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3rd international conference on Computer Vision and Image Processing, CVIP 2018

3rd International Conference on Computer Vision and Image Pr...

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3rd international conference on Computer Vision and Image Processing, CVIP 2018

ISBN: (纸本)9789813290877

The proceedings contain 38 papers. The special focus in this conference is on Computer Vision and Image Processing. The topics include: Mushroom Classification Using Feature-Based Machine Learning Approach;deep Learning Framework for Detection of an Illicit Drug Abuser Using Facial Image;diagnosis of Prostate Cancer with Support Vector Machine Using Multiwavelength Photoacoustic Images;a Deep Learning Framework Approach for Urban Area Classification Using Remote Sensing Data;things at Your Desk: A Portable Object Dataset;storm Tracking Using Geostationary Lightning Observation Videos;pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm;eigenvector Orientation Corrected LeNet for Digit recognition;retinal-Layer Segmentation Using Dilated Convolutions;vehicle Speed Determination and License Plate Localization from Monocular Video Streams;detecting Face Morphing Attacks with Collaborative Representation of Steerable Features;enabling Text-Line Segmentation in Run-Length Encoded Handwritten Document Image Using Entropy-Driven Incremental Learning;fused Spectral Features in Kernel Weighted Collaborative Representation for Gender Classification Using Ocular Images;investigation on the Muzzle of a Pig as a Biometric for Breed Identification;integrated Semi-Supervised Model for Learning and Classification;robust Detection of Defective Parts Using pattern Matching for Online Machine Vision System;detection of Down Syndrome Using Deep Facial recognition;cosaliency Detection in Images Using Structured Matrix Decomposition and Objectness Prior;agriculture Parcel Boundary Detection from Remotely Sensed Images;a Reference Based Secure and Robust Zero Watermarking System;two-View Triangulation: A Novel Approach Using Sampson’s Distance;linear Regression Correlation Filter: An Application to Face recognition;human Head Pose and Eye State Based Driver Distraction Monitoring System;preface.

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Face recognition: Novel Comparison of Various Feature Extraction Techniques 4th

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4th international conference on Harmony Search, soft computing and Applications (ICHSA)

作者： Makhija, Yashoda Sharma, Rama Shankar Rajasthan Tech Univ Kota Rajasthan India

ISBN: (纸本)9789811307614;9789811307607

Face recognition system certainly recognizes a face in a picture. This involves extracting features of an image and then recognizing it, despite lighting, expression, pose, aging, and transformations (translate, rotate, and scale image) which is a tough task. In the following research paper, a comprehensive literature review of various kinds of technologies for feature extraction is listed. To present a comprehensive review, we classify residing feature extraction technologies along with detailed description of specific approaches within each classification. These strategies are grouped into four noteworthy classifications, specifically, feature-based, appearance-based, template-based, and part-based approaches. The motivation for our work is the unavailability of comprehensive and direct independent comparison of each one of the feasible algorithm executions in the previously available survey. After considerable exploration of these strategies, we analyze that various feature extraction technologies provide leading results for various applications of image processing.

关键词： Feature extraction Principal component analysis (PCA) Linear discriminant analysis (LDA) Elastic bunch graph matching (EBGM) Local binary pattern histogram (LBPH)

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A Study of Prefrontal Cortex Task Switching Using Spiking Neural Networks 12

A Study of Prefrontal Cortex Task Switching Using Spiking Ne...

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12th international conference on Advanced Computational Intelligence (ICACI)

作者： Viswanathan, K. Ashwin Mylavarapu, Goutam Chen, Kun Thomas, Johnson P. Oklahoma State Univ Dept Compute Sci Stillwater OK 74078 USA

ISBN: (纸本)9781728142487

In this work we study the behavior of Prefrontal Cortex (PFC) and understand its role in task switching by developing a biologically based computational model. We build the PFC neurons using Spiking Neural Networks (SNN) with biologically realizable features having lateral inhibition, synaptic weight changes using unsupervised Spike Timing Dependant Plasticity (STDP) learning rule, spiking threshold and biological ranges for neuronal parameter values. The SNN is composed of Leaky Integrate and Fire (LIF) neurons which are efficient to model and represents the Excitatory neurons in Glutamate layer and Inhibitory neurons in GABA layer. In this implementation we use two real world datasets as tasks for the PFC network to learn. We demonstrate the switching behavior of the neurons and their synaptic weight adaptations by formulating experiments in a manner consistent with real world trials used in the study of cognitive psychology. Using these experiments we show how our model adapts and responds to task changes exhibiting biological behaviors like Long Term Potentiation (LTP), Long Term Depression (LTD) and Task-set reconfiguration (TSR) thereby giving insights into understanding the importance of duration between changing tasks and its effect on performance and efficacies of multi-tasking. The results shown in this paper relate favorably well with the natural neuronal responses found in the brain.

关键词： Spiking Neural Network neuroscience cognitive modeling STDP unsupervised learning cognitive science pattern recognition cognitive computing Computational neuroscience

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New Vague Set Based Similarity Measure for pattern recognition 20

New Vague Set Based Similarity Measure for Pattern Recogniti...

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20th IEEE/ACIS international conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed computing (SNPD 2019)

作者： Lin, Kuo-Sui Aletheia Univ Dept Informat Management New Taipei Taiwan

ISBN: (纸本)9781728116518

By analyzing existing famous similarity measures, these similarity measures are neither reasonable nor capable of discriminating difference between vague sets in some cases. Therefore, the main purpose of this study was to propose a new similarity measure to solve the drawbacks of those similarity measures and apply it to handle pattern recognition problems, which are with imperfect and/or imprecise information. It has proved that the proposed similarity measure satisfies all properties in the axiomatic definition of similarity measure. Furthermore, it also has illustrated to compare and to justify that the proposed similarity measure performs the same or better than existing similarity measures between vague sets. Finally, an application of the proposed similarity measure has successfully demonstrated that the proposed similarity measure can overcome the drawback of the existing similarity measure for measuring degree of similarity between vague sets and is effective and efficient for applying in the context of recognizing patterns.

关键词： normalized Hamming distance pattern recognition similarity measure vague set

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Artificial Intelligent and pattern recognition system based Novel Intelligent Algorithm for Scientific Measurements

Artificial Intelligent and Pattern Recognition system based ...

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international conference on Inventive Research in computing Applications (ICIRCA)

作者： Yanhua Pang Shanshan Xiao Huaiyu Yan Technology Centre of Dalian Customs China

ISBN: (数字)9781728153742

ISBN: (纸本)9781728153759

Determination of different forms of Chromium in cosmetics by reversed-phase Ion-pair liquid Chromatography-inductively coupled plasma mass spectrometry with the artificially intelligent systems is analyzed in this paper. The pattern recognition system (PRS) that introduces these algorithms is intelligent. This so-called accuracy often has some considerable limitations, firstly statically, and then secondly unilaterally. Because each object of the theory of boundary is not clear, the core static membership function description is not reality, object and environment are not static. Hence, this paper proposes the novel intelligent algorithm is proposed for scientific measurement. The experiment is conducted through a massive database. The simulation results have then proven effectiveness.

关键词： Methanol Intelligent systems Ions pattern recognition Liquids Mass spectroscopy conferences

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An All-Optical Binary pattern recognition System Applied in Photonic Firewall based on VPI Simulation 24

An All-Optical Binary Pattern Recognition System Applied in ...

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24th OptoElectronics and Communications conference (OECC) / international conference on Photonics in Switching and computing (PSC)

作者： Guo, Junfeng Li, Xin Tang, Ying Zhang, Lu Gao, Tao Huang, Shanguo Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing Peoples R China

An all-optical binary pattern recognition system based on XOR is simulated by VPI for the first time. The simulation results show the system can recognize and locate target in the data at 10Gb/s and 40Gb/s.

ISBN: (纸本)9784885523212

关键词： Semiconductor optical amplifiers optical signal processing

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pattern of Diffusion recognition in a Molecular Communication Model 1st

Pattern of Diffusion Recognition in a Molecular Communicatio...

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1st international conference on Applied computing Research to Support Industry: Innovation and Technology, ACRIT 2019

作者： Jani, Athraa Juhi Department of Computer Science Al-Mustansiriyah University Baghdad Iraq

ISBN: (纸本)9783030387518

A nanomachine is the basic functional unit in nanotechnology that can perform simple tasks, like sensing and actuation. A set of nanomachines can perform more complex tasks through communicating and sharing information, and by that, they form a nanonetwork. Different communication techniques are proposed for information exchange among nanomachines. Molecular communication is one of these techniques, which is a bio-inspired communication mechanism. In this paper a time slotted model consist of n nanomachines in a bounded environment is considered. These nanomachines are communicate according to diffusion based molecular communication. Information molecules are encoded based on the variation in the concentration of molecules in the communication medium. Thus, the receiver nanomachine decode the sensed information molecules during a certain time slot as ‘1’, if its concentration exceeds a certain threshold τ, and ‘0’ otherwise. The main objective is to study the performance of diffusion based molecular communication model, the pattern of diffusion was explored, by inspecting how a receiver nanomachine could distinguish a message from one transmitter nanomachine at a distance d or two transmitter nanomachines at different distances. The information molecules were represented as 2-bits by using 22 different values with 22 − 1 thresholds. In the implemented experiments the effects of distance, time, sensed molecular concentration and interference are considered. © 2020, Springer Nature Switzerland AG.

关键词： Diffusion

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2nd international conference on Computational Intelligence, Security and Internet of Things, ICCISIoT 2019

2nd International Conference on Computational Intelligence, ...

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2nd international conference on Computational Intelligence, Security and Internet of Things, ICCISIoT 2019

ISBN: (纸本)9789811536656

The proceedings contain 37 papers. The special focus in this conference is on Computational Intelligence, Security and Internet of Things. The topics include: Machine learning based efficient multi-copy routing for OppIoT networks;An open-hardware approach for IoT enabled smart meter monitoring and controlling system using MQTT protocol;Performance of NOMA-MUSA system for 5G Using m-ZCZ sequences;ioT based multi-sensor data acquisition system for the application of smart agriculture;context-oriented user-centric search system for the IoT based on fuzzy clustering;implementation of a smart embedded system for passenger vessel safety;routine statistical framework to speculate kannada lip reading;empirical analysis on the effect of image compression and denoising using different wavelets on iris recognition;segmentation of chart images using H-Means algorithm;hand vein biometric recognition using local binary pattern;distance calculation of an object in a stereo vision system;comparative assessment of consumer perception on sports brands using data mined from online communities;analytical modelling of internet of vehicles (IoV) by IoT cloud for dipping traffic congestion and safety alert: A review;Object detection using point feature matching based on SURF algorithm;diabetes mellitus prediction using ensemble machine learning techniques;incepting on language structures with phonological and corpus analysis using multilingual computing;morphotactics of manipuri verbs: A finite state approach;proportional analysis of feature extraction for tamil handwritten characters using centroid based method;mobile supported interaction modeling to find engagement of acolyte in live classroom;Integration of BYOD technology in traditional classroom: A statistical approach;preface;real time adaptive street lighting system.

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Combination filter for keypoint detection and pattern recognition in traffic visual surveillance 2

Combination filter for keypoint detection and pattern recogn...

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2nd international conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2019

作者： Snehalatha, O.J. Sureshkumar, E. Govt. Rajiv Gandhi Institute of Technology Dept. of ECE Kottayam India College of Engineering Dept. of ECE Trivandrum India

ISBN: (纸本)9781728102832

The detection of keypoints is very important for several computer applications. There are several ways for keypoint detection and pattern recognition. Earliest work proposes a trainable filter called COSFIRE filter which is a combination of bank of log gabor filters. This proposesd work interested in configuration of more sophisticated combination filter using bank of log gabor filters with bigger support. The specific function that use here to combine filter response is weighted geometric mean, essentially multiplication, which has specific advantages regarding shape recognition and robustness to contrast variations. The proposed work tends to demonstrate the effectiveness of filter in traffic visual surveillance including detection and recognition of traffic signs in complicated scenes. © 2019 IEEE.

关键词： pattern recognition

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Dimension Reduction for Efficient pattern recognition in High Spatial Resolution Data Using Quantum Algorithms 32

Dimension Reduction for Efficient Pattern Recognition in Hig...

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32nd IEEE international System-on-Chip conference (IEEE SOCC)

作者： Mahmud, Naveed El-Araby, Esam Univ Kansas Elect Engn & Comp Sci Lawrence KS 66045 USA

ISBN: (纸本)9781728134826

With the promising advancement in quantum computing technology in the last decade, there is a strong motivation to find suitable applications for quantum algorithms and quantum computers. Domains such as High Energy Physics (IIEP) have an enormous readout count of high-resolution data. Performing pattern recognition on this readout is computationally. , challenging and time-consuming because of the multi-dimensionality of the data. In this paper, we propose a methodology that employs quantum algorithms such as Quantum Wavelet Transform and Grover's search algorithm for time-efficient pattern recognition in data sets that are characterized by high spatial resolution and high dimensionality. The motivation behind using quantum algorithms is the potential speedup relative to classical methods, when performed by a quantum computer. In our proposed methodology, Quantum Wavelet Transform is performed on the high spatial resolution data to reduce its dimensionality while quantum Grover's search algorithm is employed to search for target patterns in the reduced data set. Performing the search operation on data with reduced spatial resolution, minimizes processing overheads and computation times. Moreover, use of quantum techniques yield faster results, compared to classical dimension reduction and search methods. We demonstrate the feasibility of the proposed methodology by emulating the quantum algorithms on classical hardware based on field programmable gate arrays (FPGAs). A high performance reconfigurable computer (IIPRC) was used for the experimental evaluation. The obtained results are favorable towards our proposed approach.

关键词： Quantum computing Quantum Emulation

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