval system cannot guarantee that it has presented all the relevant tuples to the user. In this paper, we propose a framework that integrates the similarity retrieval and skyline exploration. Using the relevance feedback as a way to constrain the search space, our framework can intelligently explore only a necessary portion of data that contains all the relevant tuples. Our framework is also flexible enough to incorporate model refinement techniques to retrieving relevant results as early as possible.
14652056
A framework for refining similarity queries using learning techniques
In numerous applications that deal with similarity search, a user may not have an exact idea of his information need and/or may not be able to construct a query that exactly captures his notion of similarity. A promising approach to mitigate this problem is to enable the user to submit a rough approximation of the desired query and use the feedback on the relevance of the retrieved objects to refine the query. In this paper, we explore such a refinement strategy for a general class of SQL similarity queries. Our approach casts the refinement problem as that of learning concepts using examples. This is achieved by viewing the tuples on which a user provides feedback as a labeled training set for a learner. Under this setup, SQL query refinement consists of two learning tasks, namely learning the structure of the SQL query and learning the relative importance of the query components. The paper develops appropriate machine learning approaches suitable for these two learning tasks. The primary contribution of the paper is a general refinement framework that decides when each learner is invoked in order to quickly learn the user query. Experimental analyses over many real life datasets and queries show that our strategy outperforms the existing approaches significantly in terms of retrieval accuracy and query simplicity.
8131766
Segmentation of a piece-wise planar scene from perspective images
We study and compare two novel embedding methods for segmenting feature points of piece-wise planar structures from two (uncalibrated) perspective images. We show that a set of different homographies can be embedded in different ways to a higher-dimensional real or complex space, so that each homography corresponds to either a complex bilinear form or a real quadratic form. Each embedding reveals different algebraic properties and relations of homographies. We give a closed-form segmentation solution for each case by utilizing these properties based on subspace-segmentation methods. These theoretical results show that one can intrinsically segment a piece-wise planar scene from 2-D images without explicitly performing any 3-D reconstruction. The resulting segmentation may make subsequent 3-D reconstruction much better-conditioned. We demonstrate the proposed methods with some convincing experimental results.
9330524
Structure and pose from single images of symmetric objects with applications to robot navigation
In this paper, we provide a principled and unified explanation how knowledge in global 3-D structural invariants, typically captured by a group action on a symmetric structure, can significantly facilitate the task of reconstructing a 3-D scene from one or more images. More importantly, the "absolute" pose between the camera frame and the canonical frame that symmetric objects (e.g., buildings, hallways) provide us overwhelming clues to their orientation and position. We give the necessary and sufficient conditions under which this pose can be uniquely determined, and when such conditions are not satisfied, exactly to what extent this pose can be recovered. We show how algorithms from conventional multiple-view geometry, after properly modified and extended, can be effectively applied to perform such recovery. Since the structure, pose and even camera calibration can be recovered from a single image; the techniques naturally apply to vision-based robot navigation where global position and orientation is important.
61335921
Minimum effective dimension for mixtures of subspaces: a robust GPCA algorithm and its applications
We propose a robust model selection criterion for mixtures of subspaces called minimum effective dimension (MED). Previous information-theoretic model selection criteria typically assume that data can be modelled with a parametric model of certain (possibly differing) dimension and a known error distribution. However, for mixtures of subspaces with different dimensions, a generalized notion of dimensionality is needed and hence introduced in this paper. The proposed MED criterion minimizes this geometric dimension subject to a given error tolerance (regardless of the noise distribution). Furthermore, combined with a purely algebraic approach to clustering mixtures of subspaces, namely the generalized PCA (GPCA), the MED is designed to also respect the global algebraic and geometric structure of the data. The result is a non-iterative algorithm called robust GPCA that estimates from noisy data an unknown number of subspaces with unknown and possibly different dimensions subject to a maximum error bound. We test the algorithm on synthetic noisy data and in applications such as motion/image/video segmentation.
17940922
Symmetry-based 3-D reconstruction from perspective images
Symmetry is an important geometric cue for 3-D reconstruction from perspective images. In this paper, we introduce a unified theoretical framework for extracting poses and structures of 2-D symmetric patterns in space from calibrated images. The framework uniformly encompasses all three fundamental types of symmetry, i.e., reflective, rotational, and translational, based on a systematic study of the homography groups in image induced by the symmetry groups in space. We claim that if a planar object admits rich enough symmetry, no 3-D geometric information is lost through perspective imaging. Based on two fundamental principles that utilize common spatial relations among symmetric objects, we have developed a prototype system which can automatically extract and segment multiple 2-D symmetric patterns present in a single perspective image. The result of such a segmentation is a hierarchy of new geometric primitives, called symmetry cells and complexes, whose 3-D structures and poses are fully recovered. Finally, we propose a new symmetry-based matching technique, which can effectively establish correspondences among the extracted symmetry cells across multiple images. We demonstrate the application of the proposed algorithms on image segmentation, matching, and 3-D reconstruction with extensive experimental results. The algorithms and systems are more accurate and easier to implement than existing point-or line-based methods.
7189578
The Kalman Filter as the optimal linear minimum mean-squared error multiuser CDMA detector
It is shown that a first-order linear state-space model applies to the asynchronous code-division multiple-access (CDMA) channel, and thus the Kalman filter produces symbol estimates with the minimum mean-squared error (MMSE) among all linear filters, in long- or short-code systems for a given detection delay. This result may be used as a benchmark against which to compare the performance of other linear detectors in asynchronous channels. It also reveals that a time-varying recursive filter with a fixed and finite complexity implements the fixed-lag linear MMSE (LMMSE) detector, which hitherto has been assumed to require a processing window (and hence complexity) that grows with time.