27027454
Shape-Guided Split and Merge of Image Regions
A method for deformable shape-based image segmentation is described. Regions in an image are merged together and/or split apart, based on their agreement with an a priori distribution on the global deformation parameters for a shape template. Perceptually-motivated criteria are used to determine where and how to split regions, based on the local shape properties of the region group's bounding contour. In general, finding the globally optimal region partition for an image is an NP hard problem; therefore, two approximation strategies are employed: the highest confidence first algorithm and shape indexing trees. Experiments show that the speedup obtained through use of the approximation strategies is significant, while accuracy of segmentation remains high. Once trained, the system autonomously segments shapes from the background, while not merging them with adjacent objects or shadows.
12537942
Deformable shape detection and description via model-based region grouping
A method for deformable shape detection and recognition is described. Deformable shape templates are used to partition the image into a globally consistent interpretation, determined in part by the minimum description length principle. Statistical shape models enforce the prior probabilities on global, parametric deformations for each object class. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with adjacent objects or shadows. The formulation can be used to group image regions based on any image homogeneity predicate; e.g., texture, color or motion. The recovered shape models can be used directly in object recognition. Experiments with color imagery are reported.
45704904
Automatic Deformable Shape Segmentation for Image Database Search Applications
A method for shape based image database indexing is described. Deformable shape templates are used to group color image regions into globally consistent configurations. A statistical shape model is used to enforce the prior probabilities on global, parametric deformations for each object class. The segmentation is determined in part by the minimum description length (MDL) principle. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with adjacent objects or shadows. The formulation can be used to group image regions based on any image homogeneity predicate; e.g., texture, color, or motion. Preliminary experiments in color segmentation and shape-based retrieval are reported.
32876579
Medical image segmentation and retrieval via deformable models
A new method based on deformable shape models for medical image segmentation is described. Experiments for blood cell micrographs have been conducted to verify the accuracy of the shape model-based segmentation and object shape description method. The cell segmentation method does not require user input for initialization. Coherence information between cells is utilized via a globally consistent cost function. The proposed segmentation method can be used in automated analysis for images of stained blood smear and segmentation of other medical structures. A method for shape population-based retrieval is also described. Results of population-based image queries for a database of blood cell micrographs are shown.
13633204
An expiration age-based document placement scheme for cooperative Web caching
The sharing of caches among proxies is an important technique to reduce Web traffic, alleviate network bottlenecks, and improve response time of document requests. Most existing work on cooperative caching has been focused on serving misses collaboratively. Very few have studied the effect of cooperation on document placement schemes and its potential enhancements on cache hit ratio and latency reduction. We propose a new document placement scheme which takes into account the contentions at individual caches in order to limit the replication of documents within a cache group and increase document hit ratio. The main idea of this new scheme is to view the aggregate disk space of the cache group as a global resource of the group and uses the concept of cache expiration age to measure the contention of individual caches. The decision of whether to cache a document at a proxy is made collectively among the caches that already have a copy of this document. We refer to this new document placement scheme as the Expiration Age-based scheme (EA scheme). The EA scheme effectively reduces the replication of documents across the cache group, while ensuring that a copy of the document always resides in a cache where it is likely to stay for the longest time. We report our study on the potentials and limits of the EA scheme using both analytic modeling and trace-based simulation. The analytical model compares and contrasts the existing (ad hoc) placement scheme of cooperative proxy caches with our new EA scheme and indicates that the EA scheme improves the effectiveness of aggregate disk usage, thereby increasing the average time duration for which documents stay in the cache. The trace-based simulations show that the EA scheme yields higher hit rates and better response times compared to the existing document placement schemes used in most of the caching proxies.
6353666
ClusterMap: labeling clusters in large datasets via visualization
With the rapid increase of data in many areas, clustering on large datasets has become an important problem in data analysis. Since cluster analysis is a highly iterative process, cluster analysis on large datasets prefers short iteration on a relatively small representative set. Thus, a two-phase framework "sampling/summarization - iterative cluster analysis" is often applied in practice. Since the clustering result only labels the small representative set, there are problems with extending the result to the entire large dataset, which are almost ignored by the traditional clustering research. This extending is often named as labeling process. Labeling irregular shaped clusters, distinguishing outliers and extending cluster boundary are the main problems in this stage. We address these problems and propose a visualization-based approach to dealing with them precisely. This approach partially involves human into the process of defining and refining the structure "ClusterMap". Based on this structure, the ClusterMap algorithm scans the large dataset to adapt the boundary extension and generate the cluster labels for the entire dataset. Experimental result shows that ClusterMap can preserve cluster quality considerably with low computational cost, compared to the distance-comparison-based labeling algorithms.
17453773
Automatic 3D to 2D registration for the photorealistic rendering of urban scenes
This paper presents a novel and efficient algorithm for the 3D range to 2D image registration problem in urban scene settings. Our input is a set of unregistered 3D range scans and a set of unregistered and uncalibrated 2D images of the scene. The 3D range scans and 2D images capture real scenes in extremely high detail. A new automated algorithm calibrates each 2D image and computes an optimized transformation between the 2D images and 3D range scans. This transformation is based on a match of 3D with 2D features that maximizes an overlap criterion. Our algorithm attacks the hard 3D range to 2D image registration problem in a systematic, efficient, and automatic way. Images captured by a high-resolution 2D camera, that moves and adjusts freely, are mapped on a centimeter-accurate 3D model of the scene providing photorealistic renderings of high quality. We present results from experiments in three different urban settings.
15148208
Resilient trust management for Web service integration
In a distributed Web service integration environment, the selection of Web services should be based on their reputation and quality-of-service (QoS). Various trust models for web services have been proposed to evaluate the reputation of Web services/service providers. Current mechanisms are based on tracing the feedbacks to the past behaviors of Web services. However, very few of them consider the robustness and attack-resiliency of the trust models. In this paper, we present an attack resilient distributed trust management system in a Web service management environment. The proposed attack resilient trust model uses two vectors to capture the behavior and the trustworthiness of a Web service/service provider based on our analysis on the possible attacks against the trust models. We also present a set of experiments that show the effectiveness of our trust model in detecting malicious behavior of service providers.
6609876
MobiEyes: A Distributed Location Monitoring Service Using Moving Location Queries
With the growing popularity and availability of mobile communications, our ability to stay connected while on the move is becoming a reality instead of science fiction as it was just a decade ago. An important research challenge for modern location-based services is the scalable processing of location monitoring requests on a large collection of mobile objects. The centralized architecture, though studied extensively in literature, would create intolerable performance problems as the number of mobile objects grows significantly. This paper presents a distributed architecture and a suite of optimization techniques for scalable processing of continuously moving location queries. Moving location queries can be viewed as standing location tracking requests that continuously monitor the locations of mobile objects of interest and return a subset of mobile objects when certain conditions are met. We describe the design of MobiEyes, a distributed real time location monitoring system in a mobile environment. The main idea behind the MobiEyes' distributed architecture is to promote a careful partition of a real time location monitoring task into an optimal coordination of server-side processing and client-side processing. Such a partition allows evaluating moving location queries with a high degree of precision using a small number of location updates, thus providing highly scalable location monitoring services. A set of optimization techniques are used to limit the amount of computation to be handled by the mobile objects and enhance the overall performance and system utilization of MobiEyes. Important metrics to validate the proposed architecture and optimizations include messaging cost, server load, and amount of computation at individual mobile objects. We evaluate the scalability of the MobiEyes location monitoring approach using a simulation model based on a mobile setup. Our experimental results show that MobiEyes can lead to significant savings in terms of server load and messaging cost when compared to solutions relying on central processing of location information