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Software development system for fuzzy control

Robotica 2000年第4期18卷 375-380页

作者： Yang, Jie Guo, Yingkai Huang, Xin Inst. Image Proc. and Pattern Recog. Shanghai Jiao-Tong University Shanghai 200030 China

Fuzzy control has been widely applied in industrial controls and domestic electrical equipment. The automatic learning of fuzzy rules is a key technique in fuzzy control. In this paper, a software development system for fuzzy control is presented. Since the learning of fuzzy rules can be seen as finding the best classifications of fuzzy memberships of input-output variables in a fuzzy controller, it can also be seen as the combination optimization on input-output fuzzy memberships. Multi-layer feedforward network and genetic algorithms (GA) can be used for the automatic learning of fuzzy rules. The algorithms and their characteristics are described. The software development system has been successfully used for the design of some fuzzy controllers.

关键词： Fuzzy control

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image segmentation and edge detection based on watershed techniques

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International Journal of Computers and Applications 2003年第4期25卷 258-263页

作者： Salman, N.H. Liu, Chong-Qing Inst. of Image Proc./Pattern Recog. Shanghai Jiao Tong University Shanghai 200030 China

K-means and watershed segmentation techniques are presented to perform image segmentation and edge detection tasks. We first used the K-means technique to obtain a primary segmented image. We then employed a watershed technique that works on that image;this process includes gradient of the segmented input image, divides the image into markers, completes the watershed line by using the markers, and stores the image in the format of region adjacency graph (RAG). The initial segmentation result was obtained by the watershed algorithm. We then used merging techniques based on mean gray values and two edge strengths (T1, T2) to obtain edge maps. In this article we solved the problem of undesirable oversegmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps have no broken lines on the entire image, and the final edge detection result is one closed boundary per actual region in the image.

关键词： image segmentation

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Mushroom Classification Based on Deep Residual Network 4

Mushroom Classification Based on Deep Residual Network

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4th IEEE International Conference on pattern recognition and Machine Learning, PRML 2023

作者： Feng, Ju Ling, Xufeng Wang, Yubo Yang, Jie Shanghai Normal University Tianhua College School of Artificial Intelligence Shanghai China Shanghai Acoustics Laboratory Chinese Academy of Sciences Shanghai China Inst. of Image Proc. and Pattern Recog. and Inst. of Medical Robotics Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9798350324303

Due to the similarity in mushroom features and the difficulty in distinguishing between poisonous and nonpoisonous varieties, mushrooms pose a threat to human health. To address the challenge of mushroom classification and identification, this paper proposes a mushroom classification method based on residual networks. Firstly, a network architecture with multiple residual blocks is designed, and it is trained using an image dataset. Then, a transfer learning strategy is employed to initialize the network parameters from a pre-Trained model, followed by fine-Tuning to adapt to the mushroom classification task. Finally, multiple testing experiments are conducted to evaluate the effectiveness of the proposed method. The experimental results demonstrate excellent performance of the proposed method in mushroom classification tasks. Compared to traditional feature extraction methods, it can better capture the details and texture features of mushrooms, thereby improving classification accuracy. In conclusion, the mushroom classification method based on residual networks exhibits high accuracy and generalization capability. This method has potential applications in the field of mushroom classification, aiding in the better identification and differentiation of poisonous mushrooms, thereby protecting human health. © 2023 IEEE.

关键词： Convolutional neural networks

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A novel method for boundary condition computation in soft tissue deformation

A novel method for boundary condition computation in soft ti...

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2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009

作者： Shengzheng, Wang Jie, Yang Kai, Xie Merchant Marine College Shanghai Maritime University 200135 Shanghai China Inst. of Image Processing and Pattern Recognition Shanghai Jiaotong University 200240 Shanghai China Methodist Hospital Research Institute Cornell University Houston TX 77030 United States

ISBN: (纸本)9780769535074

Simulating the soft tissue deformation based on bone-related planning is one of the most difficult issues in generating realistic motion of soft tissues. The difficulty of this problem arises from unclear boundary conditions. In facial soft tissue deformation, the boundary conditions of the finite element modeling are related to the bone movements. In order to better map the bone movements to the soft tissue model, a fast and robust computational strategy for boundary conditions is presented in this paper. First, a collision detection algorithm is employed to query contact position after repositioning of bones. Then, an improved 3D discrete distance field algorithm is proposed to perform imposed displacement computation of contact vertices. Finally, boundary conditions are calculated in terms of the imposed displacements. This approach can automatically handle boundary conditions of the complex geometrical modeling without prior assumption or knowledge about the position of contacts. The effectiveness of our method is demonstrated on complex soft tissue deformation. © 2008 IEEE.

关键词： Boundary conditions

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An SVM-based small target segmentation and clustering approach

An SVM-based small target segmentation and clustering approa...

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proceedings of 2004 International Conference on Machine Learning and Cybernetics

作者： Zheng, Sheng Liu, Jian Tian, Jin-Wen Key Lab. Image Proc. Intelligent C. Inst. Pattern Recog. Artif. Intell. Huazhong Univ. of Sci. and Technol. Wuhan 430074 China

ISBN: (纸本)0780384032

Segmentation and clustering of infrared small target images in a sky or sea-sky background is considered in this paper, which is the preprocessing part of the detection and recognition of the moving small targets in an infrared image sequence. The infrared image intensity surface is well fitted by the least squares support vector machines (LS-SVM), and then the maximum extremum points are detected on the well fitted intensity surface by convolving the image with the second order directional derivative operators deduced from the mapped LS-SVM with mixtures of kernels. With the coarse locations, the possible targets are extracted by the clustering analysis. The computer experiments are carried out for the real and simulated sky and sea-sky infrared images. The experimental results demonstrate the proposed approach is effective.

关键词： Infrared imaging

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Clock recovery circuit for fast ethernet application

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Guti Dianzixue Yanjiu Yu Jinzhan/Research and Progress of Solid State Electronics 2004年第4期24卷 472-475+481页

作者： Zhu, Quanqing Li, Haihua Zou, Xuecheng Shen, Xubang Inst. for Pattern Recognition Lab. for Image Proc. Wuhan 430074 China Dept. of Elec. Sci. and Technol. Huazhong Univ. of Sci. and Technol. Wuhan 430074 China

This thesis presents a circuit architecture to realize clock recovery for fast Ethernet application, including system architecture, modified Mueller Muller algorithm for 100BASE-TX, phase detector for 100BASE-FX and multiple output charge pump PLL. The clock generator circuit with 3.3 V operation voltage has been verified by TSMC 0.35 μm 1P5M CMOS process. The results show that this clock recovery circuit can exactly extract the timing information. It has advantages over other ones for simplicity and easy implementation.

关键词： Data communication equipment

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Object localization based on Markov random fields and symmetry interest points

Object localization based on Markov random fields and symmet...

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10th International Conference on Medical image Computing and Computer-Assisted Intervention (MICCAI 2007)

作者： Donner, Rene Micusik, Branislav Langs, Georg Szumilas, Lech Peloschek, Philipp Friedrich, Klaus Bischof, Horst Graz Univ Technol Inst Comp Graph & Vis A-8010 Graz Austria Vienna Univ Technol Pattern Recognition & Image Proc Grp Vienna Austria Ecole Cent Paris GALEN Grp Math Appl Syst Paris France Med Univ Vienna Dept Radiol Vienna Austria

ISBN: (纸本)9783540757580

We present an approach to detect anatomical structures by configurations of interest points, from a single example image. The representation of the configuration is based on Markov Random Fields, and the detection is performed in a single iteration by the MAX-SUM algorithm. inst.ad of sequentially matching pairs of interest points, the method takes the entire set of points, their local descriptors and the spatial configuration into account to find an optimal mapping of modeled object to target image. The image information is captured by symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.

关键词： Medical imaging

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A new printing measurement method and structure feature extraction of microscopic dot

A new printing measurement method and structure feature extr...

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WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference proceedings

作者： Wang, Yonggang Yang, Jie Inst. Image Proc./Pattern Recog. Shanghai Jiaotong University Shanghai 200030

To overcome the drawbacks of print measurements, a novel measurement approach, namely dot structure based measurement, is proposed. In addition, the structure feature extraction of microscopic dot is discussed. The proposed scheme provides a precondition for such further researches as print quality surveillance, fault diagnosis, color design and printing theories as well.

关键词： Feature extraction

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Analog signal generator for BIST of wideband IF signals bandpass sigma-delta modulator

Analog signal generator for BIST of wideband IF signals band...

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作者： Huang, Feng Shen, Xuban Zou, Xuecheng Chen, Chaoyang State Key Lab. Image Proc./I.C. Inst. Pattern Recognition/Artif. I. Huazhong Univ. of Sci./Technology Wuhan Hubei 430074 China Dept. of Electron. Sci./Technology Huazhong Univ. of Sci./Technology Wuhan Hubei 430074 China

This paper presents improvements in generation of wideband and high dynamic range analog signal for area-efficient MADBIST, especially for the on-chip testing of wireless communication IF digitizing sigma-delta modulator chip. Via increasing the order of the one-bit bandpass sigma-delta modulation algorithm up to 12 and using finite repetitious bitstream approximating scheme, it can achieve great improvements in signal bandwidth inst.ad of purity at the cost of very little hardware overhead. Another contribution in this work is to provide the theoretical analysis of the reconstructed signal degradation due to harmonic distortion and clock jitter. Such on-chip analog stimulus generation scheme is especially fit for IF digitizing bandpass sigma-delta modulator chip's production-time testing and in-the-field diagnostics. The technique can also be extended to mixed-signal communication SoC built-in-self-test. © 2002 Elsevier Science Ltd. All rights reserved.

关键词： Modulators

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Crowd Scene Analysis: Crowd Counting using MCNN based on Self-Supervised training with Attention Mechanism

Crowd Scene Analysis: Crowd Counting using MCNN based on Sel...

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IEEE International Conference on Multi Topic

作者： Muhammad Junaid Asif Mujtaba Asad Shaheer Imran Faculty of IT & CS University of Central Punjab Lahore Pakistan SEIEE SJTU Inst. of Image Processing and Pattern Recognition Shanghai China

Fully-supervised learning requires expensive and laborious annotations of labeled data for crowd-counting tasks. To alleviate this burden, it is desirable to explore methods that reduce the need for extensive labeling. Fortunately, there are a vast number of unlabeled images available in the world, making them easily accessible compared to labeled datasets. This paper proposes a self-supervised learning-based M-CNN framework with an attention mechanism that aims to leverage unlabeled data for pre-training the model. The framework consists of four sub-modules: a data augmentation framework, a self-supervised training network, a multi-column CNN, and an attention mechanism. These networks receive the images that undergo random processing using two defined augmentation transformations. Transformed images are then subjected to self-supervised learning and fed to a feature extraction network. FEN consists of M-CNN with five convolutional branches to extract features at a multi-scale level. These extracted features are then employed as an attention mechanism to focus on the head or shoulder location of people. To evaluate the effectiveness of our proposed model, experiments are conducted on two public datasets: ShanghaiTech Part A, Part B, and UCFQNRF. The experimental results demonstrate that our approach outperforms state-of-the-art semi-supervised methods, showcasing the effectiveness of our proposed approach in leveraging both unlabeled and limited labeled data for crowd counting tasks.

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