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Performance Evaluation between RGB and YCrCb in TC-SF-CNNLS for Vehicle Type recognition System

Performance Evaluation between RGB and YCrCb in TC-SF-CNNLS ...

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international conference on Industrial Engineering and Applications (ICIEA)

作者： Suryanti Awang Nik Mohamad Aizuddin Nik Azmi Faculty of Computing Soft Computing & Intelligent Systems (SPINT) Universiti Malaysia Pahang Kuantan Malaysia

ISBN: (纸本)9781665428958;9781665428965

In this paper, the performance impact of vehicle type recognition (VTR) using Three-Channel of Sparse-Filtered Convolutional Neural Network with Layer-Skipping strategy (TC-SF-CNNLS) technique is observed. Unlike other techniques that lacking in extracting unique features, TC-SF-CNNLS able to extract the unique features from colour image. However, colour image can be in many schemes. Thus, we implemented the technique with two colour schemes which is RGB and YCrCb to analyze which one is able to give better performance in the VTR. We tested the implementation with a benchmark dataset known as BIT and self-obtained dataset known as SPINT. The results are observed based on the accuracy performance that represented in a confusion table. Based on the results, YCrCb outperformed RGB with the highest average accuracy 90.5% and 90.4% for both datasets, respectively. We can conclude that TC-SF-CNNLS is best performed when YCrCb is used as the colour scheme in extracting the vehicle features from the colour image.

关键词： Performance evaluation Image segmentation Image recognition Image color analysis conferences Color Industrial engineering

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HUMA'20: 1st international Workshop on Human-Centric Multimedia Analysis 20

HUMA'20: 1st International Workshop on Human-Centric Multime...

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28th ACM international conference on Multimedia (MM)

作者： Liu, Wu Gan, Chuang Song, Jingkuan Zhang, Dingwen Huang, Wenbing Smith, John AI Res JDcom Beijing Peoples R China MIT IBM Watson AI Lab Cambridge MA USA UESTC Chengdu Peoples R China Xidian Univ Xian Peoples R China Tsinghua Univ Beijing Peoples R China IBM TJ Watson Res Ctr Yorktown Hts NY USA

ISBN: (纸本)9781450379885

The First international Workshop on Human-Centric Multimedia Analysis is concentrated on the tasks of human-centric analysis with multimedia and multimodal information. It is one of the fundamental and challenging problems of multimedia understanding. The human-centric multimedia analysis involves multiple tasks such as face detection and recognition, human body pattern analysis, person re-identification, human action detection, person tracking, human-object interaction, and so on. Today, multiple multimedia sensing technologies and large-scale computing infrastructures are producing at a rapid velocity a wide variety of big multi-modality data for human-centric analysis, which provides rich knowledge to help tackle these challenges. Researchers have strived to push the limits of human-centric multimedia analysis in a wide variety of applications, such as intelligent surveillance, retailing, fashion design, and services. Therefore, this workshop aims to provide a platform to bridge the gap between the communities of human analysis and multimedia.

关键词： Human-centered computing Human Analysis Face Analysis Multimedia and Multimodal Analysis

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Impact of Variability Issues of Resistive Memory Synapses on pattern recognition Systems 17

Impact of Variability Issues of Resistive Memory Synapses on...

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17th international SoC Design conference (ISOCC)

作者： Woo, Jiyong Lee, Miyoung Kim, Jeong Hun Im, Jong-Pil Im, Solyee Kim, Yeriaron Moon, Seung Eon Lee, Joohyun Elect & Telecommun Res Inst ICT Creat Res Lab Gwangju South Korea Elect & Telecommun Res Inst Artificial Intelligence Res Lab Gwangju South Korea Kyungpook Natl Univ Sch Elect Engn Daegu South Korea

ISBN: (纸本)9781728183312

Neuromorphic systems heavily rely on multiply-accumulate (MAC) operations to recognize image patterns. The computation performed repeatedly can be speeded up by a cross-point array. In the architecture, a weight storage element should consist of resistive memory and additional selector. To evaluate the recognition accuracy of handwritten digits, we construct cross-point arrays in simulation with the 2-bit weights obtained from Ta2O5 based resistive memory experimentally connected in series with multi tunnel-barrier selector. Based on the high accuracy close to 95 % obtained from quantized convolutional neural networks, how the accuracy is related to non-ideal factors that can be generated from array and device is studied.

关键词： Resistive memory synapse neuromoprhic computing

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Controlled prosthesis for upper limb amputees using pattern recognition techniques 11

Controlled prosthesis for upper limb amputees using pattern ...

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11th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2020

作者： Anisha, C.D. Arulanand, N. PSG College of Technology Dept. of CSE Coimbatore India

ISBN: (纸本)9781713818519

Upper limb amputees are individuals who lost their hands due to trauma and injury. Controlled prosthesis based on surface Electromyography (sEMG) signals recovers the lost functionality for upper limb amputees. Several pattern recognition techniques help amputees in controlling prosthesis by classifying different upper limb movements intuitively. The proposed framework performs an analysis of classification of upper limb movements on real time and retrieved surface Electromyography (sEMG) signal data. Band pass filter is used in pre-processing stage and Time Domain features are extracted. The Features selection analysis is also performed wherein Extra Tree classifier and histogrambased features is used for retrieved and real time data respectively. The pre-processed real time and retrieved data with features and classes are fed to the classification stage. The hyperparameters of the classifiers are tuned using Grid Search Method. The classifiers to be stacked are Adaptive Boosting, Gradient Boosting Machine, Quadratic Discriminant Analysis, Linear Discriminant Analysis, K Nearest Neighbor and Random Forest. The properties of the proposed stacking classifier are diverse and same error rate classifiers procured using McNemar's hypothesis testing. The evaluation metrics considered are Accuracy, Precision, Recall and F1 Score. The evaluation results signify that stacking classifier provides a highest accuracy in all experiments. © Grenze Scientific Society, 2020.

关键词： Decision trees

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A new geodesic-based feature for characterization of 3D shapes: application to soft tissue organ temporal deformations

A new geodesic-based feature for characterization of 3D shap...

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international conference on pattern recognition

作者： Karim Makki Amine Bohi Augustin C. Ogier Marc Emmanuel Bellemare Aix Marseille Univ Université de Toulon CNRS LIS Marseille France

Spatio-temporal feature descriptors are of great importance for characterizing the local changes of 3D deformable shapes. In this study, we propose a method for characterizing 3D shapes from point clouds and we show a direct application on a study of organ temporal deformations. As an example, we characterize the behavior of the bladder during forced respiratory motion with a reduced number of 3D surface points: first, a set of geodesically equidistant points representing the vertices of quadrilateral mesh for the organ surface are tracked throughout a long dynamic MRI sequence using a large deformation diffeomorphic metric mapping (LDDMM) framework. Second, a novel 3D shape descriptor invariant to translation, scale and rotation is proposed for characterizing the temporal organ deformations by employing an Eulerian Partial Differential Equations (PDEs) methodology. We demonstrate the robustness of our feature on both synthetic 3D shapes and realistic dynamic Magnetic Resonance Imaging (MRI) data sequences portraying the bladder deformation during a forced breathing exercise. Promising results are obtained, showing that the proposed feature may be useful for several computer vision applications such as medical imaging, aerodynamics and robotics.

关键词： Three-dimensional displays Shape Tracking Magnetic resonance imaging Biological systems Bladder Aerodynamics

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Wearable Sensors for Activity Analysis using SMO-based Random Forest over Smart home and Sports Datasets 3

Wearable Sensors for Activity Analysis using SMO-based Rando...

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3rd international conference on Advancements in Computational Sciences (ICACS)

作者： Tahir, Sheikh Badar Ud Din Jalal, Ahmad Batool, Mouazma Air Univ Dept Comp Sci Islamabad Pakistan

ISBN: (数字)9781728142357

ISBN: (纸本)9781728142357

Human activity recognition using MotionNode sensors is getting prominence effect in our daily life logs. Providing accurate information on human's activities and behaviors is one of the most challenging tasks in ubiquitous computing and human-Computer interaction. In this paper, we proposed an efficient model for having statistical features along SMO-based random forest. Initially, we processed a 1-D Hadamard transform wavelet and 1-D LBP based extraction algorithm to extract valuable features. For activity classification, we used sequential minimal optimization along with Random Forest over two benchmarks USC-HAD dataset and IMSB datasets. Experimental results show that our proposed model can compete with other state-of-the-art methods and can be effectively used to recognize robust human activities in terms of efficiency and accuracy.

关键词： Gait Analysis 1D Local Binary pattern (LBP) MotionNode Statistical features Wearable sensors

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Real-time texture extraction based on the improved median robust extended local binary pattern 9

Real-time texture extraction based on the improved median ro...

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9th international conference on computing and pattern recognition, ICCPR 2020

作者： Wang, Mengnan Zhang, Yanjun School of Information and Electronics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781450387835

Local binary patterns (LBP) are considered as the most computation efficient and high-performance texture features. Among all variants of the LBPs, Median Robust Extended Local Binary pattern (MRELBP) [1] is considered as one of the highest-performance one. Based on the MRELBP, this paper proposes an improved real-time texture extraction architecture implemented on a field-programmable gate array (FPGA) device. The fixed-point fraction is used to replace the double float-point fraction, only results in less than 1% difference to the accuracy. The proposed system is implemented on Xilinx Zynq-7045, and the extraction time is linear with the size of the input image. The extraction time of the 128x128 image in the dataset Outex-TC is merely 85.64μs, which is more than 4800 times faster than Liu et al. implemented on MATLAB [2]. © 2020 ACM.

关键词： MATLAB

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Extracting Discriminative Features for Cross-View Gait recognition Based on the Attention Mechanism

Extracting Discriminative Features for Cross-View Gait Recog...

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IEEE international conference on Computational Science and Engineering, CSE

作者： Ruicheng Sun Shuo Han Weihang Peng Hanxiang Zhuang Xin Zeng Xingang Liu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Glasgow College University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665416610

Human identification based on gait biometrics has become a popular research topic of computer vision and pattern recognition due to its great potential in public security and surveillance system. However, the recognition accuracy can be seriously degraded because of the appearance differences caused by view angle variation. To tackle this problem, we propose a method based on convolutional neural network (CNN) and attention mechanism to solve the cross-view problem in gait recognition. In the proposed algorithm, we firstly extract the features based on CNN structure and then the Horizontal Splitting operation is done to obtain the feature partitions in different granularities. After that, the attention mechanism is utilized to calculate the attention scores of the input partitions on both spatial and channel domain and finally the group of feature vectors can be obtained to determine the corresponding identity. In order to verify the effectiveness of the proposed method, the experiments are done based on two popular gait datasets–CASIA-B and OU-ISIR LP. The results show that the proposed model can effectively extract the discriminative gait features robust to view angle variation and improve the crossview gait recognition accuracy compared with the state-of-the-arts.

关键词： Computer vision Scientific computing Surveillance conferences Feature extraction pattern recognition Partitioning algorithms

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KBP: Mining Block Access pattern for I/O Prediction with K-Truss

KBP: Mining Block Access Pattern for I/O Prediction with K-T...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Jia Ma Xianqi Zheng Yubo Liu Zhiguang Chen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

Block prefetching is a technology widely used to improve the I/O efficiency of storage systems. Block access pattern prediction is a key part of the prefetching algorithm. However, existing block access pattern prediction methods cannot achieve the goals of low overhead, real-time, and self- adaptability at the same time. In this paper, we propose a real-time prediction method, called KBP (K-Truss-based Block access pattern Prediction). KBP uses SA (Sequential Access) Filter to identify and filter sequential access patterns to distinguish different patterns. Then, KBP uses K-Truss dense subgraph algorithm to detect compound access patterns, so as to use the time and space advantages of the K-Truss algorithm to reduce the overhead of access pattern recognition and make it possible for KBP to run in real time. Furthermore, KBP uses online reinforcement learning to achieve the goal of self- adaptability. We evaluate KBP in real-world workloads and the results show that KBP can improve the hit rate of 11.2% compared to the state-of-the-art prefetching algorithms on average.

关键词： Prefetching Reinforcement learning Prediction methods Filtering algorithms Prediction algorithms Real-time systems pattern recognition

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15th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th international conference on Harmony Search, soft computing and Applications, ICHSA 2019

15th International Conference on Natural Computation, Fuzzy ...

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15th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th international conference on Harmony Search, soft computing and Applications, ICHSA 2019

ISBN: (纸本)9783030324551

The proceedings contain 227 papers. The special focus in this conference is on Natural Computation, Fuzzy Systems and Knowledge Discovery. The topics include: Apple Freshness recognition Technology Based on Gas Sensors;CDIA: A Feasible Community Detection Algorithm Based on Influential Nodes in Complex Networks;context-Sensitive Cross- and Auto-correlation Based Supervised Change Detection;cultural Ranking of Countries Using the Analytic Hierarchy Process Methodology;sustainable Performance Evaluation of Urban Mobility Projects Using Multicriteria Group Decision Making Approach;A Parallel Fuzzy Method to Reduce Mixed Gaussian-Impulsive Noise in CT Medical Images;structural Parameters Optimization and Grey Relational Analysis in Honeycomb Spiral Heat Exchangers;interval Valued Generalised Fuzzy soft Expert Set and Its Application;a Review of the Theory and Method for New Developed Feedforward Neural Networks;awareness Learning for Balancing Performance and Diversity in Neural Network Ensembles;a Model and an Algorithm for Empty Car Distribution in Railway Transportation;levelized Cost of Energy Optimization Method for the Dish Solar Thermal Power Generation System;a Novel Ant Colony Optimization Algorithm with Dynamic Control Population for Community Detecting;a Hybrid Bat Algorithm Based on Combined Semantic Measures for Word Sense Disambiguation;a Multi-objective Optimization Algorithm Based on Monarch Butterfly Optimization;an Improved Quantum Genetic Algorithm Based on Population Partition and Dynamic Probability Amplitude;Sink Location Privacy Protection Algorithm Based on Tangential Path in WSN;global Features of Fused Frame Relationships Help Video Classification;study on the Energy Dissipation of Two Lane Traffic Flow with Lane Reduction;chaos and Quasi-period in Erbium-Doped Fiber Laser.

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