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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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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...

引用

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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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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A Review Study: recognition of Co-located pattern in Video Stream 5

A Review Study: Recognition of Co-located Pattern in Video S...

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5th international conference on Advanced computing and Communication Systems (ICACCS)

作者： Jayaram, C., V Bhajantri, Nagappa U. MYCEM Dept CS&E Mysore Karnataka India Govt Engn Coll Dept CS&E Chamrajnagar Karnataka India

ISBN: (纸本)9781538695333

This review article provides a vast study on automated recognition of co-located patterns in video streaming. Traditionally numerous glitches such as face recognition, pattern or object recognition, scene understanding, co-located pattern recognition etc., have emerged through practices in pattern recognition domain. Our review study, purposes delivering an all-inclusive state-of-the-art review in the domain, and also discourses numerous encounters and trials in relation with its applications and its system. In this assessment, several applications are conversed in boundless aspect.

关键词： pattern recognition Co-location Object detection Frame extraction

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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 ...

引用

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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Face recognition Using Multi-local Descriptors-A Novel Approach 3rd

Face Recognition Using Multi-local Descriptors-A Novel Appro...

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3rd international conference on Smart computing and Informatics (SCI)

作者： Mohanty, Lipika Saloni Satapathy, Suresh Chandra Kalinga Inst Ind Technol Sch Comp Engn Bhubaneswar Odisha India MAQ Software Hyderabad Telangana India

ISBN: (纸本)9789813296909;9789813296893

While face recognition has been a topic of interest for the researchers for quite some time now, most of the advancements and superior results have come out from the field of face recognition in controlled scenarios. As we shift away from the controlled environment like passport images, driver's license etc. to unconstrained environment like images taken from surveillance footage, images taken by bystanders etc. the recognition accuracy significantly decreases. This inconsistency is due to the fact that face images in unconstrained environment have vast variations in parameters like illumination, background detail, pose, expression, occlusion etc. At present times when important disciplines like security and forensics application depends on such systems it can prove to be very useful if a face recognition system in unconstrained environment can give comparable results to the systems in controlled environment. Our work focuses on face recognition using deep feature extraction by concatenating the features of different feature extractors to improve the recognition accuracy in unconstrained environment. We use multiple feature based methods (variants of LBP and LGS) to extract important features from the same image and combine them to form a single feature vector. For classification SVM is used and two face databases viz. ORL face database and LFWCrop (Labeled Faces in the Wild). The experimental results reveal that the proposed method improves the performance of the face recognition system.

关键词： Face recognition Local Binary pattern Local Graph Structure Concatenated feature vector

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2nd international conference on soft computing and Signal Processing, ICSCSP 2019

2nd International Conference on Soft Computing and Signal Pr...

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2nd international conference on soft computing and Signal Processing, ICSCSP 2019

ISBN: (纸本)9789811524745

The proceedings contain 70 papers. The special focus in this conference is on soft computing and Signal Processing. The topics include: Human Pose Estimation and Activity Classification Using Machine Learning Approach;implementation of Interpolation in Credit Card Fraud Detection;A Statistical-Semantic PSO Model for Customer Reviews-Based Question Answering Systems;low-Cost IoT Based Spirometer Device with Silicon Pressure Sensor;recommending Research Article Based on User Queries Using Latent Dirichlet Allocation;an Applied Time Series Forecasting Model for Yield Prediction of Agricultural Crop;design and Implementation of the Protocol for Secure software-Based Remote Attestation in IoT Devices;label Assignment and Sentimental Analysis for a Product Review on Twitter Data;preserving the Privacy of Audio File Using Ideal Secret Sharing Scheme with Cloud Storage;hadoop Performance Acceleration by Effective Data and Job Placement;modified Quick Fuzzy Hypersphere Neural Network for pattern Classification Using Supervised Clustering;third Eye: Assistance for Reading Disability;enhanced Attach Procedure for Prevention of Authentication Synchronisation Failure Attack;static Load Balancing Algorithms in Cloud computing: Challenges and Solutions;a Survey on Text Question Responsive Systems in English and Indian Languages;vehicle Type Classification Using Deep Learning;Deep Learning Approach: Detection of Replay Attack in ASV Systems;acoustic Scene Classification Using Convolutional Neural Network;classification of Microscopic Algae: An Observational Study with AlexNet;Innovative Secure Authentication Interface for Hadoop Cluster Using DNA Cryptography: A Practical Study;a Novel True Colour Image Bit Modification Technique for Image Steganography;A GRU-Based Neural Machine Translation Followed by Proper Noun Transliteration;real-Time Suspicious Activity Detection.

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Open-World Group Retrieval with Ambiguity Removal: A Benchmark

Open-World Group Retrieval with Ambiguity Removal: A Benchma...

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international conference on pattern recognition

作者： Ling Mei Jianhuang Lai Zhanxiang Feng Xiaohua Xie School of Electronics and Information Technology Sun Yat-sen University China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China School of Data and Computer Science Sun Yat-Sen University China

ISBN: (纸本)9781728188089;9781728188096

Group retrieval has attracted plenty of attention in artificial intelligence, traditional group retrieval researches assume that members in a group are unique and do not change under different cameras. However, the assumption may not be met for practical situations such as open-world and group-ambiguity scenarios. This paper tackles an important yet non-studied problem: re-identifying changing groups of people under the open-world and group-ambiguity scenarios in different camera fields. The open-world scenario considers that there are probably non-target people for the probe set appear in the searching gallery, while the group-ambiguity scenario means the group members may change. The open-world and group-ambiguity issue is very challenging for the existing methods because the changing of group members results in dramatic visual variations. Nevertheless, as far as we know, the existing literature lacks benchmarks which target on coping with this issue. In this paper, we propose a new group retrieval dataset named OWGA-Campus to consider these challenges. Moreover, we propose a person-to-group similarity matching based ambiguity removal (P2GSM-AR) method to solve these problems and realize the intention of group retrieval. Experimental results on OWGA-Campus dataset demonstrate the effectiveness and robustness of the proposed P2GSM-AR approach in improving the performance of the state-of-the-art feature extraction methods of person re-id towards the open-world and ambiguous group retrieval task.

关键词： Visualization Benchmark testing Cameras Feature extraction Robustness pattern recognition Probes

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Multi-Task Learning with Deep Dual-Path Network for Facial Attribute recognition 2020

Multi-Task Learning with Deep Dual-Path Network for Facial A...

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Proceedings of the 2020 9th international conference on computing and pattern recognition

作者： Xinyu Lai Si Chen Da-Han Wang Shunzhi Zhu School of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding

ISBN: (纸本)9781450387835

Facial attribute recognition is a popular and challenging research topic in computer vision. In the traditional deep learning based attribute recognition methods, the mid-level network features and the differences between attribute groups are not fully explored. To solve the above problem, a deep dual-path network is proposed for facial attribute recognition. In the multi-task learning framework, two sub-networks are employed to respectively extract the features of two attribute groups, i.e., local attributes and global ones, and designed with both different scale images and different depth networks. Furthermore, an adaptive Focal loss penalty scheme is developed to automatically assign weights to handle the class imbalance problem for facial attribute recognition. Experimental results on the challenging CelebA dataset show that the proposed method achieves the better performance than state-of-the-art methods.

关键词： multi-task learning dual-path network Facial attribute recognition attribute groups

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