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12th International Conference on Virtual Campus, JICV 2022

作者： Suay, Adrian Perez Vaerenbergh, Steven Van Piles, Maria Laparra, Valero Pascual-Venteo, Ana B. Ruescas, Ana B. Fernandez-Moran, Roberto Martinez-Garcia, Marina Adsuara, Jose E. Amoros, Julia Munoz-Mari, Jordi Epifanio, Irene Fernandez-Torres, Miguel-Angel Universitat de València Dept. of Mathematics Teaching València Spain Universidad de Cantabria Dept. of Mathematics Statistics and Computer Science Santander Spain Image Processing Laboratory Universitat de València València Spain Universitat Jaume I Dept. of Mathematics Castelló Spain Universitat de València Dept. of Computer Science València Spain Universitat de València Dept. of Electronical Engineering Valencia Spain

ISBN: (数字)9781665451062

ISBN: (纸本)9781665451062

Learning Management Systems (LMS) are educational tools used to plan, implement and assess the learning-teaching procedure. LMS are continuously acquiring data from students in their learning progress when they interact with the platform. In this work, we use statistically-based approaches to learn about student performance solely using data from Moodle LMS, which is widely used along the educational institutions around the world. Using logged data from several subjects and degrees, our results indicate that the number of interactions with the LMS is a good indicator of student performance and can potentially be used as a measure to track students' learning progress. © 2022 IEEE.

关键词： Students

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RJB-Net: Residual Deep Learning with Joint Bilateral Denoising Network for Remote Sensing image Fusion

RJB-Net: Residual Deep Learning with Joint Bilateral Denoisi...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Krupa Patel Indranil Misra Narendra Patel S. Manthira Moorthi Debajyoti Dhar Dept. of Computer Science & Engineering Birla Vishvakarma Mahavidyalaya Engineering College Anand India Signal and Image Processing Group Space Application Centre(SAC) Indian Space Research Organisation (ISRO) Ahmedabad India

Remote sensing image fusion amalgamate information from panchromatic and multispectral remote sensing images to generate an optimum representative image. In this paper, we have proposed a novel method for image fusion known as Residual Deep Learning with Joint Bilateral Denoising Network (RJB-Net). The RJB-Net procedure is initiated with panchromatic image denoising using a joint bilateral filter to suppress noise and retain edge information. RJB-Net model is trained at image patches covering all major spectral classes. The denoised panchromatic image along with multispectral data is input to the residual deep learning network for inference and generates a fused image. The remote sensing datasets used for RJB-Net training and evaluation are Indian Cartosat-1 panchromatic and Resourcesat-2 multispectral imagery. The experiment section compares the RJB-Net denoising output with prominent spatial domain image filtering methods both qualitatively and quantitatively. It is found that RJB-Net fused images inject the spatial information from panchromatic images in a controlled manner while retaining the spectra characteristics of multispectral data. The merging performance of RJB-Net is compared with state-of-the-art remote sensing image fusion techniques that range from classical to recent deep learning model. The proposed method RJB-Net generates superior results among all fusion methods at different feature targets and compared the performance both visually as well as with image quality metrics.

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MB-CapsNet: An Efficient Fusion of MobileNetV3 and Matrix Capsule Networks

MB-CapsNet: An Efficient Fusion of MobileNetV3 and Matrix Ca...

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Chinese Automation Congress (CAC)

作者： Wenxing Han An-Min Zou Dept. of Electronic and Information Engineering Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Shantou University Shantou China

The capsule network constructs the relationship between the part and the whole in an image through feature coding, which shows excellent performance in the image classification. However, the original capsule network is weak in extracting useful feature information, because it tends to explain all the information in a graph and the advantages of the capsule network cannot be reflected in the face of complex background datasets. By using MobileNetV3 as the backbone to replace the simple primary convolutional network layer, an improved capsule network named MB-CapsNet is proposed in this paper. The advanced network reducing the interference of invalid features to focus on effective features. At the same time, a variety of parameter sets are employed and extensive experiments are proceeded on four kinds of datasets. After that, an optimal network configuration is obtained which is suitable for different datasets. The experimental results illustrate that the advanced MB-CapsNet is more effectively than the original capsule network in the feature extraction, showing its advantages and potential in complex datasets.

关键词： Sensitivity Convolution Neural networks Interference Routing Feature extraction Convolutional neural networks

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Fast Low-parameter Video Activity Localization in Collaborative Learning Environments

arXiv

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arXiv 2024年

作者： Jatla, Venkatesh Teeparthi, Sravani Egala, Ugesh Pattichis, Sylvia Celedón Pattichis, Marios S. Image and Video Processing and Communications Lab Dept. of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM87131 United States Department of Curriculum and Instruction The University of Texas at Austin AustinTX78712-1293 United States

Research on video activity detection has primarily focused on identifying well-defined human activities in short video segments. The majority of the research on video activity recognition is focused on the development of large parameter systems that require training on large video datasets. This paper develops a low-parameter, modular system with rapid inferencing capabilities that can be trained entirely on limited datasets without requiring transfer learning from large-parameter systems. The system can accurately detect and associate specific activities with the students who perform the activities in real-life classroom videos. Additionally, the paper develops an interactive web-based application to visualize human activity maps over long real-life classroom videos. Long-term video activity detection in real-life classroom videos present unique challenges, such as the need to detect multiple simultaneous activities, rapid transitions between activities, long-term occlusions, durations exceeding 15 minutes, and numerous individuals performing similar activities in the background. Moreover, subtle hand movements further complicate the need to differentiate between actual typing and writing activities as opposed to unrelated hand movements. The system processes the input videos using fast activity initializations and current methods for object detection to determine the location and the the person performing the activities. These regions are then processed through an optimal low-parameter dyadic 3D-CNN classifier to identify the activity. The proposed system processes 1 hour of video in 15 minutes for typing and 50 minutes for writing activities. The system uses several methods to optimize the inference pipeline. For each activity, the system determines an optimal low-parameter 3D CNN architecture selected from a family of low-parameter architectures. The input video is broken into smaller video regions that are transcoded at an optimized frame rate. For inference, a

关键词： Object detection

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Long-term Human Participation Assessment In Collaborative Learning Environments Using Dynamic Scene Analysis

arXiv

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arXiv 2024年

作者： Shi, Wenjing Tran, Phuong Celedón-Pattichis, Sylvia Pattichis, Marios S. Image and Video Processing and Communications Lab Dept. of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM87131 United States Department of Curriculum and Instruction The University of Texas at Austin AustinTX78712-1293 United States

The paper develops datasets and methods to assess student participation in real-life collaborative learning environments. In collaborative learning environments, students are organized into small groups where they are free to interact within their group. Thus, students can move around freely causing issues with strong pose variation, move out and re-enter the camera scene, or face away from the camera. We formulate the problem of assessing student participation into two subproblems: (i) student group detection against strong background interference from other groups, and (ii) dynamic participant tracking within the group. A massive independent testing dataset of 12,518,250 student label instances, of total duration of 21 hours and 22 minutes of real-life videos, is used for evaluating the performance of our proposed method for student group detection. The proposed method of using multiple image representations is shown to perform equally or better than YOLO on all video instances. Over the entire dataset, the proposed method achieved an F1 score of 0.85 compared to 0.80 for YOLO. Following student group detection, the paper presents the development of a dynamic participant tracking system for assessing student group participation through long video sessions. The proposed dynamic participant tracking system is shown to perform exceptionally well, missing a student in just one out of 35 testing videos. In comparison, a state-of-the-art method fails to track students in 14 out of the 35 testing videos. The proposed method achieves 82.3% accuracy on an independent set of long, real-life collaborative videos. © 2024, CC BY.

关键词： Students

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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Graph learning and augmentation based interpolation of signal strength for location-aware communications 28

Graph learning and augmentation based interpolation of signa...

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28th European Signal processing Conference, EUSIPCO 2020

作者： Chiu, Hong-Ming Fung, Carrson C. Ortega, Antonio Dept. of Electronics Engineering National Chiao Tung University Hsinchu Taiwan Institute of Electronics National Chiao Tung University Hsinchu Taiwan Signal and Image Processing Institute Department of Electrical and Computer Engineering University of Southern California Los Angeles United States

ISBN: (纸本)9789082797053

A graph learning and augmentation (GLA) technique is proposed herein to solve the received signal power interpolation problem, which is important for preemptive resource allocation in location-aware communications. A graph parameterization results in the proposed GLA interpolator having superior mean-squared error performance and lower computational complexity than the traditional Gaussian process method. Simulation results and analytical complexity analysis are used to prove the efficacy of the GLA interpolator. © 2021 European Signal processing Conference, EUSIPCO. All rights reserved.

关键词： Interpolation

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Intelligent Reflecting Surface OFDM Communication with Deep Neural Prior

Intelligent Reflecting Surface OFDM Communication with Deep ...

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IEEE International Conference on Communications (ICC)

作者： Tomer Fireaizen Gal Metzer Dan Ben-David Yair Moshe Israel Cohen Emil Bjö rnson Signal and Image Processing Laboratory (SIPL) Faculty of Electrical &amp Computer Engineering Technion &#x2013 Israel Institute of Technology Haifa Israel Faculty of Engineering Tel Aviv University Tel Aviv Israel Dept. of Electrical Engineering (ISY) Link&#x00F6 ping University and KTH Royal Insitute of Technology Sweden

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683484

An Intelligent Reflecting Surface (IRS) is an emerging technology for improving the data rate over wireless channels by controlling the underlying channel. In this paper, we describe a novel solution for IRS configuration to maximize the data rate over wideband channels. The optimization is obtained by online training of a deep generative neural network. Inspired by related works in image processing, this network is randomly initialized and acts as a regularization term for the optimization process since the structure of the generator is sufficient to capture a great deal of IRS statistics prior to any learning. In contrast to recent deep learning techniques for IRS configuration, the proposed technique does not require an offline training stage and can adapt quickly to any environment. Compared to the previous state-of-the-art algorithm, the proposed method is significantly faster and obtains IRS configurations that achieve higher data transmission rates.

关键词： Wireless communication Training Deep learning OFDM image processing Neural networks Generators

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Gender Detection using Facial Features with Support Vector Machine

Gender Detection using Facial Features with Support Vector M...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Jayaprada S. Hiremath Shantala S. Hiremath Sujith Kumar Manjunath S. Chincholi Shantakumar B. Patil Mrutyunjaya S. Hiremath Computer Science and Engineering Research Scholar at VTU Belgaum Karnataka India Specialist in Image Processing Tata Elxsi Limited Bangalore Karnataka India Principal Engineer (Artificial Intelligence / Machine learning) VVDN Technologies Pvt. Ltd. Bangalore Karnataka India Dept. of Image Processing eMath Technology Pvt. Ltd. Bangalore Karnataka India Computer Science and Engineering Sai Vidya Institute of Technology Bengaluru Karnataka India

ISBN: (纸本)9781665491129

Innovative security technologies have increased the need for accurate identification. Gender identification has been widespread use of image analysis in recent decades. The face is one of the most popular biometric features in image processing. In image processing and video surveillance, systems that automatically discern gender from facial photos are gaining prominence. This study discusses face traits and gender categorization. In this study, we created a technique with good runtime and efficiency to determine human gender using face photos. It uses characteristics taken from pre-processed face photographs of various ages. Support Vector Machine (SVM) Classification was used to determine class thresholds. Our method classifies UTK-FACE gender with 91.63% accuracy.

关键词： Support vector machines Wireless communication Training Machine learning algorithms Face recognition Support vector machine classification Feature extraction

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Talking detection in collaborative learning environments

arXiv

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arXiv 2021年

作者： Shi, Wenjing Pattichis, Marios S. Celedón-Pattichis, Sylvia LópezLeiva, Carlos Image and Video Processing and Communications Lab Dept. of Electrical and Computer Engineering University of New Mexico United States Dept. of Language Literacy and Sociocultural Studies University of New Mexico United States

We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems. The small projection images are then easily classified using a simple majority vote of standard classifiers. For talking detection, our proposed approach is shown to significantly outperform single activity systems. We have an overall accuracy of 59% compared to 42% for Temporal Segment Network (TSN) and 45% for Convolutional 3D (C3D). In addition, our method is able to detect multiple talking instances from multiple speakers, while also detecting the speakers themselves. © 2021, CC BY.

关键词： Speech recognition

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