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共 18111 条记录，以下是411-420 订阅

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Deep Learning based Detection, Segmentation and vision based Pose Estimation of Staircase 1

Deep Learning based Detection, Segmentation and Vision based...

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1st international conference on the Paradigm Shifts in Communication, Embedded Systems, Machine Learning and signal processing, PCEMS 2022

作者： Rekhawar, Nilakshi Govindani, Yogesh Rao, Neeraj Visvesvaraya National Institute of Technology Department of Electronics and Communication Engineering Nagpur India

ISBN: (纸本)9781665459044

Mobile robots can move around in the surrounding. They are used in military, industrial applications, for surveillance tasks, etc. These tasks involve multi-floor navigation through the staircase. For efficient and safe stair climbing, it is necessary for the robot to align itself with the staircase. This paper presents a deep learning based approach for staircase detection, semantic segmentation of stair edges and vision based techniques with statistical operations for pose estimation of the staircase. The objective is to solve the problem of staircase traversing for mobile robots without any prior knowledge of the stair geometry and location by iteratively calculating the pose of the staircase from the robot's point of view till it gets aligned with the staircase. The experimental results of staircase detection, semantic segmentation and pose estimation algorithms are presented further in the paper. © 2022 IEEE.

关键词： Stairs

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Image processing for robotic Control in Life Science Laboratory Automation 5

Image Processing for Robotic Control in Life Science Laborat...

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5th international conference on robotics and Computer vision, ICRCV 2023

作者： Yu, Haoying Møller, Vilhelm Krarup Frandsen, Rasmus John Normand Mansourvar, Marjan Department of Electrical Engineering Technical University of Denmark Copenhagen Denmark Department of Biotechnology and Biomedicine Technical University of Denmark Copenhagen Denmark

ISBN: (纸本)9798350326369

In the contemporary age of advanced technology, image processing plays a vital role as an efficient and indispensable tool across industries, enterprises, monitoring systems, and various applications. This study focuses on employing image processing techniques with an Opentrons industrial robot to ensure quality control during production. By leveraging the YOLOv5 model, the robot adeptly recognizes diverse pipette tip types, identifies liquids by their colors, and accurately measures liquid levels to estimate volume percentages within each tip. These detection results then guide a programmable robot, enabling it to perform specific actions on the tips based on predetermined parameters. This paper provides a comprehensive description of the methodology, implementation, and evaluation of our integrated control-robotic system, highlighting its potential to significantly enhance efficiency and precision in laboratory settings. © 2023 IEEE.

关键词： Quality control

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Text-Guided Few-Shot Semantic Segmentation with Training-Free Multimodal Feature Matching

Text-Guided Few-Shot Semantic Segmentation with Training-Fre...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Buthmann, Guillaume Sakai, Tomoya Qiu, Haoxiang Katsuki, Takayuki Kimura, Daiki IBM Research Tokyo Japan Mines Paris - PSL University Paris France

ISBN: (纸本)9798350368741

This paper addresses few-shot semantic segmentation (FSS) guided by text, where we classify unseen novel classes using image and text references as in-context examples, without the need for training. We enhance the quality and stability of the segmentation masks generated by FSS by combining the capability of open-vocabulary zero-shot semantic segmentation (ZSS) based on foundation models for image and text. We propose a training-free approach using multimodal feature matching that performs segmentation by identifying regions in a target image that match the features from both the image and text references. Experimental results demonstrate that the proposed method outperforms state-of-the-art FSS and ZSS methods. © 2025 IEEE.

关键词： few-shot segmentation semantic segmentation vision-language model zero-shot segmentation

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High-Throughput Asynchronous Convolutions for High-Resolution Event-Cameras 8

High-Throughput Asynchronous Convolutions for High-Resolutio...

引用

8th international conference on Event-Based Control, Communication and signal processing (EBCCSP)

作者： Rosa, Leandro de Souza Dinale, Aiko Bamford, Simeon Bartolozzi, Chiara Glover, Arren Italian Inst Technol Event Driven Percept Robot Genoa Italy Delft Univ Technol Cognit Robot Grp Delft Netherlands

ISBN: (数字)9781665453493

ISBN: (纸本)9781665453493

Event cameras are promising sensors for on-line and real-time vision tasks due to their high temporal resolution, low latency, and redundant static data elimination. Many vision algorithms use some form of spatial convolution (i.e. spatial pattern detection) as a fundamental component. However, additional consideration must be taken for event cameras, as the visual signal is asynchronous and sparse. While elegant methods have been proposed for event-based convolutions, they are unsuitable for real scenarios due to their inefficient processing pipeline and subsequent low event-throughput. This paper presents an efficient implementation based on decoupling the event-based computations from the computationally heavy convolutions, increasing the maximum event processing rate by 15.92x to over 10 million events/second, while still maintaining the event-based paradigm of asynchronous input and output. Results on public datasets with modern 640 x 480 event-camera recordings show that the proposed implementation achieves real-time processing with minimal impact on the convolution result, while the prior state-of-the-art results in a latency of over 1 second.

关键词： Event-Driven Cameras Asynchronous processing Spatial Convolutions Real-Time processing

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A Rust Removal and Painting robot for Offshore Wind Turbine Towers 4

A Rust Removal and Painting Robot for Offshore Wind Turbine ...

引用

4th international signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Pan, Xiaoyu Su, Qichao Yang, Guangzhao Jiamusi University Jiamusi China

ISBN: (纸本)9798331528676

In response to the significant risks and low efficiency associated with traditional manual operations on offshore wind turbine towers, a robotic rust removal and painting system tailored for offshore wind turbine tower cylinders is proposed. This robotic system is designed to replace human involvement in routine inspections, rust removal, cleaning, and anti-corrosion painting tasks on offshore wind turbine tower cylinders. The robot primarily comprises three key components: a chain-driven permanent magnet adhesion walking mechanism, a rust removal unit, and a high-pressure spraying mechanism. It adopts a tracked walking mode, with multiple adhesion units evenly distributed on each track. The rust removal mechanism is mounted at the front of the robot, and three high-pressure spray guns are located at the rear, each equipped with windproof covers. Through the establishment of mathematical models for the robot, static and dynamic analyses are conducted to explore critical values for three scenarios: climbing, sliding, flipping, and forward movement. Based on these critical values, physical models are designed, and performance tests involving climbing, rust removal, and painting are conducted to determine the robot's performance parameters. The robot's overall structure is straightforward, with characteristics such as ease of operation, convenient maintenance, and a high degree of automation. It is not only suitable for rust removal, cleaning, and anti-corrosion painting tasks on offshore wind power equipment but also applicable for routine fault inspections of offshore wind power equipment. © 2024 IEEE.

关键词： Offshore wind turbines

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Dynamic Balancing of Bipedal robot on Inclined Terrain using Polynomial Trajectories 23

Dynamic Balancing of Bipedal Robot on Inclined Terrain using...

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7th international conference on Graphics and signal processing (ICGSP)

作者： Semwal, Vijay Bhaskar Tokas, Pratibha Maske, Nilesh Khare, Nilay MANIT Bhopal Bhopal India

ISBN: (纸本)9798400700460

This paper presents foot trajectory planning for bipedal robots during the swing phase. This experiment aims to design an efficient and dynamically stable foot trajectory for a 10-DOF (Degree of freedom) bipedal robot. The experiment considers the foot's quadratic, cubic, and quintic polynomial trajectories while climbing the inclined plane. Forward and Inverse kinematics is used to calculate the variation of joint angles during the swing phase. Dynamic Balance Margin DBM has been computed using Zero Moment Point ZMP principle while walking. The angle variation and the ZMP for the aforementioned polynomial trajectories are presented. The maximum DBM is achieved for quintic polynomial trajectory, leading to a considerable reduction in power consumption. Our paper presents a dynamically stable walking pattern of a bipedal robot. It also ensures repeatability, which has been validated using a stick diagram.

关键词： Biped robot Polynomial Trajectory Forward and Inverse Kinematics Zero Moment Point Dynamic Balance Margin Dynamic Stability

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CVOCR: Context vision OCR 20

CVOCR: Context Vision OCR

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20th IEEE/ASME international conference on Mechatronic and Embedded Systems and Applications (MESA)

作者： Sadeghian, Rasoul Shahin, Shahrooz Sareh, Sina Royal Coll Art RCA Robot Lab London England

ISBN: (纸本)9798331516246;9798331516239

Optical Character Recognition (OCR) technologies are crucial for automated information extraction across various domains. However, the intricate layouts and diverse text properties often found on different products can complicate accurate data retrieval and categorization. This paper introduces Context vision OCR (CVOCR), a versatile framework designed to address the proposed challenges using advanced image processing and text analysis techniques. While CVOCR is applicable to any OCR-related application, this paper focuses on pharmaceutical items as a case study due to the stringent accuracy requirements and the complexity of medicine packaging. The CVOCR algorithm is developed based on the integration of the Fast Super-Resolution Convolutional Neural Network (FSRCNN) for enhanced image clarity, LayoutLMv2 for spatial layout understanding, Tesseract OCR for robust character recognition, and GPT-Neo for advanced contextual analysis. The strategic integration of these components form a cohesive system that significantly improves text detection and interpretation accuracy. We demonstrate the efficacy of the CVOCR system through testing on various pharmaceutical products, where it consistently outperforms Tesseract OCR.

关键词： Optical character recognition FSRCNN GPT-Neo large language model Tesseract OCR

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Research on indoor robot navigation algorithm based on deep reinforcement learning 2

Research on indoor robot navigation algorithm based on deep ...

引用

2nd international conference on Image processing, Computer vision and Machine Learning, ICICML 2023

作者： Lei, Yaohua Cheng, Yunli Tan, Xueqin Tan, Yanxian Software Engineering Institute of Guangzhou Guangdong Guangzhou510990 China

ISBN: (纸本)9798350331417

Highly intelligent robot is the trend in the new round of development, and it is also the ultimate goal of robot navigation technology *** ability to learn is an important embodiment of robot *** navigation is the basic function of indoor mobile robots[1]and it is the premise and key technology to realize the autonomy and intelligence of *** the continuous development of robot-related technologies and the continuous expansion of application scope, the tasks faced by robots are more challenging, and the work scenes are increasingly complex and diversified, which puts forward new requirements and challenges for navigation technology. Indoor robot navigation systems need not only to maintain accuracy and stability in fixed scenes, but also to be able to handle dynamic changes in the environment, and to take into account safety and the impact on human comfort in an environment that coexists with *** paper takes the indoor mobile robot platform as the research object[2], introduces the machine learning algorithm based on neural network into the navigation task[3], utilizes the existing research achievements in the field of deep learning to build a robot navigation system based on different learning modes, and endows the robot with intelligent perception ability and navigation strategy learning ability[4],. © 2023 IEEE.

关键词： Imitation learning Indoor robot navigation Navigation algorithms reinforcement learning

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A Neuromorphic System for the Real-time Classification of Natural Textures

A Neuromorphic System for the Real-time Classification of Na...

引用

IEEE international conference on robotics and Automation (ICRA)

作者： Brayshaw, George Ward-Cherrier, Benjamin Pearson, Martin J. Univ Bristol Dept Aerosp Engn Bristol Avon England Univ Bristol Dept Engn Math Bristol Avon England Univ West England Bristol Robot Lab Bristol Avon England

ISBN: (纸本)9798350384581;9798350384574

Tactile exploration of surfaces is a key component of everyday life, allowing us to make complex inferences about our environments even when vision is occluded. The emergence of biomimetic neuromorphic hardware in recent years has furthered our ability to create biologically plausible sensing solutions. While these platforms continue to improve in regards to latency and power consumption, within recent literature on tactile texture classification there is an emphasis on accuracy at the expense of real-time processing. In order for these tactile sensing systems to find use outside of experimental laboratory environments, it is key to design systems capable of capturing and processing data in real-time. Within this paper we present a system for the real-time classification of texture using a neuromorphic tactile sensor, a spiking neural network and a novel decision making algorithm. Our real-time system achieves classification accuracies of 94% on a dataset of 11 natural textile textures. Furthermore our system is capable of identifying textures at human-level performance in as little as 84ms. Additionally, benchmarking our system across CPU, GPU and Loihi2 hardware platforms resulted in a 96% reduction in power consumption on the neuromorphic platform. This system out-performed previous work by the authors and the state of art, both in terms of accuracy and classification speed.

关键词： Digital storage

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Diverse Image processing Techniques for Machine Learning Methods 3

Diverse Image Processing Techniques for Machine Learning Met...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Bhagya, A. Perumal, S. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350361186

Machine learning is a trending topic in the area of computer vision, which makes the machine able to learn about it without being expressly programmed using various algorithms. When a model is designed to predict future-based on historical data using machine learning algorithms, data is vital because high quality data is needed to run the model efficiently and to predict highly accurate results. Machine learning algorithms receive data in many forms, like audio (speech recognition), images (feature recognition), numeric data (like height and weight) etc. When it comes to computer vision, the machine learning algorithms must be able to understand the image data, and before the images are processed to the algorithms it should be pre-processed (cleaned and set to the desired format) using various image pre-processing techniques. This research paper presents various image processing techniques, literature reviews on various image processing techniques and image PSNR value after applying median filter, grayscale filter and box filters. © 2024 IEEE.

关键词： Median filters

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