An unsupervised keyword extraction method based on corpora is proposed. After representing each document in the collection by a fuzzy set of candidate keywords, the problem is translated into finding appropriate fuzzy...
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An unsupervised keyword extraction method based on corpora is proposed. After representing each document in the collection by a fuzzy set of candidate keywords, the problem is translated into finding appropriate fuzzy membership degree for each candidate. In order to determine the membership degrees, first, all terms of the vocabulary are mapped into a two-dimensional space called class-collection map by obtaining two newly proposed fuzzy measures called fuzzy significance and fuzzy relevance for each term. At the second step, the mapped terms are grouped into three categories, namely, features, keywords and stopwords that are discriminated by their contribution to the meaning of the documents. Instead of a clustering approach for grouping purpose, a fuzzy rule base is provided. The method is independent of the language and data dimensionality. It does not require the use of dictionary, thesaurus nor natural language processing.
This paper proposes a new approach to beat detection and prediction in music. Recurrent timing networks are used to detect and predict periodicities in an onset stream and are contained within nodes that compete for s...
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This paper proposes a new approach to beat detection and prediction in music. Recurrent timing networks are used to detect and predict periodicities in an onset stream and are contained within nodes that compete for selection as the best beat hypothesis. Beat prediction nodes perform period self adjustment to better represent the detected music beat period. The system is tested using a variety of music from different genres and shows promise, in many cases with high correct beat detection percentages.
One of the problems in image processing is finding an appropriate threshold in order to convert an image to a binary one. In this paper we introduce a new method for image thresholding. We use reinforcement learning a...
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One of the problems in image processing is finding an appropriate threshold in order to convert an image to a binary one. In this paper we introduce a new method for image thresholding. We use reinforcement learning as an effective way to find the optimal threshold. Q(Λ) is implemented as a learning algorithm to achieve more accurate results. The reinforcement agent uses objective rewards to explore/exploit the solution space. It means that there is not any experienced operator involved and the reward and punishment function must be defined for the agent. The results show that this method works successfully and can be trained for any particular application.
This work presents a generalized framework for approaching the problem of optimizing parameters for a multistep computer vision algorithm using reinforcement learning (RL) agent. We then focus on the specific task of ...
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This work presents a generalized framework for approaching the problem of optimizing parameters for a multistep computer vision algorithm using reinforcement learning (RL) agent. We then focus on the specific task of text detection in images taken from video sequences for semantic indexing. Employing the fuzzy ARTMAP ANN is an effective way to manage and predict large state and action spaces. Image features in the state space will allow for optimization on a per-image.
A general framework for reinforcement learning (RL) to the parameter selection problem in multi-step image and vision-based tasks is discussed. RL is described as a computational approach to learning through interacti...
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ISBN:
(纸本)0780384822
A general framework for reinforcement learning (RL) to the parameter selection problem in multi-step image and vision-based tasks is discussed. RL is described as a computational approach to learning through interacting with the environment. The utility of the framework has been demonstrated by optimizing a set of parameters for an algorithm to detect text images taken from video sequences. It is suggested that introducing image features in the state space will allow for optimization on a per-image, rather than per-set basis.
In this paper, we present a novel biometric authentication system to identify a person’s identity by his/her palmprint. In contrast to existing palmprint systems for criminal applications, the proposed system targets...
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This paper discusses the results of a Work Domain Analysis (WDA) applied to the management of diabetes. The goal is to develop a conceptual framework to help diabetic patients understand and manage their disease. The ...
This paper discusses the results of a Work Domain Analysis (WDA) applied to the management of diabetes. The goal is to develop a conceptual framework to help diabetic patients understand and manage their disease. The results of the WDA will be used to design and implement mobile and desktop information displays that will be used by the patient and their health care team. This poses additional challenges in demonstrating if and how Ecological interfacedesign can be used to design medical information displays on various mobile devices. When applied to this medical domain, the usefulness of the WDA to the design of medical information displays becomes restricted because of the complex, interconnected processes involved and the limited amount of sensor data available. Alternatively, the Work Domain representation can be used as a teaching resource or used to define requirements for future monitoring technologies.
This paper describes Work Domain Analysis (WDA) applied to the domain of automated midair collision detection and avoidance as a first step in the design of improved ecological interfaces for automated traffic alertin...
This paper describes Work Domain Analysis (WDA) applied to the domain of automated midair collision detection and avoidance as a first step in the design of improved ecological interfaces for automated traffic alerting displays. Three abstraction hierarchies (AH) modelling the aircraft, collision environment, and a traffic alerting system are presented, and the challenges of adapting the AH to enhanced displays will be examined. It will be shown that WDA is a feasible framework for establishing information requirements of flight and automated collision dynamics. Ecological interfacedesign (EID) can then be applied to develop displays that invoke a greater trust in the automation.
This paper introduces a methodology for integrating Cognitive engineering into information System Analysis and design (SAD). Work Domain Analysis (WDA) in CE is used to analyze and capture a large amount data and comp...
This paper introduces a methodology for integrating Cognitive engineering into information System Analysis and design (SAD). Work Domain Analysis (WDA) in CE is used to analyze and capture a large amount data and complex relationships in a work domain. In SAD, Data Flow Diagrams (DFD) and Entity-Relationship (ER) approaches are two important tools. Based on the captured data and relationships in the WDA model, DFD method is employed to further analyze data, data processing, and their relationships from the point of data flow. On the basis of creating WDA and DFD models, Entity-Relationship analysis methods uses normalization principles to optimize table structures, create the relationships among tables, and finally to create an Entity-Relationship Diagram. By applying the integrated methodology to the SAD of a database for health information, we created a system structure diagram, a work domain model, a DFD model, and an ER model. The methodology should emphasize human factors in Information System development, enhance the reliability of SAD and database integrity, and reduce system development time. The database is based on a three-tiered client/server system architecture and running on Oracle9i.
In this paper, a non-time based tracking controller of a nonholonomic mobile robot is first analyzed. Non-time based motion controllers have been successfully applied to many areas such as robot motion control, multi-...
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