We present a new disk scheduling framework to address the needs of a shared multimedia service that provides differentiated multilevel quality-of-service for mixed-media workloads. In such a shared service, requests from different users have different associated performance objectives and utilities, in accordance with the negotiated service-level agreements (SLAs). Service providers typically provision resources only for average workload intensity, so it becomes important to handle workload surges in a way that maximizes the utility of the served requests. We capture the performance objectives and utilities associated with these multiclass diverse workloads in a unified framework and formulate the disk scheduling problem as a reward maximization problem. We map the reward maximization problem to a minimization problem on graphs and, by novel use of graph-theoretic techniques, design a scheduling algorithm that is computationally efficient and optimal in the class of seek-optimizing algorithms. Comprehensive experimental studies demonstrate that the proposed algorithm outperforms other disk schedulers under all loads, with the performance improvement approaching 100% under certain high load conditions. In contrast to existing schedulers, the proposed scheduler is extensible to new performance objectives (workload type) and utilities by simply altering the reward functions associated with the requests.

In this position paper we argue that to effectively address coordination challenges in distributed software development, we need to go beyond making individual development tools more collaborative and design a framework that enables common understanding of the information from different tools and supports loose coupling between them. Stakeholders can go about doing their work in their choice of tools, and as long as these tools adhere to certain interface requirements imposed by the framework, information may be shared across tools in a seamless fashion. The framework should also be able to pick up on cues within the project execution environment, analyze them, and provide useful information as alerts and advisories to stakeholders. Finally, the framework should be adaptive to new tools introduced in the development process as well as to changing project governance needs. We present a preliminary architecture of the framework and discuss candidate technologies to realize it.

Logical reorganization of data and requirements of differentiated QoS in information systems necessitate bulk data migration by the underlying storage layer. Such data migration needs to ensure that regular client I/Os are not impacted significantly while migration is in progress. We formalize the data migration problem in a unified admission control framework that captures both the performance requirements of client I/Os and the constraints associated with migration. We propose an adaptive rate-control based data migration methodology, QoSMig, that achieves the optimal client performance in a differentiated QoS setting, while ensuring that the specified migration constraints are met QoSMig uses both long term averages and short term forecasts of client traffic to compute a migration schedule. We present an architecture based on Service Level Enforcement Discipline for Storage (SLEDS) that supports QoSMig. Our trace-driven experimental study demonstrates that QoSMig provides significantly better I/O performance as compared to existing migration methodologies.

Variability and diverseness among incoming requests to a service hosted on a finite capacity resource necessitates sophisticated request admission control techniques for providing guaranteed quality of service (QoS). We propose in this paper a service time based online admission control methodology for maximizing profits of a service provider. The proposed methodology chooses a subset of incoming requests such that the revenue of the provider is maximized. Admission control decision in our proposed system is based upon an estimate of the service time of the request, QoS bounds, prediction of arrivals and service times of requests to come in the short-term future, and rewards associated with servicing a request within its QoS bounds. Effectiveness of the proposed admission control methodology is demonstrated using experiments with a content-based messaging middleware service.

Inherent subjectivity in user's perception of an image has motivated the use of relevance feedback (RF) in the image desigined output's retrieval process. RF techniques interactively determine the user's query concept, given the user's relevance judgments on a set of images. In this paper we propose a robust technique that utilizes non-relevant images to efficiently discover the relevant search region. A similarity metric, estimated using the relevant images is then used to rank and retrieve database images in the relevant region. The partitioning of the feature space is achieved by using a piecewise linear decision surface that separates the relevant and non-relevant images. Each of the hyperplanes constituting the decision surface is normal to the minimum distance vector from a non-relevant point to the convex hull of relevant points. Experimental results demonstrate significant improvement in retrieval performance for the small feedback size scenario over two well established RF algorithms.

Most interactive "query-by-example" based image retrieval systems utilize relevance feedback from the user for bridging the gap between the user's implied concept and the low-level image representation in the database. However, traditional relevance feedback usage in the context of content-based image retrieval (CBIR) may not be very efficient due to a significant overhead in database search and image download time in client-server environments. In this paper, we propose a CBIR system that efficiently addresses the inherent subjectivity in user perception during a retrieval session by employing a novel idea of intra-query modification and learning. The proposed system generates an object-level view of the query image using a new color segmentation technique. Color, shape and spatial features of individual segments are used for image representation and retrieval. The proposed system automatically generates a set of modifications by manipulating the features of the query segment(s). An initial estimate of user perception is learned from the user feedback provided on the set of modified images. This largely improves the precision in the first database search itself and alleviates the overheads of database search and image download. Precision-to-recall ratio is improved in further iterations through a new relevance feedback technique that utilizes both positive as well as negative examples. Extensive experiments have been conducted to demonstrate the feasibility and advantages of the proposed system.

Learning user perception of an image is a challenging issue in interactive content-based image retrieval (CBIR) systems. These systems employ relevance feedback mechanism to learn user perception in terms of a set of model-parameters and in turn iteratively improve the retrieval performance. Since the quantity of user feedback is expected to be small, learning the user's perception essentially involves parameter estimation with very few training points. We propose a novel, and more efficient method for relevance feedback. Contrary to existing geometric model-based relevance feedback methods, the proposed technique explicitly uses information about irrelevant data points to estimate the parameters of the model. This algorithm iteratively updates the parameters of the similarity metric so as to fit the relevant examples while excluding the irrelevant ones. This is achieved by modifying the weights associated with the relevant examples. Experiments on image and synthetic datasets demonstrate the retrieval effectiveness of the proposed approach.

Content-based Image Retrieval (CBIR) involves retrieving images similar to an example query image in terms of some features extracted from the image. However, inherent subjectivity in user perception of an image results in retrieved images that are largely irrelevant to the user. We propose a novel methodology for efficient understanding of user perception from the query image itself. Our system automatically generates a set of modified images, after the user selects object(s) of interest from the segmented query image. Our goal is to learn the retrieval parameters by modifying the segment-level description of the query image. Segment-level description includes individual segment properties as well as the inter-segment relationships. The user perception is then learnt on the basis of user feedback on this set of modified images. We demonstrate the feasibility and advantages of the proposed approach with examples. The proposed methodology of intra-query learning saves the cost of repeated database search incurred in existing relevance feedback based approaches.

Many content-based image retrieval (CBIR) systems utilize image segmentation for enabling the user to perform object-level database querying. We propose an integrated segmentation technique for interactive image retrieval, that is reasonably accurate and fast. An initial over-segmentation is generated by finding the dominant color modes in the global histogram of the image using the mean-shift algorithm. Edge-based processing is performed at the initial segment boundaries to merge non-obvious segments. Finally segment shapes are regularized using a Hopfield (1985) type neural network to improve their perceptual quality. A scalable implementation is presented for ensuring fast serial execution of the Hopfield network. The entire segmentation process takes less than 10 seconds to segment 128/spl times/192 stock photos on a standard workstation.

A weighted Jacobi smoother-based algebraic technique is proposed for smoothing discrete data, e.g., signal, image or video on grids with arbitrary topology. The energy of discrete data is defined in H/sup 1/-space and a requirement for discrete scale-space theory is suggested based on the nonincrease of energetic norm of the data. A shape-preserving smoothing method is also derived using a combination of Jacobi smoothers. Scale-selective smoothing of the data is achieved by eigenanalysis of the stiffness matrix. Experimental results are shown for isotropic image data.

It is desirable to enhance the fingerprint image for achieving good fingerprint matching performance. In our earlier work, we have proposed a method for learning a set of partitioned least-squares filters from a given set of images and ground truth pairs. In this paper, we propose a refined approach to learn a class of hierarchical filter banks to extend our enhancement technique. The new technique is shown to obtain much better results particularly when the input image is not easily restored using our earlier technique. We evaluate the performance of our new approach to fingerprint enhancement filter design by assessing the effect of enhancement on performance of (i) fingerprint feature extraction and (ii) fingerprint matching.

Fingerprint images contain varying amount of noise because of the limitations of the fingerprint acquisition process. It is often necessary to enhance such noisy fingerprint images so that the features extracted from them are reliable. We propose a novel approach to fingerprint enhancement where a set of filters are learned using the "learn-from-example" paradigm. An expert provides the ground truth information for ridges in a small set of representative fingerprint images. The space of local fingerprint patterns in a small neighborhood is partitioned into a set of expressive yet computationally simple classes. A filter is learnt for each partition by finding the optimal linear mapping (in least-square sense) from the input to the enhanced space. The proposed approach offers distinct performance and speed advantages for a wide variety of fingerprint images.

In this paper, a novel model-based approach is proposed for generating a set of image feature maps (or primal sketches). For each type of feature, a piecewise smooth parametric model is developed to characterize the local intensity function in an image. Projections of the intensity profile onto a set of orthogonal Zernike-moment-generating polynomials are used to estimate model-parameters and, in turn, generate the desired feature map. A small set of moment-based detectors is identified that can extract various kinds of primal sketches from intensity as well as range images. One main advantage of using parametric model-based techniques is that it is possible to extract complete information (i.e., model parameters) about the underlying image feature, which is desirable in many high-level vision tasks. Experimental results are included to demonstrate the effectiveness of proposed feature detectors.

Automatic target recognition (ATR) applications require simultaneously a wide field of view (FOV) for better detection and situation awareness, high resolution for target recognition and threat assessment, and high frame rate for detecting brief events and disambiguating frame-to-frame correlation. Uniformly sampling the entire FOV at recognition resolution is simply wasteful in ATR scenarios with localized regions of interest (ROIs). Foveal data acquisition with space-variant sampling and context-sensitive sensor articulation is highly optimized for active ATR applications. We propose a multiscale local Zernike filter-based front end target detection technique for a commercially feasible foveal sensor topology with piecewise constant resolution profile. Anisotropic heat diffusion is employed for preprocessing of the foveal data. Expansion template matching is used to derive a detection filter that optimizes the discriminant signal-to-noise ratio (SNR). Results are presented with simulated foveal imagery, derived from real uniform acuity FLIR data.

Multiple moving objects, partially occluded objects, or even a single object moving against the background gives rise to discontinuities in the optical flow field in corresponding image sequences. While uniform global regularization based moderately fast techniques cannot provide accurate estimates of the discontinuous flow field, statistical optimization based accurate techniques suffer from excessive solution time. A 'weighted anisotropic' smoothness based numerically robust algorithm is proposed that can generate discontinuous optical flow field with high speed and linear computational complexity. Weighted sum of the first-order spatial derivatives of the flow field is used for regularization. Less regularization is performed where strong gradient information is available. The flow field at any point is interpolated more from those at neighboring points along the weaker intensity gradient component. Such intensity gradient weighted regularization leads to Euler-Lagrange equations with strong anisotropies coupled with discontinuities in their coefficients. A robust multilevel iterative technique, that recursively generates coarse-level problems based on intensity gradient weighted smoothing weights, is employed to estimate discontinuous optical flow field. Experimental results are presented to demonstrate the efficacy of the proposed technique.

A novel model-based unified approach is proposed for generating a set of image feature maps (or primal sketches). For each type of feature, a parametric model is developed to characterize the local intensity function in an image. Projections of intensity profile onto a set of orthogonal Zernike moment-generating polynomials are used to estimate model-parameters and in turn generate the desired feature map. A small set of moment-based detectors is identified that can extract various kinds of primal sketches from intensity as well as range images. One main advantage of using parametric model-based techniques is that it is possible to extract complete information (i.e., model parameters) about the underlying image feature, which is desirable in many high-level vision tasks. Experimental results are included to demonstrate the effectiveness of the proposed feature detectors.>

The paper presents a robust algorithm for computation of optical flow using the principle of conservation of a set of semi-invariant local features that are representatives of local gray-level properties in an image. Specifically, a set of rotation-invariant local orthogonal Zernike moments is used as features. These are inherently integral-based features, and therefore are robust against possible variations of intensity values that may occur over a sequence of images due to sensor noise, varying illumination etc. The 2D local optical flow field is obtained by the singular value decomposition of an overdetermined set of linear equations of velocity field components, resulting from the principle of conservation of features in a small neighborhood. The proposed approach is compared with Horn-Schunck (1981), and Lucas-Kanade's optical flow techniques. Experimental results with synthetic as well as real sequences are presented to demonstrate the effectiveness of the proposed approach.>

The paper presents a new parametric model-based approach to high-precision composite edge detection using orthogonal Zernike moment-based operators. It deals with two types of composite edges: (a) generalized step and (b) pulse/staircase edges. A 2-D generalized step edge is modeled in terms of five parameters: two gradients on two sides of the edge, the distance from the center of the candidate pixel, the orientation of the edge and the step size at the location of the edge. A 2-D pulse/staircase edge is modeled in terms of two steps located at two positions within the mask, and the edge orientation. A pulse edge is formed if the steps are of opposite polarities whereas a staircase edge results from two steps having the same polarity. Two complex and two real Zernike moment-based masks are designed to determine parameters of both the 2-D edge models. For a given edge model, estimated parameter values at a point are used to detect the presence or absence of that type of edge. Extensive noise analysis is performed to demonstrate the robustness of the proposed operators. Experimental results with intensity and range images are included to demonstrate the efficacy of the proposed edge detection technique as well as to compare its performance with the geometric moment-based step edge detection technique and Canny's (1986) edge detector.

A new approach to range image segmentation is presented. The proposed approach involves two phases in which the region and edge information detected using a set of orthogonal Zernike moment-based operators are combined to provide robust segmentation of range images. In the first phase, each range image point is characterized by the surface normal vector and the depth value at that point. A surface feature-based clustering of range image points yields its initial region-based segmentation. This initial segmentation phase often produces oversegmented images. In the second phase of the proposed technique, the oversegmented image is resegmented by appropriately merging adjacent regions using the edge information to produce final segmentation. One attractive characteristic of the proposed technique is that the same set of three moment-based operators is used to extract both surface and edge features. Thus only three convolution operations are needed at an image point to compute all the desired surface and edge features associated with that point. The performances of the proposed Zernike moment-based operators in surface and edge feature detection are theoretically analyzed.>

Presents a new approach to detect step edges with subpixel accuracy. The proposed approach is based on a set of orthogonal complex moments of the image known as Zernike moments. An ideal 2-D step edge is modeled in terms of four parameters: the background gray level, the step size, the distance of the edge from the center of the mask, and the orientation of the edge. Discrete Zernike moments are used to obtain a total of three masks to compute all the edge parameters for subpixel detection. For pixel-level edge detection only two masks (one real and one complex) are required. The theoretical analysis of the influence of noise on the location and the orientation of an edge is presented. This analysis reveals that the accuracy of the proposed approach is virtually unaffected by the additive noise. Experimental results are presented to demonstrate the efficacy of the proposed technique.>

The authors proposed a novel Zernike moment-based generalized step edge detection method which can be used for segmenting range and intensity images. A generalized step edge detector is developed to identify different kinds of edges in range images. These edge maps are thinned and linked to provide final segmentation. A generalized edge is modeled in terms of five parameters: orientation, two slopes, one step jump at the location of the edge, and the background gray level. Two complex and two real Zernike moment-based masks are required to determine all these parameters of the edge model. Theoretical noise analysis is performed to show that these operators are quite noise tolerant. Experimental results are included to demonstrate edge-based segmentation technique.>