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Evaluate student achievement by classifying brain structure and its functionality with novel hybrid method

NEURAL COMPUTING & applications 2024年第7期36卷 3357-3368页

作者： Atas, Pinar Karadayi Istanbul Arel Univ Dept Comp Engn TR-34537 Istanbul Turkiye

In a labor market that demands a workforce well-trained in science, technology, engineering, and mathematics (STEM) subjects, it is required of children to successfully develop their mathematical skills in order to become highly productive adults. Recent developments in computer vision, artificial intelligence, machine learning, and medical imaging techniques give us new opportunities for building intelligent support tools to help us learn more about the neural underpinnings of how children learn math and how that knowledge relates to individual differences in skill. This study examines the brain activities of students during problem-solving by checking brain structure and its functionality. By using powerful techniques in the light of machine learning and image processing, the relationship between success and the background of a child was researched. The aim is to make a solid prediction of the possible future success of the children by observing their brain activities. The children we investigated were asked different questions to get information about their intelligence. In our study, we have tried to find how those questions and answers may affect the future success of a child. For this purpose, a novel hybrid classification model that utilizes cluster analysis, Random Forest, Logistic Regression, and ensemble learning is intended for classification tasks. Our study includes two main stages. Firstly, the image processing techniques were applied to create unique features of brain images. Then, machine learning tecnniques were used to select a set of features, and for getting prediction results our hybrid classification model was applied. In the end, we obtained useful results indicating that there is a complicated connection between the success rate and the history of a child. This novel approach to classification, which combines multiple methods by using a hybrid model, has the potential to be implemented in computational tools for strategic decision support sys

关键词： MRI brain imaging machine learning image processing Brain structure and functionality Hybrid classification

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The JPEG AI Standard: Providing Efficient Human and machine Visual Data Consumption

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IEEE MULTIMEDIA 2023年第1期30卷 100-111页

作者： Ascenso, Joao Alshina, Elena Ebrahimi, Touradj Inst Super Tecn Inst Telecomunicacoes P-1049001 Lisbon Portugal Huawei Technol Duesseldorf GmbH D-80992 Munich Germany Ecole Polytech Fed Lausanne Multimedia Signal Proc Grp CH-1015 Lausanne Switzerland

The Joint Photographic Experts Group (JPEG) AI learning-based image coding system is an ongoing joint standardization effort between International Organization for Standardization (ISO), International Electrotechnical Commission (IEC), and International Telecommunication Union - Telecommunication Sector (ITU-T) for the development of the first image coding standard based on machine learning (a subset of artificial intelligence), offering a single stream, compact compressed domain representation, targeting both human visualization and machine consumption. The main motivation for this upcoming standard is the excellent performance of tools based on deep neural networks, in image coding, computer vision, and image processing tasks. The JPEG AI aims to develop an image coding standard addressing the needs of a wide range of applications such as cloud storage, visual surveillance, autonomous vehicles and devices, image collection storage and management, live monitoring of visual data, and media distribution. This article presents and discusses the rationale behind the JPEG AI vision, notably how this new standardization initiative aims to shape the future of image coding, through relevant application-driven use cases. The JPEG AI requirements, the JPEG AI history, and current status are also presented, offering a glimpse of the development of the first learning-based image coding standard.

关键词： Performance evaluation Visualization image coding Artificial intelligence Surveillance Transform coding Streaming media

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Recent developments in computer vision and artificial intelligence aided intelligent robotic welding applications

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第11-12期126卷 4763-4809页

作者： Eren, Berkay Demir, Mehmet Hakan Mistikoglu, Selcuk Iskenderun Tech Univ Dept Mechatron Engn TR-31200 Hatay Turkiye Iskenderun Tech Univ Dept Mech Engn TR-31200 Hatay Turkiye Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA

The welding process, which is an indispensable part of the manufacturing industry, has been in demand for years and continues to attract the attention of researchers. With the transition to Industry 4.0, the welding process got out of the control of the operators and became automated with sensors and artificial intelligence methods, and as a result, it became inevitable for industrial manipulators or robots to enter the production sector. One of the most important details in making the welding process autonomous in manufacturing is the sensors, and among the sensors are the vision sensors. In recent years, it is seen that robotic welding applications are applied very sensitively and successfully when visual sense and artificial intelligence are used together. This study comprehensively reviewed research and development for cutting-edge applications using visual sensors and artificial intelligence for robotic welding applications. The processes that are the subject of intelligent robotic welding applications such as calibration, determination of welding starting point, seam tracking, and welding quality are determined and discussed based on current studies and critical analyzes. The detection, tracking, diagnosis, classification, and prediction performances of various methods of machine learning (ML), which is one of the most used areas in artificial intelligence-based applications, in welding applications are examined comparatively. This review article will help researchers about what should be considered in vision sensor aided robotic welding applications and how to contribute more to studies with artificial intelligence support.

关键词： Robotic welding vision sensing image processing Seam tracking machine learning Deep learning

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Stereo vision Meta-Lens-Assisted Driving vision

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ACS PHOTONICS 2024年第7期11卷 2546-2555页

作者： Liu, Xiaoyuan Li, Wuyang Yamaguchi, Takeshi Geng, Zihan Tanaka, Takuo Tsai, Din Ping Chen, Mu Ku City Univ Hong Kong State Key Lab Terahertz & Millimeter Waves Kowloon Hong Kong 999077 Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong 999077 Peoples R China RIKEN Ctr Adv Photon Innovat Photon Manipulat Res Team Saitama 3510198 Japan RIKEN Cluster Pioneering Res Metamat Lab Saitama 3510198 Japan Tokushima Univ Inst PostLED Photon Tokushima 7708506 Japan City Univ Hong Kong Ctr Biosyst Neurosci & Nanotechnol Hong Kong 999077 Peoples R China Tsinghua Univ Inst Data & Informat Tsinghua Shenzhen Int Grad Sch Shenzhen 518071 Guangdong Peoples R China

Object detection and depth perception are key foundations of object tracking and machine navigation, facilitating a thorough perception and understanding of the surrounding environment. Currently, autonomous vehicles employ complex and bulky systems with high cost and energy consumption to achieve demanding multimodal vision. An imperative exists for the development of compact and reliable technology to enhance the cost-effectiveness and efficiency of autonomous driving systems. Meta-lens, a novel flat optical device, has an artificial nanoantenna array to manipulate the light properties. It is lightweight, ultrathin, and easy to integrate, making it suitable for various applications. We developed a stereo vision meta-lens imaging system for assisted driving vision, a comprehensive perception including imaging, object detection, instance segmentation, and depth information. The compact system comprises a band-pass filter, a stereo vision meta-lens, and a complementary metal oxide semiconductor (CMOS) sensor. In comparison to traditional two-camera-based stereo vision systems, the meta-lens stereo vision imaging system eliminates the need for distortion correction or camera calibration. A tailored data processing pipeline is proposed with an intensity and depth gradient cross-validation optimization mechanism and three deep learning modules for object detection, instance segmentation, and stereo matching foundations. Final assisted driving vision provides multimodal perception by integrating the raw image, instance labels, bounding boxes, segmentation masks in depth pseudo color, and depth information for each detected object. Our assisted driving vision based on a stereo meta-lens system offers a comprehensive perception for scene understanding of machines, benefiting the applications of human-computer interaction, machine navigation, autonomous driving, and augmented reality.

关键词： meta-lens stereo vision depth sensing image segmentation recognition

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Learning Location From Shared Elevation Profiles in Fitness Apps: A Privacy Perspective

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第1期23卷 581-596页

作者： Meteriz-Yildiran, Ulku Yildiran, Necip Fazil Kim, Joongheon Mohaisen, David Meta Menlo Pk CA 94025 USA Google Mountain View CA 94043 USA Korea Univ Dept Elect Engn Seoul 02841 South Korea Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

The extensive use of smartphones and wearable devices has facilitated many useful applications. For example, with Global Positioning System (GPS)-equipped smart and wearable devices, many applications can gather, process, and share rich metadata, such as geolocation, trajectories, elevation, and time. For example, fitness applications, such as Runkeeper and Strava, utilize the information for activity tracking and have recently witnessed a boom in popularity. Those fitness tracker applications have their own web platforms and allow users to share activities on such platforms or even with other social network platforms. To preserve the privacy of users while allowing sharing, several of those platforms may allow users to disclose partial information, such as the elevation profile for an activity, which supposedly would not leak the location of the users. In this work, and as a cautionary tale, we create a proof of concept where we examine the extent to which elevation profiles can be used to predict the location of users. To tackle this problem, we devise three plausible threat settings under which the city or borough of the targets can be predicted. Those threat settings define the amount of information available to the adversary to launch the prediction attacks. Establishing that simple features of elevation profiles, e.g., spectral features, are insufficient, we devise both natural language processing (NLP)-inspired text-like representation and computer vision-inspired image-like representation of elevation profiles, and we convert the problem at hand into text and image classification problem. We use both traditional machine learning- and deep learning-based techniques and achieve a prediction success rate ranging from 59.59% to 99.80%. The findings are alarming, highlighting that sharing elevation information may have significant location privacy risks.

关键词： Location privacy privacy breach privacy in social media fitness applications natural language processing applied machine learning

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A review of recent advances in data-driven computer vision methods for structural damage evaluation: algorithms, applications, challenges, and future opportunities

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2025年 1-33页

作者： Pan, Xiao Yang, Tony T. Y. Li, Jun Ventura, Carlos Malaga-Chuquitaype, Christian Li, Chaobin Su, Ray Kai Leung Brzev, Svetlana Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Clear Water Bay Hong Kong Peoples R China Univ British Columbia Dept Civil Engn Vancouver BC Canada Curtin Univ Ctr Infrastruct Monitoring & Protect Perth WA Australia Imperial Coll London Dept Civil & Environm Engn London England Univ Hong Kong Dept Civil Engn Hong Kong Peoples R China

Computer vision techniques have gained great traction in civil infrastructure inspection and monitoring. This paper conducted a systematic review of recent data-driven computer vision algorithms in structural damage detection published during the past 5 years. The theories of prevalent computer vision models are first reviewed with an emphasis on the progressive innovation in algorithms' architecture. Then, recent applications of computer vision models for structural damage evaluation are discussed, which are classified into different structural categories by their material types (i.e., concrete, steel, masonry, timber) at three hierarchical levels including damage recognition, localization, and quantification. In particular, the paper also highlights the current state of using computer vision for damage assessment of timber structures, which remains under-explored compared to concrete and steel structures. Next, the paper scrutinizes existing structural damage inspection guidelines to identify key technological gaps between the capability of existing computer vision methods and manual inspection practices in the field. Finally, the paper summarizes existing challenges and recommends future research opportunities including the integration of computer vision methods with multimodal large language models, sensor-fusion, and mobile inspection approaches.

关键词： Structural damage detection Structural health monitoring Computer vision image processing machine learning Deep learning Sensor fusion Unmanned aerial and ground vehicles

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Compute-First Optical Detection for Noise-Resilient Visual Perception

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ACS PHOTONICS 2025年第2期12卷 1137-1145页

作者： Kim, Jungmin Yu, Nanfang Yu, Zongfu Univ Wisconsin Dept Elect & Comp Engn Madison WI 53706 USA Columbia Univ Dept Appl Phys & Appl Math New York NY 10027 USA

During machine visual perception, the optical signal from a scene is transferred into the electronic domain by detectors in the form of image data, which are then processed for the extraction of visual information. In noisy environments, such as a thermal imaging system, however, the neural performance faces a significant bottleneck due to the inherent degradation of data quality upon noisy detection. Here, we propose a concept of optical signal processing before detection to address this issue. We demonstrate that spatially redistributing optical signals through a properly designed linear transformer can enhance the detection noise resilience of visual perception, as benchmarked with MNIST classification. A quantitative analysis of the relationship between signal concentration and noise robustness supports our idea with its practical implementation in an incoherent imaging system. This compute-first detection scheme can advance infrared machine vision technologies for industrial and defense applications.

关键词： Optical processing linear transformation signal-to-noise ratio metasurface optical neuralnetwork visual perception

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Hand Gesture Recognition on Edge Devices: Sensor Technologies, Algorithms, and processing Hardware

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SENSORS 2025年第6期25卷 1687-1687页

作者： Fertl, Elfi Castillo, Encarnacion Stettinger, Georg Cuellar, Manuel P. Morales, Diego P. Infineon Technol AG D-85579 Neubiberg Germany Univ Granada Dept Elect & Comp Technol E-18071 Granada Spain Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

Hand gesture recognition (HGR) is a convenient and natural form of human-computer interaction. It is suitable for various applications. Much research has already focused on wearable device-based HGR. By contrast, this paper gives an overview focused on device-free HGR. That means we evaluate HGR systems that do not require the user to wear something like a data glove or hold a device. HGR systems are explored regarding technology, hardware, and algorithms. The interconnectedness of timing and power requirements with hardware, pre-processing algorithm, classification, and technology and how they permit more or less granularity, accuracy, and number of gestures is clearly demonstrated. Sensor modalities evaluated are WIFI, vision, radar, mobile networks, and ultrasound. The pre-processing technologies stereo vision, multiple-input multiple-output (MIMO), spectrogram, phased array, range-doppler-map, range-angle-map, doppler-angle-map, and multilateration are explored. Classification approaches with and without ML are studied. Among those with ML, assessed algorithms range from simple tree structures to transformers. All applications are evaluated taking into account their level of integration. This encompasses determining whether the application presented is suitable for edge integration, their real-time capability, whether continuous learning is implemented, which robustness was achieved, whether ML is applied, and the accuracy level. Our survey aims to provide a thorough understanding of the current state of the art in device-free HGR on edge devices and in general. Finally, on the basis of present-day challenges and opportunities in this field, we outline which further research we suggest for HGR improvement. Our goal is to promote the development of efficient and accurate gesture recognition systems.

关键词： hand gesture recognition edge machine learning artificial intelligence algorithms signal processing radar WiFi 4G 5G LTE ultrasound lidar vision image processing AI accelerators

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Adaptive adam-based optimizers using second-order weight decoupling and gradient-aware weight decay for vision transformer

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machine vision AND applications 2025年第3期36卷 1-14页

作者： Sai, Boyapati Hemanth Mukherjee, Snehasis Dubey, Shiv Ram Shiv Nadar Inst Eminence Greater Noida UP India Indian Inst Informat Technol Allahabad Prayagraj UP India

Optimizers play important roles in enhancing the performance of a deep network. A study on different optimizers is necessary to understand the effect of optimizers on the performance of the deep network for a given target task, such as image classification. Several attempts were made to investigate the effect of optimizers on the performance of CNNs. However, such experiments have not been carried out on vision transformers (ViT), despite the recent success of ViT in various image processing tasks. In this paper, we conduct exhaustive experiments with ViT using different optimizers. In our experiments, we found that weight decoupling and weight decay in optimizers play important roles in training ViT. We focused on the concept of weight decoupling and tried different variations of it to investigate to what extent weight decoupling is beneficial for a ViT. We propose two techniques that provide better results than weight-decoupled optimizers: (i) The weight decoupling step in optimizers involves a linear update of the parameter with weight decay as the scaling factor. We propose a quadratic update of the parameter which involves using a linear as well as squared parameter update using the weight decay as the scaling factor. (ii) We propose using different weight decay values for different parameters depending on the gradient value of the loss function with respect to that parameter. A smaller weight decay is used for parameters with a higher gradient value and vice versa. image classification experiments are conducted over CIFAR-100 and TinyimageNet datasets to observe the performance of these proposed methods with respect to state-of-the-art optimizers such as Adam, RAdam, and AdaBelief. The code is available at https://***/Hemanth-Boyapati/Adaptive-weight-decay-optimizers.

关键词： Adam-based optimizer Weight decoupling Transformers Weight decay Adaptive optimizers

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Transformer with multi-level grid features and depth pooling for image captioning

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machine vision AND applications 2024年第5期35卷 1-17页

作者： Bui, Doanh C. Nguyen, Tam V. Nguyen, Khang Vietnam Natl Univ Univ Informat Technol Ho Chi Minh City Vietnam Univ Dayton Dept Comp Sci Dayton OH USA

image captioning is an exciting yet challenging problem in both computer vision and natural language processing research. In recent years, this problem has been addressed by Transformer-based models optimized with Cross-Entropy loss and boosted performance via Self-Critical Sequence Training. Two types of representations are embedded into captioning models: grid features and region features, and there have been attempts to include 2D geometry information in the self-attention computation. However, the 3D order of object appearances is not considered, leading to confusion for the model in cases of complex scenes with overlapped objects. In addition, recent studies using only feature maps from the last layer or block of a pretrained CNN-based model may lack spatial information. In this paper, we present the Transformer-based captioning model dubbed TMDNet. Our model includes one module to aggregate multi-level grid features (MGFA) to enrich the representation ability using prior knowledge, and another module to effectively embed the image's depth-grid aggregation (DGA) into the model space for better performance. The proposed model demonstrates its effectiveness via evaluation on the MS-COCO "Karpathy" test split across five standard metrics.

关键词： image captioning Transformer Depth estimation Multi-level feature maps Grid features

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