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image-Based Force Estimation in Medical applications: A Review

IEEE SENSORS JOURNAL 2021年第7期21卷 8805-8830页

作者： Nazari, Ali A. Janabi-Sharifi, Farrokh Zareinia, Kourosh Ryerson Univ Dept Mech & Ind Engn Toronto ON M5B 2K3 Canada

Minimally invasive robotic interventions have highlighted the need to develop efficient techniques to measure forces applied to the soft tissues. Since the last decade, many scholars have focused on micro-scale and macro-scale robotic manipulations. Early articles used the model of soft tissue mathematically and tracked the displacement of the contour of the object in the vision system to provide the corresponding force to the user. Lack of knowledge of different materials and the computational complexity led to a transition from model-based to learning-based approaches to interpret the relation between object deformations, extracted from the vision system, and the real forces applied to the object. The dramatic growth of machine learning techniques and its integration with computer vision has brought novel learning-based visual data processing methods to the area. The application of the image-based force estimation methods in a controlled medical intervention has also received significant attention in the last five years. A decent number of surveys have been published on micromanipulation in recent years, especially for cell microinjection. However, the state of the art in meso- and macro-scale medical robotic interventions has not been reviewed. The aim and contribution of this paper are to fill the stated gap by reviewing the recent advances in image-based force estimation in robotic interventions. The survey shows that learning-based force estimation methods are growing significantly by using deep learning-based methods. The survey will encourage researchers and surgeons to apply learning-based algorithms to real-time medical and health-related operations.

关键词： Force Deformable models Biological tissues Robots Computational modeling Sensors Robot sensing systems Deformable object learning-based force estimation minimally invasive robotic surgery soft tissue vision-based force estimation

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Reconfigurable nonlocal thin film nano-cavity for image processing

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OPTICS AND LASER TECHNOLOGY 2024年 176卷

作者： Zhang, Bowei Chen, Zhenyu Liu, Jian Wang, Bin Chamoli, Sandeep Kumar Chongqing Coll Elect Engn CQCEE Chongqing 401331 Peoples R China Nanyang Technol Univ Singapore 637457 Singapore

A combination of optical analog computing and nanophotonic design is attractive due to its fast-processing times, large data processing rates, and high integration with modern imaging systems. However, most of the proposed nano-photonics designs for this purpose have static functionality, which limits their applicability. By modulating the Green's function response of the thin film nanocavity using phase change properties of Sb2S3, we propose tunable bright and dark-field imaging. We optimize the nano-cavity for the bright field in a crystalline and dark field in the amorphous phase of Sb2S3. The proposed design shows a near-unity numerical aperture which enables the resolution of the system to similar to 500 nm. Lastly, we examine the potential integration of this cavity into a standard interferometric setup and scattering microscopy, leveraging the tunable modulation capability of the proposed cavity as a substrate in reflection mode scheme. This ultrathin, on-chip, and real-time tunable lithographic-free imaging system can play a crucial role in a host of applications such as machine vision, medical imaging, and sensing.

关键词： Thin film nanophotonics Reconfigurable photonics Phase change materials Dark and bright mode imaging Interferometric imaging

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Research on Improved image Edge Detection Based on Hough Transform

Research on Improved Image Edge Detection Based on Hough Tra...

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International Conference on image processing and Intelligent Control (IPIC)

作者： Cheng, Long Fang, Jian Wu, Yue Kang, Kai Changchun Univ Technol Elect & Elect Engn Changchun 130012 Peoples R China Changchun Univ Technol Coll Electromech Engn Changchun 130012 Peoples R China Jilin Engn Normal Univ Sch Elect Engn Changchun 130052 Peoples R China

ISBN: (数字)9781510647251

ISBN: (纸本)9781510647251;9781510647244

Line detection in images has always been one of the important research fields of artificial intelligence computer vision for image *** transform (HT) is one of the most extensive detection algorithms in image processing and machine vision applications, and has the advantages of anti-noise. However, it involves a huge amount of computation and too much *** view of its shortcomings, it is recommended to improve its performance. In this paper, a new detection method is cited, and the statistical probability Hough line transformation is *** first step is to use the Canny operator to obtain the edge information of the gray image and use the improved method to detect the straight line segment in the image. This algorithm enhances the accuracy of edge detection and optimizes the extraction of the straight line. At the same time, it has the simplicity and wide applicability of Hough transform.

关键词： image processing straight line detection edge detection Hough transform

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Gait recognition based on sparse linear subspace

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IET image processing 2021年第12期15卷 2761-2769页

作者： Wen, Junqin Wang, Xiuhui Zhejiang Tech Inst Econ Hangzhou Peoples R China China Jiliang Univ Coll Informat Engn Key Lab Electromagnet Wave Informat Technol & Met Hangzhou 310018 Peoples R China

Gait recognition has broad application prospects in intelligent security monitoring. However, due to the variability of human walking states and the complexity of external conditions during sample collection, gait recognition is still facing many challenges. Among them, gait recognition algorithms based on shallow learning are hard to achieve the correct recognition rate required by many applications, while the amount of gait training data cannot meet the needs of model training based on deep learning. To solve the above problem, this paper presents a novel gait recognition scheme based on sparse linear subspace. First, frame-by-frame gait energy images (ffGEIs) are extracted as primary gait features and sparse linear subspace technology is used to represent them for dimension reduction. Second, a new gait classification algorithm based on support vector machine is presented, which adopts Gaussian radial basis function (RBF) kernels to achieve cross-view gait recognition. Finally, the proposed gait recognition approach is evaluated on two open-accessed gait databases to demonstrate its performance.

关键词： image recognition Computer vision and image processing techniques Other topics in statistics Other topics in statistics Neural nets Support vector machines

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Hybrid Architecture using CNN and LSTM for image Captioning in Hindi Language

Hybrid Architecture using CNN and LSTM for Image Captioning ...

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2022 International Conference on machine Learning and Data Engineering, ICMLDE 2022

作者： Poddar, Ayush Kumar Rani, Rajneesh Dr. B.R. Ambedkar National Institute of Technology Punjab Jalandhar India

With the advent of deep learning in recent years, the integration of computer vision with natural language processing has garnered a lot of attention. Generating descriptions from images is one of the most intriguing and focused areas of machine Learning that faces a number of obstacles, particularly when describing images in languages other than English. In image captioning, a computer is trained to understand the visual information of an image and then generate a description based on the image features and reference sentences. Having an application that automatically describes events in their environment and then translates them into a caption or message can make a significant contribution to society. This paper presents a multi-layered CNN-LSTM neural network model that is utilized to recognize and generate Hindi captions for the objects in images. In addition, a variety of models were trained by adjusting hyperparameters and the number of hidden layers to find the optimum model and maximize the likelihood of the resultant Hindi description. Moreover, after testing the effectiveness of our models, it was observed that our model has shown an increase of 34.64% and 29.13% in terms of BLEU score (Unigram) and BLEU score (Bigram) respectively when compared to the existing work done in this field. image captioning in Hindi can have a multitude of applications in today's society, and it can also provide a user-friendly interface for Hindi speakers. © 2023 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Optical Machining Detection System Based on machine vision and Artificial Intelligence 10th

Optical Machining Detection System Based on Machine Vision a...

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10th International Conference on Frontier Computing, FC 2020

作者： Yao, Jin Feng, Jing Liu, Yuzhou Chen, Licheng You, Rentang Sun, Jiaxing Zhang, Xiaofei Xiang, Yongzhi Chen, Xiaoyun Li, Hui School of Mechanical and Material Engineering Wuzhou University Guangxi Wuzhou543000 China

ISBN: (纸本)9789811601149

With the rapid development of automation control field, machine vision technology is becoming more and more mature and has won more and more extensive applications in various fields. By processing the image information, the machine vision system realizes all kinds of measurement and judgment by using the machine instead of human eyes. The purpose of this paper is to study the optical machining detection system based on machine vision and artificial intelligence and to provide technical Suggestions for optical machining detection. This paper briefly introduces the concept of machine vision technology and the main part of machine vision system, this paper expounds the key techniques of machine vision development present situation, this paper analyzes the machine vision technology in the field of optical processing and detection, especially in optical polishing process automation related applications, the experimental results show that the light above 2.1 machine parameters, optical processing algorithm based on machine vision inspection system is more efficient than traditional way. Finally, the future development trend of machine vision in the field of optical machining and detection is prospected. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Lemon-YOLO: An efficient object detection method for lemons in the natural environment

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IET image processing 2021年第9期15卷 1998-2009页

作者： Li, Guojin Huang, Xiaojie Ai, Jiaoyan Yi, Zeren Xie, Wei Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou Peoples R China

Efficient Intelligent detection is a key technology in automatic harvesting robots. However, citrus detection is still a challenging task because of varying illumination, random occlusion and colour similarity between fruits and leaves in natural conditions. In this paper, a detection method called Lemon-YOLO (L-YOLO) is proposed to improve the accuracy and real-time performance of lemon detection in the natural environment. The SE_ResGNet34 network is designed to replace DarkNet53 network in YOLOv3 algorithm as a new backbone of feature extraction. It can enhance the propagation of features, and needs less parameter, which helps to achieve higher accuracy and speed. Moreover, the SE_ResNet module is added to the detection block, to improve the quality of representations produced from the network by strengthening the convolutional features of channels. The experimental results show that the proposed L-YOLO has an average accuracy(AP) of 96.28% and a detection speed of 106 frames per second (FPS) on the lemon test set, which is 5.68% and 28 FPS higher than the YOLOv3, respectively. The results indicate that the L-YOLO method has superior detection performance. It can recognize and locate lemons in the natural environment more efficiently, providing technical support for the machine's picking lemon and other fruits.

关键词： Optical, image and video signal processing Control applications in agriculture Mobile robots Computer vision and image processing techniques Control engineering computing Production engineering computing Industrial applications of IT Robotics Agriculture

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Improving Teledermatology Referral with Edge-AI: Mobile App to Foster Skin Lesion Imaging Standardization 14th

Improving Teledermatology Referral with Edge-AI: Mobile App ...

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14th International Joint Conference on Biomedical Engineering Systems and Technologies

作者： Vasconcelos, Maria Joao M. Moreira, Dinis Alves, Pedro Graca, Ricardo Franco, Rafael Rosado, Luis Fraunhofer Portugal AICOS P-4200135 Porto Portugal Minist Hlth Shared Serv Lisbon Portugal

ISBN: (纸本)9783031206634;9783031206641

Every year, the number of skin cancer cases has been increasing which, consequently, increases the strain on the health care systems around the globe. With the growth of processing power and camera quality on smartphones, the investment in telemedicine and the development of mobile teledermatology applications can, not only contribute to the standardization of image acquisitions but also, facilitate early diagnosis. This paper presents a new process for real-time automated image acquisition of macroscopic skin images with the merging of an automated focus assessment feature-based machine learning algorithm with conventional computer vision techniques to segment dermatological images. Three datasets were used to develop and evaluate the proposed methodology. One comprised of 3428 images acquired with a mobile phone for this purpose and 1380 from the other two datasets which are publicly available. The best focus assessment model achieved an accuracy of 88.3% and an F1-Score of 86.8%. The segmentation algorithm obtained a Jaccard index of 85.81% for the SMARTSKINS dataset and 68.59% for the Dermofit dataset. The algorithms were deployed to a mobile application, available in Android and iOS, without causing any performance hindrances. The application was tested in a real environment, being used in a 10-month pilot study with six General and Family Medicine doctors and one Dermatologist. The easiness to acquire dermatological images, image quality, and standardization were referred to as the main advantages of the application.

关键词： Mobile dermatology image acquisition image quality assessment Feature extraction machine learning image segmentation

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Performance Evaluation of Recent Object Detection Models for Traffic Safety applications on Edge 23

Performance Evaluation of Recent Object Detection Models for...

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5th International Conference on image processing and machine vision, IPMV 2023

作者： Bulut, Anilcan Ozdemir, Fatmanur Bostanci, Yavuz Selim Soyturk, Mujdat VeNIT Lab Marmara University Turkey

ISBN: (纸本)9781450397926

Real-time objection detection is becoming more important and critical in all application areas, including Smart Transport and Smart City. From safety/security to resource efficiency, real-time image processing approaches are used more than ever. On the other hand, low-latency requirements and available resources present challenges. Edge computing integrated with cloud computing minimizes communication delays but requires efficient use of resources due to its limited resources. For example, although deep learning-based object detection methods give very accurate and reliable results, they require high computational power. This overhead reveals a need to implement deep learning models with less complex architectures for edge deployment. In this paper, the performance of evolving deep learning models with their lightweight versions such as YOLOv5-Nano, YOLOX-Nano, YOLOX-Tiny, YOLOv6-Nano, YOLOv6-Tiny, and YOLOv7-Tiny are evaluated on a commercially available edge device. The results show that YOLOv5-Nano and YOLOv6-Nano with their TensorRT versions can provide real-time applicability in approximately 35 milliseconds of inference time. It is also observed that YOLOv6-Tiny gives the highest average precision while YOLOv5-Nano gives the lowest energy consumption when compared to other models. © 2023 ACM.

关键词： Object detection

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The Relationship between vision and Simulated Remote vision System Air Refueling Performance 33

The Relationship between Vision and Simulated Remote Vision ...

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IS and T International Symposium on Electronic Imaging: 33rd Stereoscopic Displays and applications, SDA 2022

作者： O’Keefe, Eleanor Ankrom, Matthew Seemiller, Eric S. Bullock, Tommy Winterbottom, Marc Knapp, Jonelle Hadley, Steve KBR BeavercreekOH United States Airman Systems Directorate OBVA Laboratory Wright-Patterson AFB OH United States

United States Air Force (USAF) vision screening tests have remained largely unchanged since WWII and it is unclear whether current standards are applicable for users of new human-machine interfaces (e.g., stereoscopic remote vision system (RVS) in the KC-46 refueling tanker). This study examined the relationships between several vision tests, including a set of automated vision tests (AVT) developed by the Operational Based vision Assessment (OBVA) Laboratory, an electronic version of the standard Titmus stereo test, and the current Armed Forces vision Tester (AFVT), and simulated air refueling task performance using a stereoscopic RVS. Additionally, the relationships between vision test scores and subjective measures of visual fatigue were analyzed. Results showed that AVT measures of disparity discrimination and horizontal fusion correlated with simulated air refueling performance. AVT measures of acuity, contrast sensitivity, disparity discrimination, and radial motion sensitivity were significantly associated with subjective measures of discomfort and visual fatigue. Notably, neither the electronic Titmus results, nor the AFVT measures were associated with either air refueling task performance or subjective measures of visual fatigue. Adjustments to the vision standards and test methods used for USAF aeromedical vision screening should therefore be considered. © 2022 Society for Imaging Science and Technology. All rights reserved.

关键词： Stereo image processing

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