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CROWD COUNTING USING DEEP LEARNING BASED HEAD DETECTION 21

CROWD COUNTING USING DEEP LEARNING BASED HEAD DETECTION

引用

IS and T International symposium on Electronic Imaging: 21st image processing: Algorithms and Systems, IPAS 2023

作者： Hassan, Maryam Hussain, Farhan Khan, Sultan Daud Ullah, Mohib Yamin, Mudassar Ullah, Habib NUST College of Electrical & Mechanical Engineering Rawalpindi Pakistan Department of Computer Science National University of Technology Islamabad Pakistan Norwegian University of Science and Technology Gjøvik2815 Norway

Scale invariance and high miss detection rates for small objects are some of the challenging issues for object detection and often lead to inaccurate results. This research aims to provide an accurate detection model for crowd counting by focusing on human head detection from natural scenes acquired from publicly available datasets of Casablanca, Hollywood-Heads and Scut-head. In this study, we tuned a yolov5, a deep convolutional neural network (CNN) based object detection architecture, and then evaluated the model using mean average precision (mAP) score, precision, and recall. The transfer learning approach is used for fine-tuning the architecture. Training on one dataset and testing the model on another leads to inaccurate results due to different types of heads in different datasets. Another main contribution of our research is combining the three datasets into a single dataset, including every kind of head that is medium, large and small. From the experimental results, it can be seen that this yolov5 architecture showed significant improvements in small head detections in crowded scenes as compared to the other baseline approaches, such as the Faster R-CNN and VGG-16-based SSD MultiBox Detector. © 2023 Society for Imaging Science and Technology. All rights reserved.

关键词： Object detection

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image-Based Visualization of Classifier Decision Boundaries

Image-Based Visualization of Classifier Decision Boundaries

引用

brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Francisco Caio M. Rodrigues Roberto Hirata Alexandru Cristian Telea Institue of Mathematics and Statistics University of São Paulo Johann Bernoulli Institute University of Groningen

Understanding how a classifier partitions a high-dimensional input space and assigns labels to the parts is an important task in machine learning. Current methods for this task mainly use color-coded sample scatterplots, which do not explicitly show the actual decision boundaries or confusion zones. We propose an image-based technique to improve such visualizations. The method samples the 2D space of a dimensionality-reduction projection and color-code relevant classifier outputs, such as the majority class label, the confusion, and the sample density, to render a dense depiction of the high-dimensional decision boundaries. Our technique is simple to implement, handles any classifier, and has only two simple-to-control free parameters. We demonstrate our proposal on several real-world high-dimensional datasets, classifiers, and two different dimensionality reduction methods.

关键词： Two dimensional displays Visualization Training Dimensionality reduction Color Brightness Task analysis

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A real-time webcam based Eye Ball Tracking System using MATLAB

A real-time webcam based Eye Ball Tracking System using MATL...

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International symposium for Design and Technology of Electronics Packages (SIITME)

作者： Osama Mazhar Taimoor Ali Shah Muhammad Ahmed Khan Sameed Tehami Computer Vision and Robotics University of Burgundy Burgundy France Electrical Engineering & Information Technology Technische Hochschule Ingolstadt Ingolstadt Germany Biomedical Engineering NED University of Engineering & Technology Karachi Pakistan

Eye Ball Tracking System is a device which is intended to assist patients that cannot perform any voluntary tasks related to daily life. Patients who only can control their eyes can still communicate with the real-world using the assistive devices like one proposed in this paper. This device provides a human computer interface in order to take decisions based on their eye movement. A real time data stream is captured via webcam that transfers data serially to MATLAB. Then a sequential image processing scheme segments the iris of the eye and calculates the centroid, thereby generating control signal with the help of a reference axis. The control signals are then used to manipulate the position of a motorized platform via USB-microcontroller interface.

关键词： image processing MATLAB Gaze tracking Webcams Microcontrollers Real-time systems computers

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Deep Transfer Learning for Segmentation of Anatomical structures in Chest Radiographs

Deep Transfer Learning for Segmentation of Anatomical Struct...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Hugo Oliveira Jefersson dos Santos Department of Computer Science Universidade Federal de Minas Gerais Minas Gerais

Segmentation of anatomical structures in Chest Posterior-Anterior Radiographs is a classical task on biomedical image analysis. Deep Learning has been widely used for detection and diagnosis of illnesses in several medical image modalities over the last years, but the portability of deep methods is still limited, hampering the reusability of pre-trained models in new data. We address this problem by proposing a novel method for Cross-Dataset Transfer Learning in Chest X-Ray images based on Unsupervised image Translation architectures. Our Transfer Learning approach achieved Jaccard values of 88.20% on lung field segmentation in the Montgomery Set by using a pre-trained model on the JSRT dataset and no labeled data from the target dataset. Several experiments in unsupervised and semi-supervised transfer were performed and our method consistently outperformed simple fine-tuning when a limited amount of labels is used. Qualitative analysis on the tasks of clavicle and heart segmentation are also performed on Montgomery samples and pre-trained models from JSRT dataset. Our secondary contributions encompass several experiments in anatomical structure segmentation on JSRT, achieving state-of-the-art results in lung field (96.02%), heart (89.64%) and clavicle segmentation (87.30%).

关键词： image segmentation Task analysis Lung computer architecture Heart Biomedical imaging Semantics

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Low-Cost Hardening of image processing Applications Against Soft Errors

Low-Cost Hardening of Image Processing Applications Against ...

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IEEE International symposium on Defect and Fault Tolerance in VLSI Systems

作者： Ilia Polian Bernd Becker Masato Nakasato Satoshi Ohtake Hideo Fujiwara Graduate School of Information Science Nara Institute of Science and Technology Ikoma City Nara Japan Albert-Ludwigs-University Freiburg im Breisgau Germany

ISBN: (纸本)9780769527062;076952706X

image processing systems are increasingly used in safety-critical applications, and their hardening against soft errors becomes an issue. The authors propose a methodology to identify soft errors as uncritical based on their impact on the system's functionality. The authors call a soft error uncritical if its impact is provably limited to image perturbations during a very short period of time (number of cycles) and the system is guaranteed to recover thereafter. Uncritical errors do not require hardening as their effects are unperceivable for the human user of the system. The authors focus on soft errors in the motion estimation subsystem of MPEG-2 and introduce different definitions of uncritical soft errors in that subsystem. A method is proposed to automatically determine uncritical errors and provide experimental results for various parameters. The concept can be adapted to further systems and enhance existing methods

关键词： image processing Costs Radiation hardening Redundancy Circuits Silicon Frequency estimation Consumer electronics Switches computer errors

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Robust Feature Spaces from Pre-Trained Deep Network Layers for Skin Lesion Classification

Robust Feature Spaces from Pre-Trained Deep Network Layers f...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Fernando Pereira dos Santos Moacir Antonelli Ponti Institute of Mathematical and Computer Sciences (ICMC) University of São Paulo (USP) São Carlos SP Brazil

The incidence of skin cancer in the world population is a public health concern, and the first diagnosis takes into account the appearance of lesions on skin. In this context, automated methods to aid the screening for malign lesions can be an important tool. However, the efficiency of developed methods depends directly on the quality of the generated feature space which may vary when considering different image datasets and sources. We present a detailed study of feature spaces obtained from deep convolutional networks (CNNs), using the benchmark PH2 dataset, considering three CNN architectures, as well as investigating different layers, impact of dimensionality reduction, use of colour quantisation and noise addition. Our results show that, features have discriminative capability comparable to competing methods with balanced accuracy 94%, and 95% with noise injection. Additionally, we present a study of fine-tuning and generalisation across image quantisation and noise levels, contributing to the discussion of learning features from deep networks and offering a guideline for future works.

关键词： Feature extraction Lesions Skin image color analysis Skin cancer Radio frequency

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Computational Science and Its Applications – ICCSA 2021 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Osvaldo Gervasi Beniamino Murgante Sanjay Misra Chiara Garau Ivan Blečić David Taniar Bernady O. Apduhan Ana Maria A. C. Rocha Eufemia Tarantino Carmelo Maria Torre

ISBN: (数字)9783030870133

ISBN: (纸本)9783030870126

The ten-volume set LNCS 12949 – 12958 constitutes the proceedings of the 21st International Conference on Computational Science and Its Applications, ICCSA 2021, which was held in Cagliari, Italy, during September 13 – 16, 2021. The event was organized in a hybrid mode due to the Covid-19 *** 466 full and 18 short papers presented in these proceedings were carefully reviewed and selected from 1588 submissions. The books cover such topics as multicore architectures, blockchain, mobile and wireless security, sensor networks, open source software, collaborative and social computing systems and tools, cryptography, applied mathematics human computer interaction, software design engineering, and others. Part IX of the set includes the proceedings of the following events: 13th International symposium on Software Engineering Processes and Applications (SEPA 2021); International Workshop on Sustainability Performance Assessment: models, approaches and applications toward interdisciplinary and integrated solutions (SPA 2021).

关键词： computer Communication Networks Information Systems and Communication Service Artificial Intelligence computer System Implementation Software Engineering image processing and computer Vision

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Video Denoising Quality Assessment for Different Noise Distributions

Video Denoising Quality Assessment for Different Noise Distr...

引用

brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Rafael Lucien Bahr Arias Alan Salvany Felinto Department of Computer Science Londrina State University (UEL) Londrina PR Brazil

Denoising algorithms often presume a single noise model, for instance, Gaussian noise, but it has been observed that during acquisition, image and video sequences can be corrupted by different types of noise, which follow a distinct probability distribution model depending on the application. This paper aims to compare the performance of several denoising algorithms, among them Non-Local Means and Block-Matching 3D, and other classical techniques such as median, Gaussian, bilateral and anisotropic diffusion, by simulating different noise distributions in videos and comparing the methods efficiency in multiple scenarios. Objective evaluation uses structural similarity (SSIM) and provides video specific assessment scores with NTIA Video Quality Metric (VQM). Results show considerable differences between intraframe and interframe filtering quality, while variations in filtering responses to each type of noise contribute to more appropriate selection of techniques to noise reduction and provide insight to noise difficulty levels.

关键词： Noise reduction Quality assessment Noise measurement standards Gaussian noise Anisotropic magnetoresistance

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Convolutional Neural Networks for static and Dynamic Breast Infrared Imaging Classification

Convolutional Neural Networks for Static and Dynamic Breast ...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Matheus de Freitas Oliveira Baffa Lucas Grassano Lattari Department of Computer Science Federal Institute of Southeast of Minas Gerais Rio Pomba MG Brazil

Breast cancer is the most frequent type of cancer among women. Since early diagnosis provides a better prognosis, different techniques have been developed by researchers all over the world. Several studies proved the efficiency of infrared image as a breast cancer screening technique. This paper proposes a methodology for analyzing infrared thermography of breast, considering distinct protocols, in order to classify patients images as healthy or non-healthy due to anomalies such as cancer. The major contribution of this work is to provide accurate classification using Convolutional Neural Networks, which were not exploited in previous works. Many methods relies on handcrafted features and traditional classificators, such as Support Vector Machines. We obtained competitive results compared to other works and we design an appropriate modelling which takes advantage of this type of deep learning architecture. Our proposal obtained 98% of accuracy for static protocol and 95% for dynamic protocol.

关键词： Protocols Breast cancer Feature extraction Infrared imaging Support vector machines

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Content-Oriented Surveillance System Based on ICN in Disaster Scenarios

Content-Oriented Surveillance System Based on ICN in Disaste...

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International symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Koki Okamoto Toru Mochida Daichi Nozaki Zheng Wen Xin Qi Takuro Sato Department of Communication and Computer Engineering School of Fundamental of Science Engineering Waseda University Tokyo Japan

ISBN: (纸本)9781538657584;9781538657577

This paper deals with an efficient image object detection method for use in a disaster prevention network that uses information-centric networking (ICN). In ICN for disaster prevention, a large number of surveillance cameras are arranged, and disaster image contents corresponding to the user's requests are distributed directly from the node attached to the surveillance camera. At this time, the name of the content requested by the user does not necessarily match the name of the image acquired by the surveillance camera. In this paper, the content requested by the user is processed and named using natural language processing (NLP). In addition, the image content from the surveillance camera is named using artificial intelligence technology. In this way, a method for improving the hit ratio between users and cameras was established. Furthermore, the volume of the interest packets decreases based on the information which area often occurs disaster. As a result, the network efficiency of ICN can be improved.

关键词： Cameras image recognition Artificial intelligence Surveillance Fires Tsunami Natural language processing

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