This paper introduces a novel approach to change gathered images from WWW into training images to build an image thesaurus. The requirements for being training images are a large number of images and with highly relev...
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ISBN:
(纸本)9781424407071
This paper introduces a novel approach to change gathered images from WWW into training images to build an image thesaurus. The requirements for being training images are a large number of images and with highly relevant to a given concept. To fulfill these requirements, a system should be able to collect a large number of relevant images to a given concept from WWW by the proposed criterion of relevance to the concept for each image. Then, the irrelevant images would be filtered out by the modified hierarchical clustering method based on the weighted combination of 5 MPEG-7 visual descriptors[9] and the proposed criterion of relevance to the concept for each cluster. Upon experimental results, the precision of the set of images generated by the proposed method is about 18% higher than that of the set of images generated by other methods[1][2].
In this paper, we present a progressive image reconstruction scheme based on the semantically scalable multi-scale edge representation of images, with the resolution and visual quality scalable to various bitrate requ...
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ISBN:
(纸本)9781424407071
In this paper, we present a progressive image reconstruction scheme based on the semantically scalable multi-scale edge representation of images, with the resolution and visual quality scalable to various bitrate requirements. In the multi-scale edge representation an image is decomposed into its multi-scale primal sketch and the background where the multi-scale primal sketch preserves the structural semantics of images, and the background represents the smooth locale. Edge compensation is performed to smoothly remove edges at each scale. The multi-scale edges are then embedded encoded using the GFA modeling. The image reconstruction is progressively achieved by synthesizing multi-scale edges on the reconstructed image obtained from previous scale. As edge synthesis is performed at consecutive scales, the visual quality of the reconstructed image is progressively enhanced. Experiment shows that the proposed scheme performs well at low bit-rate multiresoultion representation and progressive reconstruction.
A machine vision based keg inspection system can allow cost effective keg tracking and preventative maintenance programs to be implemented, leading to substantial savings for breweries with large keg fleets. A robust ...
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ISBN:
(纸本)9781424407071
A machine vision based keg inspection system can allow cost effective keg tracking and preventative maintenance programs to be implemented, leading to substantial savings for breweries with large keg fleets. A robust keg serial number recognition and keg condition assessment process is required to cater for different keg brands and a range of keg ages in the fleet It has been demonstrated that the proposed imageprocessing methodology, and neural network based number recognition system, successfully located and identified keg serial numbers with a 92% digit accuracy. Furthermore, the vision system allowed the concurrent assessment of the keg condition by assessing deformity of the keg rim, and that of the filler valve. A correlation coefficient, generated using a template matching process, proved to be a suitable metric which adequately indicated rims within and outside acceptable deformity bounds.
In the automotive sector a huge amount of measurement data is recorded for validation and safeguarding of vehicle components. These data has to be automatically evaluated for an effective data analysis. Therefore, we ...
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ISBN:
(纸本)9781424407071
In the automotive sector a huge amount of measurement data is recorded for validation and safeguarding of vehicle components. These data has to be automatically evaluated for an effective data analysis. Therefore, we need a sophisticated approach, which offers a flexible and powerful parametrisation and different signalprocessing algorithms for multiple applications. In this paper software and signal evaluation modules for an automated analysis of vehicle measurement data are presented. The data can be evaluated signal or message based with a parametrisation with reusable XML templates. Exemplary, we describe three evaluation modules integrating different signalprocessing approaches: signal analysis using an analytical signal description in combination with fuzzy logic, an efficient sliding frequency detection and the detection of predefined patterns using a modified dynamic time warping algorithm. Furthermore, an approach for a connected evaluation in consideration of time correlation is presented. Concluding, we discuss a practical application.
A novel approach based on computationalintelligence techniques for the identification of nonlinear dynamic systems is presented in this paper. The technique encompasses both the properties of the Karhunen-Loeve Trans...
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ISBN:
(纸本)9781424407071
A novel approach based on computationalintelligence techniques for the identification of nonlinear dynamic systems is presented in this paper. The technique encompasses both the properties of the Karhunen-Loeve Transform in representing stochastic processes and the approximation capabilities of multi-layer neural networks. Experimental results on nonlinear systems governed by difference equations demonstrate the effectiveness of the proposed approach that is based on a real-time learning algorithm. Exhaustive experimentation on specific case studies was performed and some experimental results were compared with other existing techniques such as the Lee-Schetzen method, Least Mean Square (LMS), Recursive Least Square (RLS) and Normalized Least Mean Square (NLMS) algorithms. A better identification-accuracy was also achieved, and a reduction of some orders of magnitude in training-times compared with the well-known Lee-Schetzen method was obtained, thus making the proposed methodology one of the current best practices in this field.
Silhouette is an important research issue in the field of Non-Photorealistic Rendering (NPR) and it is also a popular drawing feature in illustrations and line-drawing artworks. In this paper, we present a real-time i...
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ISBN:
(纸本)9781424407071
Silhouette is an important research issue in the field of Non-Photorealistic Rendering (NPR) and it is also a popular drawing feature in illustrations and line-drawing artworks. In this paper, we present a real-time image-based stylized rendering system. First, we project a 3D model to image-space. Then we extract edges in the image-space data. We perform edge-detection algorithms on GPU (Graphics processing Units) for speedup. GPU is good at floating-points calculating and processing with parallelism. Both features match the property of most imageprocessing tasks. Our system can run at an interactive frame rate when combining our edge-detection algorithms with graphic hardware architecture. We demonstrate that this system performance can reach real-time and render images in good NPR style.
This paper presents a new method for continuous and incremental learning and recognition based on self-organized incremental neural networks. It is available in the fluctuating environment where the number of recognit...
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ISBN:
(纸本)9781424407071
This paper presents a new method for continuous and incremental learning and recognition based on self-organized incremental neural networks. It is available in the fluctuating environment where the number of recognition classes cannot be defined. In this method, the learning process and recognition process are not separated. This method can acquire concept when multiple feature vectors of new input object come, and then can recognize it using previously acquired concept. We experiment an examples of life-long semi-supervised learning tasks in real world. In the result, the proposed method was able to learn and recognize 104 objects incrementally, non-stop, and in real time.
The task of autonomous surface discernment by an AIBO robotic dog is addressed. Different surface textures (plywood board, thin foam, short carpet, shag carpet) as well as different inclines (0 and 10 degrees) are con...
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ISBN:
(纸本)9781424407071
The task of autonomous surface discernment by an AIBO robotic dog is addressed. Different surface textures (plywood board, thin foam, short carpet, shag carpet) as well as different inclines (0 and 10 degrees) are considered. Using a genetic algorithm, gaits are designed which allow the robot to traverse each of these surfaces in an (approximately) optimal fashion. Frequency domain analysis of actuator readings from individual leg joints is performed for data collected using each gait on each surface type. It is found that the spectral content of these signals is significantly dependent on the characteristics of both the gait in use and the surface being walked upon. Using tap-delay Adaline neural networks to integrate actuator readings from 15 independent joints into a set of models of different gait/surface experiences, an algorithm is designed which uses these experiences to yield high classification rates across surface transitions and with low latency.
In this paper a hybrid global optimization method GLPS is further investigated and applied for feed-forward neural networks supervised learning. The method is initially tested on several benchmark problems and subsequ...
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ISBN:
(纸本)9781424407071
In this paper a hybrid global optimization method GLPS is further investigated and applied for feed-forward neural networks supervised learning. The method is initially tested on several benchmark problems and subsequently employed for pattern recognition problem. The proposed technique is used for training Neural Networks (NN) that have to inspect and classify three types of cork tiles images. During the feature extraction phase, statistical textural characteristics are obtained from the tiles' images and then used for training several different NN architectures. Results from the testing phase are discussed and analysed, showing good generalization abilities of the trained NN. Finally, directions of future work are briefly stated.
The overall objective of this paper is to present a methodology for reducing the human workload through adapting an automatic scheme for content-based image Retrieval (CBIR) engines. The proposed system utilizes an un...
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ISBN:
(纸本)9781424407071
The overall objective of this paper is to present a methodology for reducing the human workload through adapting an automatic scheme for content-based image Retrieval (CBIR) engines. The proposed system utilizes an unsupervised hierarchical clustering algorithm, known as the Directed Self-Organizing Tree Map (DSOTM) that aims to closely mimic the process of information classification thought to be at work in the human brain [1, 2]. To further refine the search process and increase retrieval accuracy, a semi-automatic relevance feedback approach is presented in this work. The Semi-automatic scheme refers to a relevance feedback CBIR engine, structured around the DSOTM algorithm. This system aims to learn from and adapt to different users' subjectivity under the guidance of an additional objective verdict provided by the DSOTM. Comprehensive comparisons with the rank-based, relevance feedback, and automatic CBIR engines, demonstrate feasibility of adapting the Semi-automatic approach.
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