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5th International Conference on machine vision and applications (ICMVA)

作者： Mucha, Wiktor Kampel, Martin TU Wien Comp Vis Lab Vienna Austria

ISBN: (纸本)9781450395670

Face detection is a well-known issue in image processing, and numerous studies are present in this field. A prominent part of the work is devoted to RGB images, leaving depth and thermal data with less interest. However, in some conditions like low-light areas where face detection is needed, non-RGB sensors might perform better. Also, mounting an additional RGB camera could be challenging or not possible, considering privacy concerns. In this work, current deep learning methodologies are employed to train depth and thermal detection models. The training is done using combined publicly available data that is processed by us for this purpose in order to create necessary annotations for a learning process. The resulting models are validated on a new trimodal dataset collected for this experiments purpose. It contains images captured with RGB, depth, and thermal sensors. Various scenes with single and multiple faces appearances can be found. The results show that non-RGB solutions can be applied in practice with highly robust accuracy and their efficiency is close to RGB detectors. However, their performance depends on the environment and that circumstances are described later in this article.

关键词： image modalities depth face detection thermal face detection datasets modalities comparison deep learning

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Feature-Guided machine-Centric image Coding for Downstream Tasks

Feature-Guided Machine-Centric Image Coding for Downstream T...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Kwak, Sangwoon Yun, Joungil Choo, Hyon-Gon Kim, Munchurl Electronics and Telecommunications Research Institute Korea Republic of Korea Advanced Institute of Science and Technology Korea Republic of

ISBN: (纸本)9798350313154

Video coding, a process of compressing and decompressing digital video content, has traditionally been optimized for human visual systems by reducing its size while maintaining the human perceptual quality. However, with the remarkable progress of artificial intelligence (AI) technology, the need for machine-centric coding has rapidly been increasing in recent years. In response to these trends, international standardization organizations such as MPEG are actively working to develop and launch new standards on coding technologies for machines, called video coding for machines (VCM). In this paper, we present a novel feature-guided block-wise image blending method for image compression, which is suitable for machine applications such as object detection and segmentation. For this, we use a gradient map of the feature loss using the pretrained encoder part of a task-specific network as guide for input degradation, so that the degraded input images can be effectively compressed for machine-centric tasks. Our method is simple but effective because additional training is not required by utilizing the pretrained encoder parts of networks for targeted tasks. Experimental results show that BD-rate gains can be obtained by applying our proposed method with averages 11% and 8% for object detection and instance segmentation tasks, respectively, compared to the image anchor results of MPEG-VCM reference software v0.4. © 2023 IEEE.

关键词： Computer vision

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Learning-Based Visual-Strain Fusion for Eye-in-Hand Continuum Robot Pose Estimation and Control

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IEEE TRANSACTIONS ON ROBOTICS 2023年第3期39卷 2448-2467页

作者： Wang, Xiaomei Dai, Jing Tong, Hon-Sing Wang, Kui Fang, Ge Xie, Xiaochen Liu, Yun-Hui Au, Kwok Wai Samuel Kwok, Ka-Wai Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China Multi Scale Med Robot Ctr Ltd Hong Kong Peoples R China Harbin Inst Technol Dept Automation Shenzhen 518055 Peoples R China Guangdong Key Lab Intelligent Morphing Mech & Adap Shenzhen 518055 Peoples R China Chinese Univ Hong Kong Dept Mech & Automation Engn Hong Kong Peoples R China

image processing has significantly extended the practical value of the eye-in-hand camera, enabling and promoting its applications for quantitative measurement. However, fully vision-based pose estimation methods sometimes encounter difficulties in handling cases with deficient features. In this article, we fuse visual information with the sparse strain data collected from a single-core fiber inscribed with fiber Bragg gratings (FBGs) to facilitate continuum robot pose estimation. An improved extreme learning machine algorithm with selective training data updates is implemented to establish and refine the FBG-empowered (F-emp) pose estimator online. The integration of F-emp pose estimation can improve sensing robustness by reducing the number of times that visual tracking is lost given moving visual obstacles and varying lighting. In particular, this integration solves pose estimation failures under full occlusion of the tracked features or complete darkness. Utilizing the fused pose feedback, a hybrid controller incorporating kinematics and data-driven algorithms is proposed to accomplish fast convergence with high accuracy. The online-learning error compensator can improve the target tracking performance with a 52.3%-90.1% error reduction compared with constant-curvature model-based control, without requiring fine model-parameter tuning and prior data acquisition.

关键词： Robot sensing systems Robots Sensors Cameras Pose estimation Robot vision systems Robot kinematics Camera pose estimation fiber Bragg grating (FBG) hybrid control online learning visual-strain fusion

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Accurate image Registration Using Evolutionary Algorithms 5th

Accurate Image Registration Using Evolutionary Algorithms

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5th International Conference on Data Science, machine Learning and applications, ICDSMLA 2023

作者： Gobi, N. Kumar, Ritesh Kumar, Deepak Singh, Ashutosh Kr. Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Mathematics Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Noida India

ISBN: (纸本)9789819780426

Photograph registration is aligning or more excellent snapshots of the same bodily object or scene;it's widely used in many regions of image processing and pc imaginative and prescient. Evolutionary algorithms are optimization algorithms stimulated with herbal choice aid and used efficiently for image registration. The principal advantage of evolutionary algorithms is their potential to remedy complex optimization tasks without relying on a given set of heuristics. Additionally, they require fewer parameters for the convergence than conventional optimization algorithms with gradient descent and might reap better accuracy with reduced computational time. Evolutionary algorithms can also deal with multiple goals simultaneously, making them suitable for photo registration packages. Genetic algorithms can deal with both simple and complex problems and can be efficiently deployed for registration responsibilities in a disbursed computing place. Evolutionary algorithms have been used efficaciously for many picture registration programs, which include medical imaging, satellite picture registration, and face reputation. Their combination of high accuracy and low computational time makes them a famous desire for registration troubles inside the subject of pc vision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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A Review of machine vision applications in Aerospace Manufacturing Quality Inspection

A Review of Machine Vision Applications in Aerospace Manufac...

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Computer, Control and Robotics (ICCCR), International Conference on

作者： Li Deng Gang Liu Yilian Zhang School of Mechanical and Automotive Engineering Shanghai University Of Engineering Science Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

machine vision is a technology that uses image processing and analysis techniques to acquire and understand image information, enabling the recognition, measurement, and detection of objects. machine vision is widely used in aerospace manufacturing for automated production, quality inspection, and robot guidance. It can improve the efficiency and quality of aerospace manufacturing, reduce labor costs and risks, promote innovation and optimization, adapt to various inspection needs, and realize intelligent, automated, and digital manufacturing processes. This paper introduces the types of vision technologies, overviews the typical applications of machine vision for quality inspection in aerospace manufacturing, (including component surface inspection, drilling quality inspection, assembly quality inspection, and gluing quality inspection), analyzes the advantages, challenges, and development trends of machine vision for quality inspection in aerospace manufacturing, and looks forward to future research directions to support further research and application.

关键词： Drilling Technological innovation Reviews machine vision Inspection Market research Sensor systems

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Detection of Paddy Plant Diseases Using Google Teachable machine 6th

Detection of Paddy Plant Diseases Using Google Teachable Mac...

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6th International Conference on Soft Computing and Data Mining (SCDM)

作者： Ramli, Nor Azuana Pratondo, Agus Sulaiman, Sahimel Azwal Yusoff, Wan Nur Syahidah Wan Abu, Noratikah Univ Malaysia Pahang Al Sultan Abdullah Ctr Math Sci Kuantan 26300 Pahang Malaysia Telkom Univ Sch Appl Sci Selaru lt 3Jl Telekomunikasi 1 Bandung 40257 West Java Indonesia

ISBN: (纸本)9783031669644;9783031669651

Malaysia faced a shortage of rice at the end of the previous year. Due to this shortage, the government had to import rice to meet the needs of the people, at the same time affecting the national economy. The shortage of rice can be caused by many factors and one of them is because of the decrease in rice production productivity due to the failure to prevent diseases that affect crop yields earlier. If no control measures are implemented after the infection begins, the disease may cause rice yield losses of up to half of the production. Hence, early detection of these diseases is important for effective management and control strategies. This study aims to develop a model that is able to detect paddy plant disease by using Google Teachable machine. This model is compared to the model developed using You Only Look Once (YOLO) version 8. As the primary dataset has not been collected yet, this study utilized dataset from the Internet. Overall, our findings highlight the model developed using Google Teachable machine outperforms the model developed using YOLOv8 in terms of accuracy, simplicity and performance time as it can be completed in half an hour compared toYOLOv8 which took 2.083 h to complete the training. For future study, the model from Teachable machine will be deployed through mobile applications for disease monitoring using data from drones.

关键词： paddy plant disease computer vision image processing

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Guest Editorial: Non-Euclidean machine Learning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2022年第2期44卷 723-726页

作者： Zafeiriou, Stefanos Bronstein, Michael Cohen, Taco Vinyals, Oriol Song, Le Leskovec, Jure Lio, Pietro Bruna, Joan Gori, Marco Imperial Coll London Dept Comp London SW7 2AZ England Univ Svizzera Italiana Inst Computat Sci CH-6900 Lugano Switzerland Qualcomm Technol Netherlands BV NL-6546 Nijmegen Netherlands Google DeepMind London AB101 England Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi 51133 U Arab Emirates Stanford Univ Comp Sci Dept Stanford CA 94305 USA Univ Cambridge Comp Lab William Gates Bldg Cambridge CB3 0FD England NYU Courant Inst Math Sci New York NY 10012 USA Univ Siena Dept Informat Engn & Math I-53100 Siena SI Italy

Over the past decade, deep learning has had a revolutionary impact on a broad range of fields such as computer vision and image processing, computational photography, medical imaging and speech and language analysis and synthesis etc. Deep learning technologies are estimated to have added billions in business value, created new markets, and transformed entire industrial segments. Most of today’s successful deep learning methods such as Convolutional Neural Networks (CNNs) rely on classical signal processing models that limit their applicability to data with underlying Euclidean grid-like structure, e.g., images or acoustic signals. Yet, many applications deal with non-Euclidean (graph- or manifold-structured) data. For example, in social network analysis the users and their attributes are generally modeled as signals on the vertices of graphs. In biology protein-to-protein interactions are modeled as graphs. In computer vision & graphics 3D objects are modeled as meshes or point clouds. Furthermore, a graph representation is a very natural way to describe interactions between objects or signals. The classical deep learning paradigm on Euclidean domains falls short in providing appropriate tools for such kind of data. Until recently, the lack of deep learning models capable of correctly dealing with non-Euclidean data has been a major obstacle in these fields. This special section addresses the need to bring together leading efforts in non-Euclidean deep learning across all communities. From the papers that the special received twelve were selected for publication. The selected papers can naturally fall in three distinct categories: (a) methodologies that advance machine learning on data that are represented as graphs, (b) methodologies that advance machine learning on manifold-valued data, and (c) applications of machine learning methodologies on non-Euclidean spaces in computer vision and medical imaging. We briefly review the accepted papers in each of the groups.

关键词： Special Issues And Sections Deep Learning machine Learning Data Models Learning Systems Computer applications Manifolds

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Hybrid two-stage cascade for instance segmentation of overlapping objects

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PATTERN ANALYSIS AND applications 2023年第3期26卷 957-967页

作者： Yang, Yakun Luo, Wenjie Tian, Xuedong Hebei Univ Sch Cyber Secur & Comp Baoding 071002 Peoples R China Hebei Univ Hebei Machine Vis Engn Res Ctr Baoding 071002 Peoples R China Hebei Univ Lab Intelligence Image & Text Baoding 071002 Peoples R China

Although two-stage methods of instance segmentation achieve better performance than one-stage counterparts, the segmentation results on overlapping objects are unsatisfactory. We found that occlusion significantly impacts the location of adjacent objects and produces coarse masks without adequate refinements. To circumvent the issue, we propose a hybrid model for instance segmentation called HTCIS, which iteratively forms the detection and segmentation. The main idea is to improve overall performance by optimizing every component based on a two-stage cascade structure. Compared with existing models, our approach decreases the loss of feature information, including semantic and detailed features. The detection branch prioritizes location accuracy when ranking bounding boxes, while the segmentation branch explores more contextual information and segments pixels in a multi-view fashion with the guide of an attention mechanism. Experimental results demonstrate that HTCIS is capable of processing occlusion. We conclude that multi-refinement of two-stage cascade is essential for accurate segmentation of overlapping objects, and our optimization is efficient in achieving this goal.

关键词： Computer vision Instance segmentation Two-stage cascade model Hybrid tasks learning Object occlusion

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ComAI: Enabling Lightweight, Collaborative Intelligence by Retrofitting vision DNNs 41

ComAI: Enabling Lightweight, Collaborative Intelligence by R...

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41st IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Jayarajah, Kasthuri Wanniarachchige, Dhanuja Abdelzaher, Tarek Misra, Archan Univ Maryland Baltimore Cty Baltimore MD 21228 USA Singapore Management Univ Singapore Singapore Univ Illinois Champaign IL USA

ISBN: (纸本)9781665458221

While Deep Neural Network (DNN) models have transformed machine vision capabilities, their extremely high computational complexity and model sizes present a formidable deployment roadblock for AIoT applications. We show that the complexity-vs-accuracy-vs-communication tradeoffs for such DNN models can be significantly addressed via a novel, lightweight form of "collaborative machine intelligence" that requires only runtime changes to the inference process. In our proposed approach, called ComAI, the DNN pipelines of different vision sensors share intermediate processing state with one another, effectively providing hints about objects located within their mutually-overlapping Field-of-Views (FoVs). CoMAI uses two novel techniques: (a) a secondary shallow ML model that uses features from early layers of a peer DNN to predict object confidence values in the image, and (b) a pipelined sharing of such confidence values, by collaborators, that is then used to bias a reference DNN's outputs. We demonstrate that CoMAI (a) can boost accuracy (recall) of DNN inference by 20-50%, (b) works across heterogeneous DNN models and deployments, and (c) incurs negligible processing, bandwidth and processing overheads compared to non-collaborative baselines.

关键词： Deep learning Runtime Computational modeling machine vision Conferences Pipelines Neural networks

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Development, Calibration, and image processing of Underwater Structured Light vision System: A Survey

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Fan, Junfeng Wang, Xuan Zhou, Chao Ou, Yaming Jing, Fengshui Hou, Zengguang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China China Univ Min & Technol Sch Mech Elect & Informat Engn Dept Mech Engn Beijing 100083 Peoples R China

The underwater structured light vision system has the characteristics of a large amount of information, high measurement accuracy, and good robustness, which has attracted the attention of researchers and is widely used in the underwater 3-D measurement. In this article, the state-of-the-art techniques of the underwater structured light vision system are comprehensively summarized, which provides valuable guidance for relevant researchers. First, underwater structured light vision systems are classified and introduced, and their advantages and disadvantages are analyzed. Then, the calibration methods of the underwater structured light vision system are introduced, including underwater camera calibration, underwater laser calibration, and underwater projector calibration. Besides, the image processing methods of the underwater structured light vision system are analyzed, including laser structured light image processing and coded structured light image processing. Moreover, the applications of the underwater structured light vision system in underwater archeology, seabed mapping, and underwater structure inspection are introduced. On this basis, the research directions of the underwater structured light vision system are prospected.

关键词： machine vision Measurement by laser beam Three-dimensional displays Cameras Sea measurements Calibration image processing image processing system calibration system development underwater structured light vision system

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