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15th Annual ACM Symposium on Eye Tracking Research and applications (ETRA)

作者： Wang, Felix Sihan Lohmeyer, Quentin Duchowski, Andrew Meboldt, Mirko Swiss Fed Inst Technol Inst Design Mat & Fabricat Zurich Switzerland Clemson Univ Sch Comp Clemson SC USA

ISBN: (纸本)9798400701504

In mobile eye-tracking, visual attention is commonly evaluated using fixation-based measures, which can be mapped to predefined objects of interest for task-specific attention analysis. Even though attention can be directed independently from the fovea, little research can be found on the quantification of peripheral vision for attention analysis. In this work, we discuss the benefits of enhancing traditional mapping methods with near-peripheral information and expand previous research by presenting a novel machine learning-based gaze measure, the visual attention index (VAI), for the analysis of visual attention using dynamic stimuli. Results are discussed using the data of two multi-object mobile eye tracking use cases and visualized using radar graphs. We show that by combining foveal and peripheral vision the VAI is effective for the comparison of visual attention over multiple tasks, trials and subjects, which offers new possibilities for a more realistic and detailed depiction of visual attention in multi-object tasks.

关键词： Attention visualization peripheral vision Computer vision image processing Eye movements and cognition AOI-based methods Visual attention eye movement measures

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Using Monocular Depth Estimation for Distance Estimation in a Moving Vehicle 1

Using Monocular Depth Estimation for Distance Estimation in ...

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1st International Conference on Computer Graphics and image processing, CGIP 2023

作者： De Guzman, Lanz Benedict N. Raymond See, Aaron De la Salle University Manufacturing Engineering and Management Department Manila City Philippines Southern Taiwan University of Science and Technology Department of Electrical Engineering Tainan City Taiwan

ISBN: (纸本)9798350333183

Accompanying the increase in demand for autonomous systems and robotic solutions is the increase in the relevance of various depth estimation technologies. Monocular Depth Estimation (MDE) is used to predict distances by generating depth maps using only a single RGB camera. However, without out-of-the-box calibration or ground truth reference for generated depth values from MDE models its use case in practical applications is limited. This research introduces a method of actualizing generated depth map values for different applications. The proposed system involves the utilization of machine vision using YOLO for object detection, followed by the computation of the lens optic algorithms to calculate the distance. Results demonstrated a real-time environment detection and depth estimation solution with more than 90% accuracy for measuring object depth in static environments. Furthermore, the system was also successfully tested in a moving vehicle to provide an estimated distance of surrounding vehicles. In the future, further tests will be done to improve the accuracy and calculation speed for use in car safety. © 2023 IEEE.

关键词： Computer vision

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Computing Resistance-Style image Sensors for Artificial Neural Networks

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IEEE INTERNET OF THINGS JOURNAL 2023年第6期10卷 4985-4997页

作者： Zhao, Guihua Peng, Yating Wang, Yizhan Wan, Caihua Liu, Xianping Zhang, Yu Han, Xiufeng Chen, Weichong Yu, Zhiyi Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Microelect Sci & Technol Zhuhai 519082 Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Inst Phys Beijing Natl Lab Condensed Matter Phys Beijing 100190 Peoples R China Songshan Lake Mat Lab Magnet Lab Dongguan 523808 Guangdong Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Optoelect Informat Proc Chi Guangzhou 510275 Peoples R China

Today, machine vision experiences large latency due to big data processing, which is a barrier to time-critical applications. To address this issue, in-sensor computing was presented in the past. Here, we present a scheme of computing in a magnetic tunneling junction (MTJ) sensor array for proof-of-principle. Using the MTJ sensor array, the functions of artificial neural network (ANN) classifiers and autoencoders were verified. The time for correct classification of one picture was less than 9 mu s . The power consumed in the sensor array can be decreased according to the square law without affecting the results. Our work shows universal circuits and algorithms to compute in resistance-style ANN image sensors with promising energy efficiency.

关键词： Manganese Magnetic tunneling Magnetic resonance imaging Sensor arrays Saturation magnetization Magnetic circuits Magnetic domains Artificial neural network (ANN) image sensor in-sensor computing magnetic tunneling junction (MTJ) sensor design sensor system integration

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Visual Feedback System Supporting Robotic Manipulation of Hemp Plants

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JOURNAL OF NATURAL FIBERS 2025年第1期22卷

作者： Kraft, Marek Ptak, Bartosz Piechocki, Mateusz Pieczynski, Dominik Mlodzikowski, Kamil Kulecki, Bartlomiej Belter, Dominik Poznan Univ Tech Inst Robot & Machine Intelligence Piotrowo 3A PL-60965 Poznan Poland

This paper presents an agricultural robotics system designed to automate the detection and manipulation of male hemp plants, addressing the challenge of manually removing these to enhance crop quality. The solution uses an industry-standard, inexpensive RGB-D camera as the input data source to derive control signals controlling the robotic arm and end effector. Input image data processing is performed by a dedicated neural network model trained using a dataset created specifically for the described task to achieve detection by stalk segmentation and postprocessing. The research involved assessing various neural network models, including UNet, DeepLabV3+, and YOLOv8 in various variants, for their capability to detect stalks accurately and swiftly. Fast operation is necessary for effective real-time feedback in robotic grasping tasks. Among tested architectures, the integration of UNet with ResNet50 was found to provide a good trade-off between detection precision and operational speed on edge AI devices. The resulting solution offers good accuracy and significantly outperforms existing methods in terms of processing speed, promising substantial improvements in agricultural robotics by enabling on-line adaptive grasping using low-cost components. The applications can be extended beyond hemp tending to include various other crops, eliminating tedious manual labor. (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)RGB-D(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(si

关键词： Hemp computer vision deep learning robotics manipulation (sic)(sic) (sic)(sic)(sic)(sic)(sic) (sic)(sic)(sic)(sic) (sic)(sic)(sic) (sic)(sic)

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ChangeSAM: Adapting the Segment Anything Model to Street Scene image Change Detection 27

ChangeSAM: Adapting the Segment Anything Model to Street Sce...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Bauer, Adrian Krabbe, Jan-Christoph Kummert, Anton University of Wuppertal Germany

ISBN: (纸本)9798331505929

In the dynamic field of machine learning, foundation models have recently gained prominence, particularly for their application in natural language processing and computer vision. The foundational Segment Anything Model (SAM), known for its interactive image segmentation via prompts, serves as the basis for this study. We introduce ChangeSAM, a tailored adaptation of SAM for street scene image change detection (CD). ChangeSAM utilizes the versatility and vast knowledge of SAM, adapting it to effectively identify semantic changes in image pairs. Two architectural adaptations are introduced - Pre Decoder Fusion (PreDF) and Post Decoder Fusion (PostDF) - enabling ChangeSAM to process dual images. Enhancements through Prompt Tuning and Low-Rank Adaptation (LoRA) are integrated, achieving a balance between reusability, computational efficiency, and accuracy. Our evaluation on the VL-CMU-CD dataset shows that with minimal parameter adjustments, ChangeSAM achieves accuracy on par with fully fine-tuned models. This work contributes to the ongoing development of foundation models in practical applications, illustrating the viability and potential of adaptable, efficient models in scenarios with limited computational resources, such as intelligent vehicles. © 2024 IEEE.

关键词： Semantics

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Spatiotemporal Alignment of Event Stream With images and Active Illumination

Spatiotemporal Alignment of Event Stream With Images and Act...

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作者： Wasti, Abhijan Rochester Institute of Technology

学位级别：M.S., Master of Science/Master of Surgery

Unlike conventional frame-based cameras that form images by sampling all pixels within the duration of the global/rolling shutter, a pixel in an event camera can be triggered independently when the log intensity change in scene luminance at the pixel exceeds a threshold. This unique feature provides several advantages over conventional sensors, including high dynamic range (HDR) (≈120dB), high temporal rate (≈10,000Hz), low latency (< 1ms), and low power requirements (≈10mW). These properties make them excellent candidates for applications such as high-speed photography, HDR image reconstruction, object tracking, depth estimation, simultaneous localization and mapping, and surveillance and monitoring. Despite their potential, the asynchronous and spatially sparse nature of events poses challenges to event processing and interpretation. This is because most advanced image processing and computer vision algorithms are designed to work with conventional image formats, and not with temporally dense streams of asynchronous pixel events (i.e., the event stream). Although emerging techniques in supervised machine learning demonstrate promise, continued and rapid progress relies on the availability of labeled event datasets, which are scarce, and difficult to produce. Moreover, generating reliable events for training models is challenging due to the scene-dependent nature of event generation, which is further complicated by varying illumination and relative motion. In this thesis, we attempt to address these limitations with a novel imaging paradigm involving the capture of frames from a conventional frame-based camera that has been spatially aligned and temporally synchronized with an event sensor. Our active illumination source allows us to generate events more consistently even under challenging illumination and motion in the scene. We demonstrate the feasibility of such a setup for a mobile eye-tracking system and acquire subpixel and microsecond accurate spatiotemporal

关键词： Computer vision Imaging science High dynamic range Conventional images Eye-tracking

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Deep Learning based image classification for Automated Face Spoofing Detection using machine Learning: Convolutional Neural Network 2

Deep Learning based Image classification for Automated Face ...

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2nd IEEE World Conference on Communication and Computing (WCONF)

作者： Kumar, Biresh Manisha, Kumari Sinha, Anurag Kumar, Abhishek Kumar, Jeevan Amity Univ Amity Inst Informat Technol Ranchi Bihar India IGNOU Sch Comp & Informat Sci New Delhi India Jharkhand Univ Technol Comp Sci & Engn Ranchi Bihar India RVSCET RVS Coll Engn & Technol Jamshedpur Bihar India

ISBN: (纸本)9798350395334;9798350395327

Facial recognition technology has gained widespread use in various applications, raising concerns about the weakness of frameworks to confront mocking assaults. This study presents an implementation of face spoofing detection using machine learning techniques. The exploration utilizes a far-reaching system that envelops data combination, preprocessing, incorporate extraction, and model readiness. A diverse dataset comprising genuine and spoofed facial images, representing various spoofing techniques, is utilized. Feature extraction leverages Convolutional Brain Organizations (CNNs) to catch discriminative facial elements. The selected machine learning model is trained and fine-tuned, with a focus on achieving robustness against evolving spoofing methods. The evaluation of the implemented system involves rigorous testing on a separate dataset, utilizing estimations like precision, exactness, survey, and F1-score. The study investigates post-processing techniques and considerations for real-time deployment, ensuring practical applicability is done by the method convolutional neural network (CNN). Cross-approval is performed to evaluate the model's speculation capacities, and the deployment phase explores integration into real-world scenarios. Ethical considerations, user feedback, and compliance with data privacy regulations are integral components of the study.

关键词： Face Spoofing image Classifying Face Recognition Computer vision Deep Learning

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45th Annual Conference of the South-African-Institute-of-Computer-Scientists-and-Information-Technologists (SAICSIT) on Human- machine-Digital-Convergence

作者： Mertens, P. J. Ngxande, Mkhuseli Stellenbosch Univ Stellenbosch South Africa

ISBN: (纸本)9783031648809;9783031648816

Face recognition plays a crucial role in various applications, ranging from security to personal convenience. Recent advancements have emphasized the importance of recognizing individuals based on age-related facial features within this domain. This paper presents a comprehensive evaluation of two deep learning architectures for age-based face recognition: Siamese Convolutional Networks (SCNs) and vision Transformers (ViTs). Convolutional Neural Networks (CNNs), which are critical in modern face recognition, serve as the backbone for Siamese Convolutional Networks (SCNs). SCNs are specifically designed to detect similarities between input pairs by emphasising local features crucial for age-related distinctions. In contrast, ViTs, initially developed for natural language processing, have demonstrated promising performance in image recognition, showcasing their aptitude for capturing global image context. This work investigates the performance of these distinct architectures in discerning age-related variations within facial data features. Performance comparisons were conducted on three established SCN models and two ViT architectures. The results revealed that the optimal SCNs primarily focused on the mouth, nose, and eye regions, indicating their reliance on local features for age estimation. Interestingly, the ViT models achieved superior performance despite lacking explicit feature localization. This suggests that a holistic understanding of the facial context may be more effective than focusing solely on isolated features for age-based recognition.

关键词： Age-Based Face Recognition Siamese Networks vision Transformers

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Multi-class object detection system using hybrid convolutional neural network architecture

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MULTIMEDIA TOOLS AND applications 2022年第22期81卷 31727-31751页

作者： Borade, Jay Laxman Lakshmi, Muddana A. GITAM Deemed Univ Hyderabad India GITAM Deemed Univ CSE Dept Hyderabad India

Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image has attracted great attention because it can effectively classify and detect the image. A multi-class object detection system from a video or image is quite challenging because of the errors obtained by the location classification process. Our proposed system generalized a hybrid convolutional neural network (H-CNN) model is used to realize the user object from an image. The proposed work integrates pre-processing, object localization, feature extraction and classification. First, the input image is pre-processed with Gaussian filtering to remove noise and improve the image quality. After completing the pre-processing procedure, it is subjected to object localization. Here the object in the image is localized using Grid Guided Localization (GGL). In the feature extraction phase, the model would be pre-trained with AlexNet. Here the AlexNet are generalized as fully connected (FC) layers. Finally, the Softmax layer in the AlexNet architecture is replaced by SVR (Support Vector Regression), which acts as a classifier for identifying the object class. The classification loss is minimized using the Improved Grey Wolf (IGW) optimization algorithm. Thus, the H-CNN model can quickly classify and label the objects from images. It also offers improved classification performance in managing effective training time. The proposed work will be implemented in PYTHON. Therefore, the model would be built using various datasets such as MIT-67, PASCAL VOC2010, MS (Microsoft)-COCO, and MSRC to effectively train and classify the object. The proposed H-CNN achieved improved results with MIT-67 (96.02%), PASCAL VOC2010 (95.04%), MSRC (97.37%), and MS COCO (94.53%). The results obtained by H-CNN proved that the excluded result of Mean Average Precision (mAP), Precision, Accuracy, Recall values and F1-Score achieved better results than with re

关键词： image processing Object localization Deep learning Object recognition machine learning

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Rosette Plant Centre Detection and Tracking using YOLO: An Efficient Deep Learning Approach 3

Rosette Plant Centre Detection and Tracking using YOLO: An E...

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3rd International Conference on Computing and machine Intelligence (ICMI)

作者： Akagic, Amila Saric, Rijad Buza, Emir Kecman, Stefani Lewsey, Mathew G. Custovic, Edhem Whelan, James Univ Sarajevo UNSA Fac Elect Engn Sarajevo 71000 Bosnia & Herceg La Trobe Inst Sustainable Agr & Food LISAF Dept Anim Plant & Soil Sci Melbourne Vic 3086 Australia La Trobe Univ Australian Res Council Res Hub Med Agr Melbourne Vic 3086 Australia Sci Instruments Australia SIA 2 Res Ave Melbourne Vic 3086 Australia Zhejiang Univ Coll Life Sci State Key Lab Plant Environm Resilience Hangzhou 310058 Peoples R China Zhejiang Univ Prov Int Sci & Technol Cooperat Base Engn Biol Haining 314400 Peoples R China

ISBN: (纸本)9798350372977;9798350372984

The precise detection of plant centres is important for growth monitoring, enabling the continuous tracking of plant development to discern the influence of diverse factors. It holds significance for automated systems like robotic harvesting, facilitating machines in locating and engaging with plants. In this paper, we explore the YOLOv4 (You Only Look Once) real-time neural network detector for plant centre detection. Our dataset, comprising over 12,000 images from 151 Arabidopsis thaliana accessions, is used to fine-tune the model. Evaluation of the dataset reveals the model's proficiency in centre detection across various accessions, boasting an mAP of 99.79% at a 50% IoU threshold. The model demonstrates real-time processing capabilities, achieving a frame rate of approximately 50 FPS. This outcome underscores its rapid and efficient analysis of video or image data, showcasing practical utility in time-sensitive applications.

关键词： Plant Phenotyping Arabidopsis thaliana Computer vision image processing Deep Learning Neural Networks

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