The Experts below are selected from a list of 184407 Experts worldwide ranked by ideXlab platform
Kuan-Quan Wang - One of the best experts on this subject based on the ideXlab platform.
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an assembled matrix distance metric for 2dpca based Image Recognition
Pattern Recognition Letters, 2006Co-Authors: Wang-Meng Zuo, David Zhang, Kuan-Quan WangAbstract:Two-dimensional principal component analysis (2DPCA) is a novel Image representation approach recently developed for Image Recognition. One characteristic of 2DPCA is that it can extract feature matrix using a straightforward Image projection technique. In this paper, we propose an assembled matrix distance metric (AMD) to measure the distance between two feature matrices. To test the efficiency of the proposed distance measure, we use two Image databases, the ORL face database and the PolyU palmprint database. The results of our experiments show that the assembled matrix distance metric is very effective in 2DPCA-based Image Recognition.
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Bidirectional PCA with assembled matrix distance metric for Image Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2006Co-Authors: Wang-Meng Zuo, Kuan-Quan WangAbstract:Principal component analysis (PCA) has been very successful in Image Recognition. Recent research on PCA-based methods has mainly concentrated on two issues, namely: 1) feature extraction and 2) classification. This paper proposes to deal with these two issues simultaneously by using bidirectional PCA (BD-PCA) supplemented with an assembled matrix distance (AMD) metric. For feature extraction, BD-PCA is proposed, which can be used for Image feature extraction by reducing the dimensionality in both column and row directions. For classification, an AMD metric is presented to calculate the distance between two feature matrices and then the nearest neighbor and nearest feature line classifiers are used for Image Recognition. The results of the experiments show the efficiency of BD-PCA with AMD metric in Image Recognition.
Wang-Meng Zuo - One of the best experts on this subject based on the ideXlab platform.
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an assembled matrix distance metric for 2dpca based Image Recognition
Pattern Recognition Letters, 2006Co-Authors: Wang-Meng Zuo, David Zhang, Kuan-Quan WangAbstract:Two-dimensional principal component analysis (2DPCA) is a novel Image representation approach recently developed for Image Recognition. One characteristic of 2DPCA is that it can extract feature matrix using a straightforward Image projection technique. In this paper, we propose an assembled matrix distance metric (AMD) to measure the distance between two feature matrices. To test the efficiency of the proposed distance measure, we use two Image databases, the ORL face database and the PolyU palmprint database. The results of our experiments show that the assembled matrix distance metric is very effective in 2DPCA-based Image Recognition.
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Bidirectional PCA with assembled matrix distance metric for Image Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2006Co-Authors: Wang-Meng Zuo, Kuan-Quan WangAbstract:Principal component analysis (PCA) has been very successful in Image Recognition. Recent research on PCA-based methods has mainly concentrated on two issues, namely: 1) feature extraction and 2) classification. This paper proposes to deal with these two issues simultaneously by using bidirectional PCA (BD-PCA) supplemented with an assembled matrix distance (AMD) metric. For feature extraction, BD-PCA is proposed, which can be used for Image feature extraction by reducing the dimensionality in both column and row directions. For classification, an AMD metric is presented to calculate the distance between two feature matrices and then the nearest neighbor and nearest feature line classifiers are used for Image Recognition. The results of the experiments show the efficiency of BD-PCA with AMD metric in Image Recognition.
Keiichi Tokuda - One of the best experts on this subject based on the ideXlab platform.
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Image Recognition based on convolutional neural networks using features generated from separable lattice hidden markov models
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2018Co-Authors: Takayuki Kasugai, Yoshihiko Nankaku, Yoshinari Tsuzuki, Kei Sawada, Kei Hashimoto, Keiichiro Oura, Keiichi TokudaAbstract:An Image Recognition method based on convolutional neural networks (CNNs) using features generated from separable lattice hidden Markov models (SLHMMs) is proposed. A major problem in Image Recognition is that the Recognition performance is degraded by geometric variations in the size and position of the object to be recognized. To solve this problem, SLHMMs have been proposed as an extension of HMMs with size and locational invariances based on state transitions. Although SLHMMs are generative models that can represent the generation processes of observations well, there is a possibility that they are not specialized for discrimination compared to discriminative models. Our method integrates SLHMMs that extract features invariant to geometric variations with CNNs that build an accurate classifier based on discriminative models with the extracted features. Face Recognition experiments showed that the proposed method improves Recognition performance.
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Image Recognition based on separable lattice hmms using a deep neural network for output probability distributions
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Eiji Ichikawa, Yoshihiko Nankaku, Kei Sawada, Kei Hashimoto, Keiichi TokudaAbstract:This paper proposes an Image Recognition method based on separable lattice hidden Markov models (SLHMMs) using a deep neural network (DNN) for output probability distributions. The geometric variations of the object to be recognized, e.g., size and location, are essential in Image Recognition. SLHMMs, which have been proposed to reduce the effect of geometric variations, can perform elastic matching both horizontally and vertically. Gaussian distributions are typical for modeling the output distribution of SLHMMs. However, these distributions may not be sufficient to represent patterns of Image regions. Our method integrates SLHMMs and a DNN and can be used to model an Image effectively by explicit modeling of the generative process based on SLHMMs and advanced feature classification based on a DNN. Image Recognition experiments showed that the proposed method improves Recognition performance.
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Image Recognition based on discriminative models using features generated from separable lattice hmms
International Conference on Acoustics Speech and Signal Processing, 2017Co-Authors: Yoshinari Tsuzuki, Yoshihiko Nankaku, Kei Sawada, Kei Hashimoto, Keiichi TokudaAbstract:This paper presents an Image Recognition technique based on discriminative models using features generated from separable lattice hidden Markov models (SL-HMMs). A major problem in Image Recognition is that the Recognition performance is degraded by geometric variations such as that in position and size of the object to be recognized. SL-HMMs have been proposed to solve this problem. SL-HMMs are an extension of HMMs with size and locational invariances based on state transitions. An SL-HMM is a generative model and can represent generation processes of observations well. However, there is a possibility that the Recognition performance of generative models is inferior to that of discriminative models because discriminative models are specialized to identification. In this paper, we propose Image Recognition based on log linear models (LLMs) using features extracted from SL-HMMs. The proposed method can extract features invariant to geometric variations by using SL-HMMs and built an accurate classifier based on discriminative models with the extracted features. Face Recognition experiments showed that the proposed method obtained higher Recognition rates than SL-HMMs and convolutional neural networks based methods.
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Image Recognition based on separable lattice trajectory 2 d hmms
IEICE Transactions on Information and Systems, 2014Co-Authors: Akira Tamamori, Yoshihiko Nankaku, Keiichi TokudaAbstract:In this paper, a novel statistical model for Image Recognition based on separable lattice 2-D HMMs (SL2D-HMMs) is proposed. Although SL2D-HMMs can model invariance to size and location deformation, its modeling accuracy is still insufficient because of the following two assumptions: i) the statistics of each state are constant and ii) the state output probabilities are conditionally independent. In this paper, SL2D-HMMs are reformulated as a trajectory model that can capture dependencies between adjacent observations. The effectiveness of the proposed model was demonstrated in face Recognition and Image alignment experiments.
Tamio Arai - One of the best experts on this subject based on the ideXlab platform.
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an integrated memory array processor for embedded Image Recognition systems
IEEE Transactions on Computers, 2007Co-Authors: Shinichiro Okazaki, Tamio AraiAbstract:Embedded processors for video Image Recognition in most cases not only need to address the conventional cost (die size and power) versus real-time performance issue, but must also maintain high flexibility due to the immense diversity of Recognition targets, situations, and applications. This paper describes IMAP, a highly parallel SIMD linear processor and memory array architecture that addresses these trade-off requirements. By using parallel and systolic algorithmic techniques, but based on a simple linear array architecture, IMAP successfully exploits not only the straightforward per-Image row data level parallelism (DLP), but also the inherent DLP of other memory access patterns frequently found in various Image Recognition tasks, while allowing programming to be done using an explicit parallel C language (1DC). We describe and evaluate IMAP-CE, one of the latest IMAP processors, integrating 128 100 MHz 8 bit 4-way VLIW PEs, 128 2 KByte RAMs, and one 16 bit RISC control processor onto a single chip. The PE instruction set is enhanced to support 1DC code. The die size of IMAP-CE is 11 times11 mm2 integrating 32.7 M transistors, while the power consumption is, on average, approximately 2 watts. IMAP-CE is evaluated mainly by comparing its performance while running 1DC code with that of a 2.4 GHz Intel P4 running optimized C code. Based on the use of parallelizing techniques, benchmark results show a speed increase of up to 20 times for Image filter kernels and of 4 times for a full Image Recognition application
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an integrated memory array processor architecture for embedded Image Recognition systems
International Symposium on Computer Architecture, 2005Co-Authors: Shorin Kyo, Shinichiro Okazaki, Tamio AraiAbstract:Embedded processors for video Image Recognition require to address both the cost (die size and power) versus real-time performance issue, and also to achieve high flexibility due to the immense diversity of Recognition targets, situations, and applications. This paper describes IMAP, a highly parallel SIMD linear processor and memory array architecture that addresses these trading-off requirements. By using parallel and systolic algorithmic techniques, despite of its simple architecture IMAP achieves to exploit not only the straightforward per Image row data level parallelism (DLP), but also the inherent DLP of other memory access patterns frequently found in various Image Recognition tasks, under the use of an explicit parallel C language (1DC). We describe and evaluate IMAP-CE, a latest IMAP processor, which integrates 128 of 100MHz 8 bit4-way VLIW PEs, 128 of 2KByte RAMs, and one 16 bit RISC control processor, into a single chip. The PE instruction set is enhanced for supporting 1DC codes. IMAP-CE is evaluated mainly by comparing its performance running 1DC codes with that of a 2.4GHz Intel P4 running optimized C codes. Based on the use of parallelizing techniques, benchmark results show a speedup of up to 20 for Image filter kernels, and of 4 for a full Image Recognition application.
David Zhang - One of the best experts on this subject based on the ideXlab platform.
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an assembled matrix distance metric for 2dpca based Image Recognition
Pattern Recognition Letters, 2006Co-Authors: Wang-Meng Zuo, David Zhang, Kuan-Quan WangAbstract:Two-dimensional principal component analysis (2DPCA) is a novel Image representation approach recently developed for Image Recognition. One characteristic of 2DPCA is that it can extract feature matrix using a straightforward Image projection technique. In this paper, we propose an assembled matrix distance metric (AMD) to measure the distance between two feature matrices. To test the efficiency of the proposed distance measure, we use two Image databases, the ORL face database and the PolyU palmprint database. The results of our experiments show that the assembled matrix distance metric is very effective in 2DPCA-based Image Recognition.
