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检索条件"任意字段=International Conference of Soft Computing and Pattern Recognition"

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Multi-Directional Ternary pattern for Facial Expression recognition 6

Multi-Directional Ternary Pattern for Facial Expression Reco...

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6th IEEE international conference on Advances in computing, Communication and Control, ICAC3 2019

作者： Dorage, Kiran Sudam Raveendran, Smitha Ramrao Adik Institute of Technology Department of Electronics Engineering Sector 7 Nerul Navi Mumbai India

ISBN: (纸本)9781728123868

Multi-Directional Ternary pattern (M-DTP) is the face descriptor which reduces the noise and extracts the key expression related characteristics of the input face image to overcome the recent challenges encountered in unconstrained face recognition. In M-DTP, the derivative of the Gaussian operator is utilized to transform the gray scale facial image into the multi-directional gradient image that is vigorous to changes in the illumination. This pre-processing is introduced to minimize the challenges due to the variations in illumination and unconstrained face expression recognition. Further, by making use of directional information and ternary pattern the emotion-related features are extracted from the face. The proposed multi-level histogram features extracted from the ternary face code technique delivers the finer grain description of facial emotions to improve the facial expression performance under various illumination conditions. The features extracted using M-DTP are high-dimensional, therefore to reduce the dimensionality Principal Component Analysis (PCA) is used. The Support Vector Machine (SVM) classifier is utilized to perform and evaluate the classification performance using different datasets. © 2019 IEEE.

关键词： Principal component analysis

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PProCRC: Probabilistic Collaboration of Image Patches for Fine-grained Classification

PProCRC: Probabilistic Collaboration of Image Patches for Fi...

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international conference on Image and Vision computing New Zealand, IVCNZ

作者： Tapabrata Chakraborti Brendan McCane Steven Mills Umapada Pal University of Otago tapabrata mccane Computer Vision and Pattern recognition Unit Indian Statistical Institute

ISBN: (数字)9781728185798

ISBN: (纸本)9781728185804

We present a conditional probabilistic framework for collaborative representation of image patches. It incorporates background compensation and outlier patch suppression into the main formulation itself, thus doing away with the need for pre-processing steps to handle the same. A closed form non-iterative solution of the cost function is derived. The proposed method (PProCRC) outperforms earlier CRC formulations: patch based (PCRC, GP-CRC) as well as the state-of-the-art probabilistic (ProCRC and EProCRC) on three fine-grained species recognition datasets (Oxford Flowers, Oxford-IIIT Pets and CUB Birds) using two CNN backbones (Vgg-19 and ResNet-50).

关键词： Visualization Image recognition Collaboration Probabilistic logic Cost function Birds Task analysis

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Action recognition framework based on normalized local binary pattern 2nd

Action recognition framework based on normalized local binar...

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2nd international conference on Advanced computing and Intelligent Engineering, ICACIE 2017

作者： Singhal, Shivam Tripathi, Vikas Department of Computer Science and Engineering Graphic Era University DehradunUttarakhand248002 India

ISBN: (纸本)9789811317071

Human action recognition in computer vision has become dexterous in detecting the abnormal activities to fortify safe events. This paper presents an efficient action recognition algorithm which is based on local binary pattern (LBP). The implementation of this approach can be used for action recognition in small premises such as ATM rooms by focusing on the LBP feature extraction via spatiotemporal relations. We also focus on decreasing the descriptor values by normalizing computed histogram bins. The results through ATM dataset demonstrate the enhancement in action recognition problem under different extensions. The normalized features obtained are classified using random forest classifier. In our study, it is shown that normalized version of LBP surpasses the conventional LBP descriptor with an average accuracy of 83%. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Feature extraction

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Recognizing soft Biometric on Pedestrian Using Convolutional Neural Network

Recognizing Soft Biometric on Pedestrian Using Convolutional...

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Data Science and Its Applications (ICoDSA), international conference on

作者： Tri Broto Siswoyo Anditya Arifianto Kurniawan Nur Ramadhani School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9781728182353

ISBN: (纸本)9781728182360

Video surveillance is very important in automatic surveillance. It is usually used to monitor any criminal activity and help to find the perpetrator. In the search for individual among pedestrians when the face image can no longer be recognized, then what can be recognized is the soft-biometric or attribute appears on the pedestrian. The attributes can be in the form of clothes or accessories worn by pedestrians. However, recognizing attributes is a more challenging task as the number of variations of the attribute is increasing. Therefore, there is a need of a system that can effectively recognize the soft-biometric of pedestrians that are expected to help in video surveillance. In this study, we propose a pedestrian soft-biometric recognition system that was built using the Convolutional Neural Network (CNN) method so that it can search for pedestrians wearing various attributes. In recognizing pedestrian soft-biometric, the authors conducted a study using transfer learning from the Resnet50 architecture. As this case belongs to the multi-label classification case, we also observed several scenarios for determining the threshold to better classify the appearance of attributes. Our system able to recognize 35 different attributes with the best results of this research indicate the average accuracy of 90.82% with 80.84% of precision and 81.08% of recall using test data.

关键词： Feature extraction Convolution Neurons Image recognition Computer architecture Measurement Training

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Behavior Analysis for Nursing Home Monitoring System 1st

Behavior Analysis for Nursing Home Monitoring System

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1st international conference on Big Data Analysis and Deep Learning (ICBDL)

作者： Seint, Pann Thinzar Zin, Thi Thi Univ Miyazaki Grad Sch Engn Miyazaki Japan

ISBN: (纸本)9789811308697;9789811308680

In this paper, we describe the nursing home monitoring system based on computer vision. This system is aimed for an effective and automatic take care of aged persons to monitor appropriate medication intake. Skin region detection for mouth and hand tracking and color label detection for water and medication bottles tracking are mainly performed for initialization. To differentiate the hand and face region, we use the regional properties of head with online learning. Tracking is done by the minimum Eigen values detection. The overlapping area ratios of desired object to body parts are simply used as feature vectors and pattern recognition Neural Network is proposed for the decision of simplest action. This paper presents the 7 types of simple actions recognition for medication intake and our experimental results give the promising results.

关键词： Medication intake monitoring Mouth and hand tracking Eigen values Feature vectors pattern recognition neural network

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Automatic Vehicle Identification Through Visual Features 17

Automatic Vehicle Identification Through Visual Features

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17th international conference on Advances in Mobile computing and Multimedia (MoMM)

作者： Ahmad, Imran Shafiq Boufama, Boubakeur Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781450371780

Detection and recognition of a vehicle license plate is a fundamental requirement of any intelligent transport system, primarily to support activities like finding a stolen vehicle, vehicle surveillance/tracking, parking-toll collection, traffic flow planning and management, etc. However, a license plate can easily be stolen and/or changed by those with criminal intent to conceal their identity. This paper proposes a new vehicle identification system to obtain high degree of accuracy and success rate by not only considering the license plate but also shape of the vehicle. The proposed system is based on four steps: license plate detection, license plate recognition, license plate jurisdiction (province) detection and the vehicle shape detection. In the proposed system, the features are converted into local binary pattern (LBP) and Histogram of Oriented Gradients (HOG) as training dataset. To obtain high degree of accuracy in real-time application, a novel method based on cascaded classifiers is used to update the system. The proposed system allows us to store features of vehicles and related information in the database, thus, allowing us to automatically detect any discrepancy between a license plate and vehicle associated with it.

关键词： Intelligent Transport System License Plate Detection Vehicle Identification Histogram of Oriented Gradients (HOG) Local Binary pattern (LBP) Binarization Convolutional Neural Network (CNN)

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Object Tracking and Anomaly Detection in Live Environment: A Survey

Object Tracking and Anomaly Detection in Live Environment: A...

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international conference on Advances in computing and Communication Engineering (ICACCE)

作者： Nikam, Gitanjali Ganpatrao Gupta, Simon Chourasiya, Priya Yadav, Medha Natl Inst Technol Dept Comp Applicat Kurukshetra Haryana India

ISBN: (数字)9781728132501

ISBN: (纸本)9781728132501

Numerous strategies are executed for the identification of peculiarities on the framework. Irregularities based strategies are looking at as proficient from that client purpose based methodology is favored for the Usage of oddity recognition. Presently multi day's decent variety of abnormality strategies are accessible In view of this, it is difficult to think about these strategies. To know this, diverse abnormality Identification is checked on and make a nitty-gritty examination in this. This paper contains examination consider of various oddity discovery strategies. Interruption perception has gained a wide consideration and turns into a gainful field for different looks into, and as yet is the subject of all-inclusive intrigue by specialists. The interruption recognition network still stands up to troublesome circumstance even after numerous long periods of research. Decreasing the tremendous number of wrong cautions all through the procedure of recognizing obscure assault designs stays vague issue. In any case, different research results as of late have appeared there are potential answers for this issue. Inconsistency identification is a key issue of interruption recognition in which Irritations of ordinary conduct determine an appearance of planned or unintended impact assaults, flaw, deformities, and others. This paper displays an outline of research bearings for applying composed and disorderly strategies to handle the issue of inconsistency discovery. The references referred to will cover the huge hypothetical issues lead the analyst in fascinating examination bearings.

关键词： Anomaly detection pattern recognition Oddity Identification Bayesian system

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The comparison of ear recognition methods under different illumination effects and geometrical changes 2

The comparison of ear recognition methods under different il...

引用

2nd international conference on Advances in Big Data, computing and Data Communication Systems, icABCD 2019

作者： Ntshangase, Cynthia Sthembile Mathekga, Dick Modelling and Digital Science Council for Scientific and Industrial Research Pretoria South Africa

ISBN: (纸本)9781538692363

This paper presents the ear shape permanence study. The main purpose of this study is to determine the permanence of the ear shape and when does the ear stop developing. This is done by comparing ear recognition methods using images affected by illumination and geometrical changes. However, current work determine the most suitable method that can be used for ear recognition of young children that still under-go different geometrical changes and skin complexion changes. Therefore, suitable algorithm should be less sensitive to illumination and more sensitive to growth in order to be able to track significant changes of the ear caused by growth. Methods that are evaluated are the Histogram of Oriented Gradients (HOG), patterns of Oriented Edge Map (POEM), Local Binary patterns (LBP) and Gabor Filters. To perform the evaluation, 1000 ear images were generated from 100 ear images, 10 per each subject. For each subject, all 10 images have different illumination and another 10 have different geometrical changes. The results obtained show that a combination of HOG and LBP is suitable for ear recognition under geometrical and illumination changes. © 2019 IEEE.

关键词： Local binary pattern

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Sequential pattern Learning of Visual Features and Operation Cycles for Vision-Based Action recognition of Earthmoving Excavators

Sequential Pattern Learning of Visual Features and Operation...

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ASCE international conference on computing in Civil Engineering (i3CE)

作者： Kim, Jinwoo Chi, Seokho Choi, Minji Seoul Natl Univ Dept Civil & Environm Engn Constuct Innovat Lab 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ Inst Construct & Environm Engn 1 Gwanak Ro Seoul 08826 South Korea

ISBN: (纸本)9780784482438

Excavator action recognition is a major task for cycle-time and productivity analysis. Many researchers have developed vision-based methods and showed promising results for automated action recognition. However, previous methods did not fully incorporate sequential working patterns of excavators although it is crucial to explain their natural operation procedure. To overcome the limitations, this paper proposes a vision-based excavator action recognition framework that considers the sequential patterns of visual features and operation cycles. The framework was validated with 1,340 images collected from actual earthmoving sites, and the average precisions and recall rates were 90.9% and 89.2% respectively. This research performed additional experiments with the other CNN model, and the precision and recall rate were decreased to 77.5% and 71.6% respectively. The experimental results showed the applicability of the developed framework and the positive impacts of sequential pattern learning. This study can contribute to more reliable automated cycle-time analysis and productivity measurement.

关键词： Excavators

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46th international conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2020

46th International Conference on Current Trends in Theory an...

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46th international conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2020

ISBN: (纸本)9783030389185

The proceedings contain 60 papers. The special focus in this conference is on Current Trends in Theory and Practice of Computer Science. The topics include: Faster STR-EC-LCS Computation;kernels of Sub-classes of Context-Free Languages;minimal Unique Substrings and Minimal Absent Words in a Sliding Window;on Synthesis of Specifications with Arithmetic;on the Average State Complexity of Partial Derivative Transducers;on the Difference Between Finite-State and Pushdown Depth;online Scheduling with Machine Cost and a Quadratic Objective Function;parallel Duel-and-Sweep Algorithm for the Order-Preserving pattern Matching;parameterized Complexity of Synthesizing b-Bounded (m, n)-T-Systems;the Lost Recipes from the Four Schools of Amathus: Invited Talk Extended Abstract;parameterized Dynamic Variants of Red-Blue Dominating Set;refined Parameterizations for computing Colored Cuts in Edge-Colored Graphs;simple Distributed Spanners in Dense Congest Networks;the Order Type of Scattered Context-Free Orderings of Rank One Is Computable;up-to Techniques for Branching Bisimilarity;crowd Detection for Drone Safe Landing Through Fully-Convolutional Neural Networks;explaining Single Predictions: A Faster Method;inferring Deterministic Regular Expression with Unorder;POI Recommendation Based on Locality-Specific Seasonality and Long-Term Trends;selection of a Green Logical Data Warehouse Schema by Anti-monotonicity Constraint;sharing Energy for Optimal Edge Performance;the HyperBagGraph DataEdron: An Enriched Browsing Experience of Datasets: Track: Foundation of Data Science and Engineering;towards the Named Entity recognition Methods in Biomedical Field;vietnamese Punctuation Prediction Using Deep Neural Networks;a Light-Weight Tool for the Self-assessment of Security Compliance in software Development – An Industry Case;Do People Use Naming Conventions in SQL Programming?.

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