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检索条件"任意字段=Proceedings - 1st Canadian Conference on Computer and Robot Vision"

共 434 条记录，以下是41-50 订阅

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Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation

Look Closer to Segment Better: Boundary Patch Refinement for...

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IEEE/CVF conference on computer vision and Pattern Recognition (CVPR)

作者： Tang, Chufeng Chen, Hang Li, Xiao Li, Jianmin Zhang, Zhaoxiang Hu, Xiaolin Tsinghua Univ THU Bosch JCML Ctr State Key Lab Intelligent Technol & Syst Dept Comp Sci & TechnolInst AIBNRist Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot HKISI Beijing Peoples R China

ISBN: (纸本)9781665445092

Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework to improve the boundary quality based on the results of any instance segmentation model, termed BPR. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted instance boundaries. The refinement is accomplished by a boundary patch refinement network at higher resolution. The proposed BPR framework yields significant improvements over the Mask R-CNN baseline on Cityscapes benchmark, especially on the boundary-aware metrics. Moreover, by applying the BPR framework to the "PolyTransform + SegFix" baseline, we reached 1st place on the Cityscapes leaderboard. Code is available at https://***/tinyalpha/BPR.

关键词： Measurement computer vision Codes Benchmark testing Business process re-engineering Pattern recognition Spatial resolution

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Design and Evaluation of a Self-Driving Prototype Using Raspberry Pi and EzBlock

Design and Evaluation of a Self-Driving Prototype Using Rasp...

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Emerging Technologies for Dependable Internet of Things (ICETI), International conference on

作者： Mahmoud Nabeel Younus Ahmed Khazal Younis Technical Engineering College Northern Technical University Mosul Iraq Technical Engineering College for Computer and AI/Mosul Northern Technical University Mosul Iraq

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

Self-driving cars are an innovative technology in transportation that seeks to eliminate human mistakes, save lives, and increase the efficiency of traffic flow. In this work, a self-driving prototype with a Raspberry Pi 4 Model B, several sensor components, and their assessment were proposed as ultrasonic and grayscale sensors, a Raspberry Pi camera, and a SunFounder robot hat, which are all managed via a mobile application from Ezblock studio. To ensure that control and decision-making are in real-time, algorithms were incorporated into the EzBlock system. The prototype was tested in various scenarios, employing multiple levels of lighting and path types and straight and curved paths with sharp and curved corners. The system's high performance was demonstrated in good lighting, whereby it could detect lanes, signs, and obstacles and avoid them. However, some difficulties were observed during tests, such as sharp corners, wide paths, and low light conditions, causing problems with camera-based detection. This work demonstrates the viability of low-cost, Raspberry Pi-based self-driving prototypes for applications such as autonomous vehicles.

关键词： Performance evaluation Face recognition robot vision systems Prototypes Lighting Transportation Gray-scale Cameras Acoustics Thermal stability

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Skin Cancer Detection and Classification Based on Deep Learning

Skin Cancer Detection and Classification Based on Deep Learn...

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2022 International conference on Cyber Resilience, ICCR 2022

作者： Said, Raed A. Raza, Hammad Muneer, Salman Amjad, Kamran Mohammed, Abdul Salam Akbar, Syed Shehryar Zonain, Muhammad Aslam, Muhammad Arslan Canadian University Dubai Dubai United Arab Emirates Canadian University Dubai Faculty of Mangament 1st Interchange Sheikh Zayed Road PO Box 117781 Dubai117781 United Arab Emirates School of Computer Science Ncbae Lahore Pakistan Skyline University University Head General Education P/O Box 1797 Sharjah1797 United Arab Emirates The Islamia University of Bahawalpur Department of Computer Systems Engineering Bahawalpur Pakistan

ISBN: (纸本)9781665461221

Melanoma (MEL) is a form of malignant cancer of the skin that is harmful to health. Appropriate analysis of MEL at initial phase is critical for the achievement percentage of comprehensive treatment. Dermoscopic pictures with benign and malignant types of skin cancer can be examined by computer vision System (CVS) to rationalize the procedure of skin cancer classification/detection. Hence, in this studies an automated method is proposed and developed that will help to detect and classify the skin cancer disease by processing skin images. For this purpose, the Deep Learning (DL) technique is performed with the assistance of the TensorFlow Back end and the Keras libraries. The HAM -10000 dataset is used for this purpose it consists of 10015 dermoscopic images which is publicly accessible. 70% of the dataset is deployed for training and 24% for testing and 6% for validation of our proposed model. In pre-processing step, data set is modified by converting into grayscale for removing hair and noise from it. Furthermore, thresholding, morphological filtering, Black Hat and Inpaint is applied to enhance the database. After that data augmentation techniques (Tensor flow back and Keras) are used to enhance the size and quality of training dataset. In particular, MobileN et optimizer is used for classification purpose. The proposed technique provides a sophisticated diagnosis and classification accuracy when compared with previous techniques. Top-N accuracy has been used to validate the performance of proposed technique. The proposed method shows the Top-l accuracy of about 90%. Top-l precision of 80 percent is considered equivalent to a best systems and proposed model accomplished the Top-1 precision of 90%. This top efficiency of the classification and the rapidity of meeting to the ultimate outcomes are the features of this study related to other research. © 2022 IEEE.

关键词： Deep learning

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Machine vision to Empower an Intelligent Personal Assistant for Assembly Tasks 1st

Machine Vision to Empower an Intelligent Personal Assistant ...

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1st International conference on Optimization, Learning Algorithms and Applications (OL2A)

作者： Talacio, Matheus Funchal, Gustavo Melo, Victoria Piardi, Luis Vallim, Marcos Leitao, Paulo Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Univ Tecnol Fed Parana UTFPR Avenida 7 Setembro 3165 BR-80230901 Curitiba Parana Brazil

ISBN: (纸本)9783030918859;9783030918842

In the context of the fourth industrial revolution, the integration of human operators in emergent cyber-physical systems assumes a crucial relevance. In this context, humans and machines can not be considered in an isolated manner but instead regarded as a collaborative and symbiotic team. Methodologies based on the use of intelligent assistants that guide human operators during the execution of their operations, taking advantage of user friendly interfaces, artificial intelligence (AI) and virtual reality (VR) technologies, become an interesting approach to industrial systems. This is particularly helpful in the execution of customised and/or complex assembly and maintenance operations. This paper presents the development of an intelligent personal assistant that empowers operators to perform faster and more cost-effectively their assembly operations. The developed approach considers ICT technologies, and particularly machine vision and image processing, to guide operators during the execution of their tasks, and particularly to verify the correctness of performed operations, contributing to increase productivity and efficiency, mainly in the assembly of complex products.

关键词： computer vision

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Enhancing Industrial Automation Flexibility Through Neural Network-Empowered Machine vision Applications 1st

Enhancing Industrial Automation Flexibility Through Neural N...

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1st International conference on Artificial Intelligence and its Applications, ICAIA 2023

作者： George, Navin M. Rosero, Evelyn Mayakannan, S. Shameem, A. Divya, S. Arul, N. Electronics and Communication Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India Riobamba060155 Ecuador Department of Mechanical Engineering Vidya Vikas College of Engineering and Technology Namakkal Tamilnadu Tiruchengode India AMET Business School AMET University Tamilnadu Chennai India Erode Tamil Nadu Perundurai638057 India Department of Mechatronics Engineering Sona College of Technology Tamilnadu Salem India

ISBN: (纸本)9783031843969

vision-based systems provide non-contact sensory input for processing and feedback, expanding the capabilities of industrial automation applications. Prediction using ANNs aids such conformities in navigating nonlinear computational landscapes. They map the plethora of potential outcomes or zones of uncertainty introduced by the system’s constituents onto a set of feasible options for action. A measure of intelligence is transferred to robotic systems through the use of trained networks. Two uses of machine vision are presented here. The 3 DOF robotic assembly enables precision cutting of fragile materials with the help of visual guiding and pixel elimination. The 6-degrees-of-freedom robot uses a combination of visual feedback from an onboard camera and supervision from an external camera to achieve its goals. Pick-and-place operations are executed with a switching control method. Both systems improved in terms of computing time and convergence once ANN was used to make the strategies intelligent. The retrieved scene image features are put to use by the networks in a variety of ways. The simulation and experimental results that back up the suggested approaches demonstrates the value of artificial neural network in machine vision applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： robotic assembly

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Deep Reinforcement Learning Applied to a robotic Pick-and-Place Application 1st

Deep Reinforcement Learning Applied to a Robotic Pick-and-Pl...

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1st International conference on Optimization, Learning Algorithms and Applications (OL2A)

作者： Gomes, Natanael Magno Martins, Felipe N. Lima, Jose Wortche, Heinrich Hanze Univ Appl Sci Inst Engn Sensors & Smart Syst Grp Groningen Netherlands Polytech Inst Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal INESC TEC Ctr Robot Ind & Intelligent Syst Porto Portugal Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands

ISBN: (纸本)9783030918859;9783030918842

Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. In many cases, the movements of industrial robot manipulators are hard-coded or manually defined, and need to be adjusted if the objects being manipulated change position. To increase flexibility, an industrial robot should be able to adjust its configuration in order to grasp objects in variable/unknown positions. This can be achieved by off-the-shelf visionbased solutions, but most require prior knowledge about each object to be manipulated. To address this issue, this work presents a ROS-based deep reinforcement learning solution to robotic grasping for a Collaborative robot (Cobot) using a depth camera. The solution uses deep Q-learning to process the color and depth images and generate a epsilon-greedy policy used to define the robot action. The Q-values are estimated using Convolutional Neural Network (CNN) based on pre-trained models for feature extraction. Experiments were carried out in a simulated environment to compare the performance of four different pre-trained CNN models (RexNext, MobileNet, MNASNet and DenseNet). Results show that the best performance in our application was reached by MobileNet, with an average of 84 % accuracy after training in simulated environment.

关键词： Cobots Reinforcement learning computer vision Pick-and-place Grasping

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Virtual Teaching for Assembly Tasks Planning 1

Virtual Teaching for Assembly Tasks Planning

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1st IEEE International conference on Human-Machine Systems (ICHMS)

作者： Angleraud, Alexandre Codd-Downey, Robert Netzev, Metodi Houbre, Quentin Pieters, Roel Tampere Univ Unit Automat Technol & Mech Engn Tampere Finland York Univ Dept Elect Engn & Comp Sci Toronto ON Canada

ISBN: (纸本)9781728158716

Small and Medium-size Enterprises require increasingly versatile robots that are capable of learning new skills during their operating life span, in addition to the ability to integrate themselves into human teams as a new and effective partner. Teaching information and skills to a robot can quickly become very complex, especially when considering that the human partner of the robot is inexperienced in the field and does not have access to intuitive interaction channels to train such robots. In this work, a system is proposed that can virtually define an assembly task, based on CAD modelling that describe constraints between assembly parts. These constraints are then extracted in an ontology which is automatically translated into Simple Temporal Networks (stNs) leading to joint action plans. Results show the automatic translation between virtual teaching and knowledge acquired by the robot on a peg-in-hole problem before illustrating the connection to planning for human-robot collaborative tasks. As a proof of concept, these developments demonstrate that a CAD guided assembly planner can circumvent the need for skilled robot programming.

关键词： robot programming

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Clarifying Myths About the Relationship Between Shape Bias, Accuracy, and Robustness

Clarifying Myths About the Relationship Between Shape Bias, ...

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canadian conference on computer and robot vision, CRV

作者： Zahra Golpayegani Patrick st-Amant Nizar Bouguila Gina Cody School of Engineering Concordia University Montreal Canada Zetane Systems Inc.

Deep learning models can perform well when evaluated on images from the same distribution as the training set. However, applying small perturbations in the forms of noise, artifacts, occlusions, blurring, etc. to a model's input image and feeding the model with out-of-distribution (OOD) data can significantly drop the model's accuracy, making it not applicable to real-world scenarios. Data augmentation is one of the well-practiced methods to improve model robustness against OOD data; however, examining which augmentation type to choose and how it affects the OOD robustness remains understudied. There is a growing belief that augmenting datasets using data augmentations that improve a model's bias to shape-based features rather than texture-based features results in increased OOD robustness for Convolutional Neural Networks trained on the ImageNet-1K dataset. This is usually stated as “an increase in the model's shape bias results in an increase in its OOD robustness”. Based on this hypothesis, some works in the literature aim to find augmentations with higher effects on model shape bias and use those for data augmentation. By evaluating 39 types of data augmentations on a widely used OOD dataset, we demonstrate the impact of each data augmentation on the model's robustness to OOD data and further show that the mentioned hypothesis is not true; an increase in shape bias does not necessarily result in higher OOD robustness. By analyzing the results, we also find some biases in the ImageNet-1K dataset that can easily be reduced using proper data augmentation. Our evaluation results further show that there is not necessarily a trade-off between in-domain accuracy and OOD robustness, and choosing the proper augmentations can help increase both in-domain accuracy and OOD robustness simultaneously.

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Optimizing Photoacoustic Tomography: Lymphatic Drainage from the Brain in Pigmented Mice 1st

Optimizing Photoacoustic Tomography: Lymphatic Drainage from...

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1st International conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Mian, Balal Toronov, Vladislav Zhou, Xun Yucel, Yeni H. Ryerson University 350 Victoria Street TorontoONM5B 2K3 Canada Li Ka-Shing Knowledge Institute 7th Floor LKS 735 209 Victoria Street TorontoONM5B 1T8 Canada Keenan Research Centre for Biomedical Science of St. Michael’s Hospital 30 Bond St TorontoONM5B 1W8 Canada Ophthalmology and Vision Sciences and Laboratory Medicine and Pathobiology Temerty Faculty of Medicine Medical Sciences Building University of Toronto 1 King’s College Circle TorontoONM5S 18 Canada

We report the development and results of an efficient optoacoustic tomography protocol based on a multispectral photoacoustic imaging system, novel contrast agent, and novel wavelength optimization technique to image ... 详细信息

ISBN: (纸本)9783030881627

关键词： Cerebrospinal fluid

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Human operator cognitive availability aware Mixed-Initiative control 1

Human operator cognitive availability aware Mixed-Initiative...

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1st IEEE International conference on Human-Machine Systems (ICHMS)

作者： Petousakis, Giannis Chiou, Manolis Nikolaou, Grigoris stolkin, Rustam Univ West Attica Egaleo Greece Univ Birmingham Extreme Robot Lab ERL Birmingham W Midlands England Univ Birmingham Natl Ctr Nucl Robot NCNR Birmingham W Midlands England

ISBN: (纸本)9781728158716

This paper presents a Cognitive Availability Aware Mixed-Initiative Controller for remotely operated mobile robots. The controller enables dynamic switching between different levels of autonomy (LOA), initiated by either the AI and/or the human operator. The controller leverages a state-of-the-art computer vision method and an off-the-shelf web camera to infer the cognitive availability of the operator and inform the AI-initiated LOA switching. This constitutes a qualitative advancement over previous Mixed-Initiative (MI) controllers. The controller is evaluated in a disaster response experiment, in which human operators have to conduct an exploration task with a remote robot. MI systems are shown to effectively assist the operators, as demonstrated by quantitative and qualitative results in performance and workload. Additionally, some insights into the experimental difficulties of evaluating complex MI controllers are presented.

关键词： Controllers

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