13249098
Automatic alignment of large-scale aerial rasters to road-maps
This paper introduces a practical approach to register large-scale GIS imagery to a database of road vectors automatically. The proposed approach breaks the global alignment problem into a set of localized domains (tiles). Within each tile, the displacement between imagery and vectors is approximated by a translation. Finally, a global thin-plate-spline warp based on these local approximations is applied to register the imagery to the vector data.
 The critical step in this approach is a fully automatic algorithm to compute the best imagery-to-vectors translation within a tile. The proposed algorithm performs vector-guided extraction of road features, aggregates features obtained in the neighborhood of multiple vectors, and then estimates the best translation through a least-squares optimization applied to a selected subset of the aggregated features. It also computes a confidence value for each processed image tile, so that a human operator can easily find out the places where the automatic approach has encountered difficulties, if necessary. The algorithm has been tested on hundreds of production satellite images of different countries. It has correctly registered over 80 percent of the imagery, and consistently reported low confidence values for the rest.
5670917
Improving the Robustness of Language Models - UIUC TREC 2003 Robust and Genomics Experiments
In this paper, we report our experiments in the TREC 2003 Genomics Track and the Robust Track. A common theme that we explored is the robustness of a basic language modeling retrieval approach. We examine several aspects of robustness, including robustness in handling different types of queries, different types of documents, and optimizing performance for difficult topics. Our basic retrieval method is the KL-divergence retrieval model with the two-stage smoothing method plus a mixture model feedback method. In the Genomics IR track, we propose a new method for modeling semi-structured queries using language models, which is shown to be more robust and effective than the regular query model in handling gene queries. In the Robust track, we experimented with two heuristic approaches to improve the robustness in using language models for pseudo feedback.
5471705
Probabilistic Models for Expert Finding
A common task in many applications is to find persons who are knowledgeable about a given topic (i.e., expert finding). In this paper, we propose and develop a general probabilistic framework for studying expert finding problem and derive two families of generative models (candidate generation models and topic generation models) from the framework. These models subsume most existing language models proposed for expert finding. We further propose several techniques to improve the estimation of the proposed models, including incorporating topic expansion, using a mixture model to model candidate mentions in the supporting documents, and defining an email count-based prior in the topic generation model. Our experiments show that the proposed estimation strategies are all effective to improve retrieval accuracy.
9299231
Language Models for Expert Finding--UIUC TREC 2006 Enterprise Track Experiments
In this paper, we report our experiments in the TREC 2006 Enterprise Track. Our focus is to study a language model for expert finding. We extend an existing language model for expert retrieval in three aspects. First, we model the document-expert association using a mixure model instead of name matching heuristics as in the existing work; such a mixture model allows us to put different weights on email matching and name matching. Second, we propose to model the prior of an expert based on the counts of email matches in the supporting documents instead of using uniform prior as in the previous work. Finally, we perform topic expansion and generalize the model from computing the likelihood to computing the cross entropy. Our experiments show that the first two extensions are more effective than the third extension, though when optimized, they all seem to be effective.
16418680
Term feedback for information retrieval with language models
In this paper we study term-based feedback for information retrieval in the language modeling approach. With term feedback a user directly judges the relevance of individual terms without interaction with feedback documents, taking full control of the query expansion process. We propose a cluster-based method for selecting terms to present to the user for judgment, as well as effective algorithms for constructing refined query language models from user term feedback. Our algorithms are shown to bring significant improvement in retrieval accuracy over a non-feedback baseline, and achieve comparable performance to relevance feedback. They are helpful even when there are no relevant documents in the top.
7037192
Interactive material replacement in photographs
Material replacement has wide application throughout the entertainment industry, particularly for post-production make-up application or wardrobe adjustment. More generally, any low-cost mock-up object can be processed to have the appearance of expensive, high-quality materials. We demonstrate a new system that allows fast, intuitive material replacement in photographs. We extend recent work in object selection and fast texture synthesis, as well as develop a novel approach to shape-from-shading capable of handling objects with albedo changes. Each component of our system runs with interactive speed, allowing for easy experimentation and refinement of results.
27646240
Mining User Position Log for Construction of Personalized Activity Map
Consider a scenario in which a smart phone automatically saves the user's positional records for personalized location-based applications. The smart phone will infer patterns of user activities from the historical records and predict user's future movements. In this paper, we present algorithms for mining the evolving positional logs in order to identify places of significance to user and representative paths connecting these places, based on which a personalized activity map is constructed. In addition, the map is designed to contain information of speed and transition probabilities, which are used in predicting the user's future movements. Our experiments show that the user activity map well matches the actual traces and works effectively in predicting user's movements.
29388694
A fuzzy logic approach for detection of video shot boundaries
Video temporal segmentation is normally the first and important step for content-based video applications. Many features including the pixel difference, colour histogram, motion, and edge information etc. have been widely used and reported in the literature to detect shot cuts inside videos. Although existing research on shot cut detection is active and extensive, it still remains a challenge to achieve accurate detection of all types of shot boundaries with one single algorithm. In this paper, we propose a fuzzy logic approach to integrate hybrid features for detecting shot boundaries inside general videos. The fuzzy logic approach contains two processing modes, where one is dedicated to detection of abrupt shot cuts including those short dissolved shots, and the other for detection of gradual shot cuts. These two modes are unified by a mode-selector to decide which mode the scheme should work on in order to achieve the best possible detection performances. By using the publicly available test data set from Carleton University, extensive experiments were carried out and the test results illustrate that the proposed algorithm outperforms the representative existing algorithms in terms of the precision and recall rates.
7764302
Cognitive personal positioning based on activity map and adaptive particle filter
This paper presents a cognitive approach for a reliable yet battery-friendly personal positioning. A user's position is learned from both historical log and possible measurements. Firstly, user's past activities recorded in the log are summarized into an activity map. Accordingly, a user-habit guided particle filtering algorithm is presented for position prediction. Specifically, our algorithm makes reference to the map to determine the most probable correct position, smoothed with occasional measurement. User's current position is modeled probabilistically by a collection of particles and her future moves are modeled with a tendency to follow a familiar path on the map; The estimate is then smoothed by Bayesian filtering. We also allow the number of particles to vary according to user's position in the map. Thus, along with better insights about user's movement experience, our approach can learn from the past and potentially improve the quality of estimates. Our experiments show that this adaptive filtering model using the activity map can deal with non-linear behaviors rather effectively. The new cognitive scheme can indeed track the user's position with a high degree of accuracy. Moreover, the algorithms exhibit low computational complexities, making them well suited for applications on wearable computers.
8868065
Controlling Uncertainty in Personal Positioning at Minimal Measurement Cost
One interesting scenario in personal positioning involves an energy-conscious mobile user who tries to obtain estimates about his positions with sufficiently high confidence while consuming as little battery energy as possible. Besides obtaining estimates directly from a position measuring device, the user can rely on extrapolative calculations based on a user movement model and a known initial estimate. Because each measuring probe usually incurs a substantially higher cost than the extrapolative calculation, the objective is to minimize the overall cost of the measurement probes. 
 
Assuming that the user moves at a normally-distributed velocity, we consider two scenarios which differ in the probing devices used. In the first scenario, only one probing device is used. In this case, the aim is to minimize the total number of probes required. In the second scenario, two types of positioning devices are given, where one type of devices offers a higher positioning precision, but also at a greater probing cost. In this case, the aim is to choose an optimal combination of probes from the two types of devices. 
 
For both scenarios, we present algorithms for determining the minimum-cost probing sequences. The algorithms are computationally efficient in reducing the searching space of all possible probing sequences. Our approach is based on Kalman Filtering theory which allows to integrate estimates obtained from the measurements and the extrapolative calculations. The variances in the estimates can provably stay below the specified level throughout the journey. 
 
To the best of our knowledge, these results appear to be the first that uses a mathematically rigorous approach to minimize the probing cost while guaranteeing the quality of estimates in personal positioning.
8593487
Behavioral Consistency Extraction for Face Verification
According to the preferred embodiment of the present invention pull-up/pull-down circuits are provided that use transistors with different threshold voltages to assure power-up to the correct predetermined state. These circuits have the ability to hold a node up or down while drawing very little DC current. In one embodiment a pull-up/pull-down circuit is provided that powers up to a first state with the pull-up node high and the pull-down node low, and that can be toggled from one state to another. A second embodiment provides a pull-up or pull-down circuit that powers up to the desired state and can be disabled by pulling the pull-up node low or pulling the pull-down node high. The circuits remain disabled until the power to the circuit is cycled.