In this paper we present a new feature extraction methodology for color texture recognition. It is based on the covariance of 2nd-order statistical features in the wavelet domain of the color channels of the images an...
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In this paper, we present a new feature extraction methodology for color texture recognition. It is based on the covariance of 2/sup nd/-order statistical features in the wavelet domain of the color channels of the im...
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In this paper, we present a new feature extraction methodology for color texture recognition. It is based on the covariance of 2/sup nd/-order statistical features in the wavelet domain of the color channels of the images and it is named as color wavelet covariance (CWC). The experimentation showed that the CWC features could be used effectively for texture representation even when illumination varies. The use of the linear K-L (Karhunen-Loeve) transformation of the RGB color space for the extraction of the CWC features resulted in a performance that was comparable to the one achieved with more complex non-linear color transformations. The recognition accuracy tested with texture mosaics reached an average of 86%. Using images acquired under varying illumination the performance of the CWC features on the K-L space reached an average of 88%.
We present the general purpose system architecture OSCAR (Operating System for the Control of Autonomous Robots) for mobile robots that completely relies on a software framework embedding several component types and t...
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Asynchronous real-time distributed systems are inherently non-deterministic. To deal with such non-determinism's, we have developed a family of proactive resource management algorithms that support benefit-functio...
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Asynchronous real-time distributed systems are inherently non-deterministic. To deal with such non-determinism's, we have developed a family of proactive resource management algorithms that support benefit-function based, end-to-end QoS management. This paper describes a middleware implementation of these algorithms, called Choir. The Choir middleware allows the user express the task end-to-end timeliness requirements using Jensen's benefit functions. Furthermore, the middleware system can transparently replicate, and possibly migrate the computational subtasks to conquer uncertainties such as workload fluctuations, changes of system resources, so that the system aggregate benefit is maximized. Initial experimental results suggest the effectiveness the Choir middleware.
We pertain to the recognition of textural regions for color video analysis. The proposed scheme uses the covariance of 2/sup nd/-order statistics on the wavelet domain, between the different color channels of the vide...
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We pertain to the recognition of textural regions for color video analysis. The proposed scheme uses the covariance of 2/sup nd/-order statistics on the wavelet domain, between the different color channels of the video frames. These features, named as color wavelet covariance (CWC), are used as color textural descriptors. A support vector machine was chosen for the classification of the CWC feature vectors. Experiments were conducted using both animated Vistex texture mosaics and standard video clips. The estimated average accuracy ranged from 90% to 97%. The results show that the proposed methodology could efficiently be used in various multimedia applications as a complete supervised color texture recognition system.
We present two classes of distributed algorithms called DRBA and DOBA, for decentralized, proactive resource allocation in asynchronous real-time distributed systems. The objective of the algorithms is to maximize agg...
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We present two classes of distributed algorithms called DRBA and DOBA, for decentralized, proactive resource allocation in asynchronous real-time distributed systems. The objective of the algorithms is to maximize aggregate application benefit and deadline-satisfied ratio for a user-specified future time interval. Since determining the optimal allocation is computationally intractable, the algorithms heuristically compute near-optimal allocations in polynomial-time. While the DRBA algorithms analyze subtask response times to determine allocation decisions which are computationally expensive, the DOBA algorithms analyze processor overloads to compute their decisions in a much faster way. Within each class, we present three algorithms that differ in the way they tolerate end-host failures: no reallocation upon failure, stateless reallocation, and stateful reallocation.
In this paper, we present a MAC-layer packet scheduling algorithm, called BPA, for real-time switched Ethernet networks. BPA considers a message model where trans-node application-level messages have end-to-end timeli...
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In this paper, we present a MAC-layer packet scheduling algorithm, called BPA, for real-time switched Ethernet networks. BPA considers a message model where trans-node application-level messages have end-to-end timeliness requirements that are specified using Jensen's benefit functions. The objective of BPA is to maximize the aggregate message-level benefit. The algorithm reasons that this objective can be achieved by maximizing aggregate packet-level benefit, where packets of messages are allowed to inherit benefit functions of their parent messages. BPA thus solves a non-preemptive packet scheduling problem. Since this problem is NP-hard, BPA heuristically computes packet schedules to maximize aggregate benefit, incurring a worst-case computational complexity of O(n/sup 2/). This is better than the O(n/sup 3/) complexity of the previously known best algorithm (called CMA) for the same problem. Further, our experimental studies show that BPA performs as good as CMA for a broad set of benefit functions, and significantly outperforms CMA for some benefit functions. Furthermore, we observe that BPA yields lower missed-deadline ratio than CMA when message arrival density increases.
In this paper we present a new probabilistic feature-based approach to multi-hypothesis global localization and pose tracking. Hypotheses are generated using a constraint-based search in the interpretation tree of pos...
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In this paper we present a new probabilistic feature-based approach to multi-hypothesis global localization and pose tracking. Hypotheses are generated using a constraint-based search in the interpretation tree of possible local-to-global pairings. This results in a set of robot location hypotheses of unbounded accuracy. For tracking, the same constraint-based technique is used. It performs track splitting as soon as location ambiguities arise from uncertainties and sensing. This yields a very robust localization technique which can deal with significant errors from odometry, collisions and kidnapping. Simulation experiments and first tests with a real robot demonstrate these properties at very low computational cost. The presented approach is theoretically sound which makes that the only parameter is the significance level on which all statistical decisions are taken.
The design flow used nowadays for embedded systems is quite inert and contains many gaps and inhomogeneities. This fact causes additional errors and makes the realization of new projects, with increasing time and cost...
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ISBN:
(纸本)0780365763
The design flow used nowadays for embedded systems is quite inert and contains many gaps and inhomogeneities. This fact causes additional errors and makes the realization of new projects, with increasing time and cost constraints, even more difficult. Here, we present a full model-based approach for the development of embedded systems with data and event driven parts, and illustrate its different advantages over the classical design flow. Using a model, from the early stages of the development a simulation makes it possible to verify the correctness of the design. The validated model is an executable specification of the desired system. The implementation is then made automatically, by means of auto code generators. The model based approach presented is evaluated in a case study on a real world example, an electronic control unit (ECU) for a hydrostatic front wheel drive of a truck.
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