63054527
Volumetric segmentation of range images of 3D objects using superquadric models
Abstract The problem of part definition, description, and decomposition is central to the shape recognition systems. We present a geometric model-driven framework for segmenting dense range data of complex 3D objects into their constituent parts in terms of surface (biquadrics) and volumetric (superquadrics) primitives, without a priori domain knowledge or stored models. Surface segmentation uses a novel local-to-global iterative regression approach of searching for the best piecewise biquadric description of the data. The region adjacency information, surface discontinuities, and global shape properties are extracted from biquadrics to guide the volumetric segmentation. Superquadric models are recovered by a global-to-local residual-driven procedure, which recursively segments the scene to derive the part-structure. A set of acceptance criteria provide the objective evaluation of intermediate descriptions and decide whether to terminate the procedure, or selectively refine the segmentation. The control module generates hypotheses about superquadric models at clusters of underestimated data and performs controlled extrapolation of part-models by shrinking the global model. Results are presented for real range images of varying complexity, including objects with occluding parts, and scenes where surface segmentation is not sufficient to guide the volumetric segmentation.
38069956
Introduction to the Special Issue on Innovative Applications of Computer Vision
5097328
Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours
The problem of segmenting an image into separate regions and tracking them over time is one of the most significant problems in vision. Terzopoulos et al. (1987) proposed an approach to detect the contour regions of complex shapes, assuming a user selected initial contour not very far from the desired solution. We propose to further explore the information provided by the user's selected points and apply an optimal method to detect contours which allows a segmentation of the image. The method is based on dynamic programming (DP), and applies to a wide variety of shapes. It is exact and not iterative. We also consider a multiscale approach capable of speeding up the algorithm by a factor of 20, although at the expense of losing the guaranteed optimality characteristic. The problem of tracking and matching these contours is addressed. For tracking, the final contour obtained at one frame is sampled and used as initial points for the next frame. Then, the same DP process is applied. For matching, a novel strategy is proposed where the solution is a smooth displacement field in which unmatched regions are allowed while cross vectors are not. The algorithm is again based on DP and the optimal solution is guaranteed. We have demonstrated the algorithms on natural objects in a large spectrum of applications, including interactive segmentation and automatic tracking of the regions of interest in medical images. >
14869645
Multi-modality model-based registration in the cardiac domain
Recent advances in CT technology have made possible the acquisition of high resolution images of the heart in which important anatomical features such as the coronary vessels are visible. The state of the art in MR Imaging techniques, on the other hand, typically provides lower resolution but can facilitate the differentiation of viable (healthy) cardiac tissue from infarcted (injured) tissue. The registration of CT and MR images of the same heart permit the association of anatomy with function. As an example, given an infarcted region, the coronary vessels responsible for irrigating that region may be isolated and targeted for treatment (e.g., coronary bypass). We demonstrate a software package, PROTEUS, which is capable of registering CT and MR images. Since the manifestation of cardiac tissue in the two modalities differs so greatly we rely on features to perform the matching. Specifically, we employ a model-based approach that relies on segmented contours delineating the inner and outer borders of the left ventricle (LV). Given these contours, our technique is completely automatic.
14750394
Global models with parametric offsets as applied to cardiac motion recovery
We introduce a new solid shape model formulation that includes built-in offsets from a base global component (e.g. an ellipsoid) which are functions of the global component's parameters. The offsets provide two features. First, they help to form an expected model shape which facilitates appropriate model data correspondences. Second, they scale with the base global model to maintain the expected shape even in the presence of large global deformations. We apply this model formulation to the recovery of 3-D cardiac motion from a volunteer dataset of tagged-MR images. The model instance is a variation of the hybrid volumetric ventriculoid (HVV), a deformable thick-walled ellipsoid model resembling the left ventricle (LV) of the heart. A unique aspect of of implementation is the employment of constant volume constraints when recovering the cardiac motion. In addition, we present a novel geodesic-like prismoidal tessellation of the model which provides for more stable fits.
11252982
Segmentation of range images as the search for geometric parametric models
Segmentation of range images has long been considered in computer vision as an important but extremely difficult problem. In this paper we present a new paradigm for the segmentation of range images into piecewise continuous surfaces. Data aggregation is performed via model recovery in terms of variable-order bi-variate polynomials using iterative regression. Model recovery is initiated independently in regularly placed seed regions in the image. All the recovered models are potential candidates for the final description of the data. Selection of the models is defined as a quadratic Boolean problem, and the solution is sought by the WTA (winner-takes-all) technique, which turns out to be a good compromise between the speed of computation and the accuracy of the solution. The overall efficiency of the method is achieved by combining model recovery and model selection in an iterative way. Partial recovery of the models is followed by the selection (optimization) procedure and only the “best” models are allowed to develop further.The major novelty of the approach lies in an effective combination of simple component algorithms, which stands in contrast to methods which attempt to solve the problem in a single processing step using sophisticated means. We present the results on several real range images.
15718873
A predictive empirical model for pricing and resource allocation decisions
We present a semi-parametric model that describes pricing behaviors in a market environment, and we show how that model can be used to guide resource allocation and pricing decisions in an autonomous trading agent. We validate our model by presenting experimental results obtained in the Trading Agent Competition for Supply Chain Management.
26822142
Minitrack Introduction
41429895
Explanation-based Failure Recovery
Interactions are inherent in design-type problem-solving tasks where only partially compiled operators are available. Failures arising from such interactions can best be recovered by explaining them in the underlying domain models. In this paper we explain how Explanation-Based Learning provides a framework for recovering in this manner. This approach also alleviates some of the problems associated with the least-commitment approach to design-type problem-solving.
2140816
Toward Comprehensive Real-Time Bidder Support in Iterative Combinatorial Auctions
Many auctions involve selling several distinct items simultaneously, where bidders can bid on the whole or any part of the lot. Such auctions are referred to as combinatorial auctions. Examples of such auctions include truck delivery routes, industrial procurement, and FCC spectrum. Determining winners in such auctions is an NP-hard problem, and significant research is being conducted in this area. However, multiple-round (iterative) combinatorial auctions present significant challenges in bid formulations as well. Because the combinatorial dynamics in iterative auctions can make a given bid part of a winning and nonwinning set of bids without any changes in the bid, bidders are usually not able to evaluate whether they should revise their bid at a given point in time or not. Therefore, in this paper we address various computational problems that are relevant from the bidder's perspective. In particular, we introduce two bid evaluation metrics that can be used by bidders to determine whether any given bid can be a part of the winning allocation and explore their theoretical properties. Based on these metrics, we also develop efficient data structures and algorithms that provide comprehensive information about the current state of the auction atany time, which can help bidders in evaluating their bids and bidding strategies. Our approach uses exponential memory storage but provides fast incremental update for new bids, thereby facilitating bidder support for real-time iterative combinatorial auctions.
1276171
Using Lagrangean Relaxation for Service Location Planning with QoS Constraints in Large-Scale Networks
Current trends in computing indicate that there is a great potential for service oriented computing and similar technologies, such as Cisco's Application Oriented Networks, where services provide a higher-level of abstraction to traditional applications. In such cases, providing and consuming services and establishing a relationship between consumers (users of services) and producers (providers of services) are still challenging and vastly researched aspects. We define a service location planning problem (SLP) that is novel in the approach it takes to match consumers to producers, such that the cost of providing services is minimized while taking into account quality of service constraints of throughput and delay. In previous work, we presented an integer linear programming (ILP) model to formulate the service location planning problem. In this paper, we present Lagrangean Relaxation of the model for large-scale networks. We use the approach of Trick [3] to decrease computational overheads.