Theoretical results suggest that in order to learn complicated functions that can represent high-level features in the computer vision field, one may need to use deep architectures. The popular choice among scientists...
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ISBN:
(纸本)9783319590639;9783319590622
Theoretical results suggest that in order to learn complicated functions that can represent high-level features in the computer vision field, one may need to use deep architectures. The popular choice among scientists and engineers for modeling deep architectures are feed-forward Deep Artificial Neural Networks. One of the latest research areas in this field is the evolution of Artificial Neural Networks: NeuroEvolution. This paper explores the effect of evolving a Node Transfer Function and its parameters, along with the evolution of connection weights and an architecture in Deep Neural Networks for patternrecognition problems. The results strongly indicate the importance of evolving Node Transfer Functions for shortening the time of training Deep Artificial Neural Networks using NeuroEvolution.
A supervised algorithm for computing perceptual groupings in dot patterns is presented. The algorithm uses shape features of the polygons in the Voronoi tessellation of the input pattern. The training patterns identif...
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Kernel tracing facilitates to demonstrate various activities running inside the Operating System. Kernel tracing tools like LTT, LTTng, DTrace, FTrace provide details about processes and their resources but these tool...
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ISBN:
(纸本)9781509015603
Kernel tracing facilitates to demonstrate various activities running inside the Operating System. Kernel tracing tools like LTT, LTTng, DTrace, FTrace provide details about processes and their resources but these tools lack to extract knowledge from it. patternrecognition is a major field of data mining and knowledge discovery. This paper presents a survey of widely used algorithms like Apriori, Tree-projection, FP-growth, Eclat for finding frequent pattern over the database. This paper presents a comparative study of frequent pattern mining algorithm and suggests that the FP-growth algorithm is suitable for finding patterns in kernel trace data.
In an effort to develop an interaction mechanism based on visual sensing to trigger and improve delivery of context based services and information pertinent to location in a solicited and near real time manner, we pre...
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ISBN:
(纸本)9781424434299
In an effort to develop an interaction mechanism based on visual sensing to trigger and improve delivery of context based services and information pertinent to location in a solicited and near real time manner, we present in this paper HISI, a softrecognition approach to the processing and identification of buildings. Using a coarse joint histogram technique, an image captured by a mobile user with a cell phone is pre-processed to reduce the search space to an adaptive list of potential buildings, after which a weighted fusion of different SIFT maps identifies the building in question. Experimental results showed HISI's effectiveness with respect to other published results in the literature and motivates further extensions to HISI, as it carries expectations for commercial values to the delivery of context based services initiated by users.
In this paper we analyze Support Vector Machine (SVM) algorithm to the problem of chemical compounds screening with a desired activity, definition of hits. The support vector machine transforms the input data in an (u...
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ISBN:
(纸本)9783540695721
In this paper we analyze Support Vector Machine (SVM) algorithm to the problem of chemical compounds screening with a desired activity, definition of hits. The support vector machine transforms the input data in an (unknown) high dimensional feature space and the kernel technique is applied to calculate the inner-product of feature data. The problem of automatically tuning multiple parameters for patternrecognition SVMs using our new introduced kernel for chemical compounds is considered. This is done by simple eigen analysis method which is applied to the matrix of the same dimension as the kernel matrix to find the structure of feature data, and to find the kernel parameter accordingly. We characterize distribution of data by the principle component analysis method.
Biometrics, the computer-based validation of persons’ identity, is becoming more and more essential due to the increasing demand for high-security systems. A biometric system testifies the authenticity of a specific ...
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In this paper we propose a new probabilistic approach to red eye detection and correction. It is based on stepwise refinement of a pixel-wise red eye probability map. Red eye detection starts with a fast non red eye r...
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ISBN:
(纸本)0769525210
In this paper we propose a new probabilistic approach to red eye detection and correction. It is based on stepwise refinement of a pixel-wise red eye probability map. Red eye detection starts with a fast non red eye region rejection step. A classification step then adjusts the probabilities attributed to the detected red eye candidates. The correction step finally applies a soft red eye correction based on the resulting probability map. The proposed approach is fast and allows achieving an excellent correction of strong red eyes while producing a still significant correction of weaker red eyes.
An expert system approach for image classification according to expert knowledge about best sites for vegetation classes is described. Uncertainty management is solved by a certainty factor approach. The numerical and...
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An expert system approach for image classification according to expert knowledge about best sites for vegetation classes is described. Uncertainty management is solved by a certainty factor approach. The numerical and symbolic data fusion is viewed as an updating process. The fusion approach is then described. A neural classifier applied to image data is the first source. A set of fuzzy neural networks representing expert knowledge constitutes the second source. A conjunctive combination based on evidence theory is applied. Finally, a possibility theory-based pooling aggregation rule is presented. These three approaches are applied to a vegetation classification problem.
In this paper, we present a soft biometrics based appearance model for multi-target tracking in a single camera. Tracklets, the short-term tracking results, are generated by linking detections in consecutive frames ba...
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ISBN:
(纸本)9781479952083
In this paper, we present a soft biometrics based appearance model for multi-target tracking in a single camera. Tracklets, the short-term tracking results, are generated by linking detections in consecutive frames based on conservative constraints. Our goal is to "re-stitching" the adjacent tracklets that contain the same target so that robust long-term tracking results can be achieved. As the appearance of the same target may change greatly due to heavy occlusion, pose variations and changing lighting conditions, a discriminative appearance model is crucial for association-based tracking. Unlike most previous methods which simply use the similarity of color histograms or other low level features to construct the appearance model, we propose to use the fusion of soft biometrics generated from sub-tracklets to learn a discriminative appearance model in an online manner. Compared to low level features, soft biometrics are robust against appearance variation. The experimental results demonstrate that our method is robust and greatly improves the tracking performance over the state-of-the-art method.
This book constitutes the refereed proceedings of the 8th internationalconference on Intelligent computing, ICIC 2012, held in Huangshan, China, in July 2012. The 242 revised full papers presented in the three volume...
ISBN:
(数字)9783642318375
ISBN:
(纸本)9783642318368
This book constitutes the refereed proceedings of the 8th internationalconference on Intelligent computing, ICIC 2012, held in Huangshan, China, in July 2012. The 242 revised full papers presented in the three volumes LNCS 7389, LNAI 7390, and CCIS 304 were carefully reviewed and selected from 753 submissions. The papers in this volume (CCIS 304) are organized in topical sections on Neural Networks; Particle Swarm Optimization and Niche Technology; Kernel Methods and Supporting Vector Machines; Biology Inspired computing and Optimization; Knowledge Discovery and Data Mining; Intelligent computing in Bioinformatics; Intelligent computing in patternrecognition; Intelligent computing in Image Processing; Intelligent computing in Computer Vision; Intelligent Control and Automation; Knowledge Representation/Reasoning and Expert Systems; Advances in Information Security; Protein and Gene Bioinformatics; softcomputing and Bio-Inspired Techiques in Real-World Applications; Bio-Inspired computing and Applications.
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