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An Abstraction of Semantic Segmentation Algorithms 1

An Abstraction of Semantic Segmentation Algorithms

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1st International conference on Machine vision and Image Processing, MVIP 2020

作者： Teymoori, Reihaneh Nabizadeh, Zahra Karimi, Nader Samavi, Shadrokh Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran

ISBN: (纸本)9781728168326

semantic segmentation is a process of classifying each pixel in the image. Due to its advantages, semantic segmentation is used in many tasks like cancer detection, robot-assisted surgery, satellite images, self-driving car, etc. in this process, accuracy and efficiency are the two crucial goals for this purpose, there are several state-of-the-art neural networks. In each method, by employing different techniques, new solutions have been presented for increasing efficiency, accuracy, and saving the costs. The diversity of the implemented approaches for semantic segmentation makes it difficult for researchers to achieve a comprehensive view. Due to this, in this paper, an abstract framework for semantic segmentation is offered. This framework consists of 4 blocks that cover the majority of the methods that have been proposed for semantic segmentation. In this paper, we also attempt to compare different approaches and consider the importance of each part in semantic segmentation. Although our proposed framework considers most of the previous methods, maybe a few papers need new blocks. © 2020 IEEE.

关键词： Semantic Segmentation

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Recognition of Dandelion Weed via computer vision for a Weed Removal robot 1

Recognition of Dandelion Weed via Computer Vision for a Weed...

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1st International conference on Industrial Artificial Intelligence (IAI)

作者： Babiker, Ibrahim Xie, Wen-Fang Chen, Guangyi Concordia Univ Dept Mech Ind & Aerosp Engn Montreal PQ Canada

ISBN: (纸本)9781728135939

This paper introduces novel methods for distinguishing dandelion weed leaves from grass blades and identifying the centroids of dandelion weeds by means of a webcam. Firstly, a method of isolating the colour green using a Hue-Saturation-Value (HSV) scheme is proposed. Next, a technique for filtering the high frequency grass blades is developed to isolate individual low frequency of weed leaves. Subsequently, two novel methods: the lengthwise intersection method and mean centroid method are proposed to detect the location of the weed center based on the individual weed leaves. The detected location of the dandelion weed centroid has been used as the desired destination of autonomous driving system of a weed removal robot for successful dandelion removal.

关键词： Image color analysis Blades computer vision Thresholding (Imaging) Feature extraction

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An Autonomously Guided Differential Drive robot Base Using Asus® Xtion Pro Live 1

An Autonomously Guided Differential Drive Robot Base Using A...

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1st International conference on Image Processing and robotics (ICIP)

作者： Diddeniya, S. I. A. P. Liyanage, J. Wanniarachchi, W. K. I. L. Premachandra, C. Gunasinghe, H. N. Univ Sri Jayewardenepura Dept Phys Nugegoda Sri Lanka eVis Microsyst Pvt Ltd Panadura Sri Lanka Shibaura Inst Technol Dept Elect Engn Tokyo Japan Sabaragamuwa Univ Sri Lanka Dept Informat Syst Belihuloya Sri Lanka

ISBN: (纸本)9781728165417

robots have been popularized in the world during the past two decades and its development has overtaken many tasks performed by people, from households to the largest companies. Further, robotics has its applications in almost all industries and sectors. robots are available in many sizes, weights, and appearances based on their usage and applicability. In our study, we are going to introduce a novel robot base. We have developed the basic structure of the base using low-cost materials and open-source software packages. We used a 3D vision sensor for capturing the environment. Xtion Pro Live sensor by Asus was chosen for the purpose as it is lightweight, small and can be powered through a USB cable. We selected a Raspberry Pi computer as the processing unit of the base because of its lightweight and low cost. Further, it functions efficiently in an indoor environment using wheels. It is Pentagon shaped, 35 cm long and 30 cm wide. It can carry up to 5 kg in weight. We have tested the robot base in an unstructured indoor environment for goal completion using five locations. The robot's accuracy in achieving its targets is 92%. We have made a prototype of a novel robot base with the intention of developing it to append a robotic arm. The system will be able to mimic the tasks in a storage area, having the ability to add, remove and reposition the items in racks.

关键词： robot base Raspberry Pi Autonomous Navigation SLAM RGB-D Sensor ROS Asus Xtion Pro Live

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Design of educational robot platform based on graphic programming

Design of educational robot platform based on graphic progra...

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1st International conference on Big Data Analytics for Cyber-Physical System in Smart City, BDCPS 2019

作者： Ju, Aiping Chen, Deng Tang, Jianying School of Computer Science and Engineering Wuhan Institute of Technology East Lake New Technology Development Zone No. 206 Optics Valley First Road Wuhan China School of Artificial Intelligence Hubei Business College 632 Xiongchu Street Hongshan District Wuhan China

ISBN: (纸本)9789811525674

This paper designs an educational robot platform according to the needs of robot education for primary and middle school students. The mechanical mechanism design of the robot platform is carried out, and the environment sensing module based on multi-sensor information fusion is designed. The robot platform control is realized based on the ADRC control algorithm. The chassis control board uses the stM32 control board with the stM32F405RG microcontroller as the core. The board integrates MicroPython firmware, which enables access and control of the underlying hardware through the Python scripting language. The sensor modules such as the inertial navigation module and the vision module are connected with the Raspberry Pi development board. The raspberry PI development board conducts path planning according to multi-sensor information fusion, and sends control instructions to the chassis control board through the serial port. On this basis, the Python plug-in management system is designed to realize the graphical programming control robot. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Python

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Multi-view Visual Question Answering Dataset for Real Environment Applications 1

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1st International conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 22nd International conference on Human-computer Interaction (HCII)

作者： Qiu, Yue Satoh, Yutaka Suzuki, Ryota Iwata, Kenji Natl Inst Adv Ind Sci & Technol Tokyo Japan Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (数字)9783030503345

ISBN: (纸本)9783030503338;9783030503345

In this paper, we propose a novel large scale Visual Question Answering (VQA) dataset, which aims at real environment applications. Existing VQA datasets either require high constructing labor costs or have only limited power for evaluating complicated scene understanding ability involving in VQA tasks. Moreover, most VQA datasets do not tackle scenes containing object occlusion, which could be crucial for real-world applications. In this work, we propose a synthetic multi-view VQA dataset along with a dataset generation process. We build our dataset from three real object model datasets. Each scene is observed from multiple virtual cameras, which often requires a multi-view scene understanding. Our dataset requires relatively low labor cost and in the meantime, have highly complicated visual information. In addition, our dataset can be further adapted to users' requirements by extending the dataset setup. We evaluated two previous multi-view VQA methods on our datasets. The results show that both 3D understanding and appearance understanding is crucial to achieving high performance in our dataset, and there is still room for future methods to improve. Our dataset provides a possible way for bridging the VQA methods aiming at CG dataset with real-world applications, such as robot picking tasks.

关键词： AI Visual question answering 3D vision Deep learning

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Ultrastructure Analysis of Cardiomyocytes and Their Nuclei 12th

Ultrastructure Analysis of Cardiomyocytes and Their Nuclei

引用

Functional Imaging and Modeling of the Heart - 12th International conference, FIMH 2023, proceedings

作者： Syed, Tabish A. Wang, Yanan Dileep, Drisya Sirajuddin, Minhajuddin Siddiqi, Kaleem School of Computer Science McGill University Montréal Canada Institute for Stem Cell Science and Regenerative Medicine Bengaluru India Bengaluru India

ISBN: (纸本)9783031353017

Cardiomyocytes are elongated and densely packed in the mammalian heart and connected end on end to achieve a functional syncytium. Qualitative accounts describe their nuclei as being elongated in their long axis direction, which might help to better distribute mechanical load and reduce mechanical stress during contraction. Alterations of nuclear orientation and shape have also been known to be associated with certain cardiomyopathies. Yet, to date, the alignment of cardiomyocytes and their nuclei at the cellular (micron) scale has not been assessed in a quantitative fashion. To examine this we developed 3D computer vision methods to segment myocytes and their nuclei in cleared and membrane stained thick 3D tissue sections from a wild type mouse heart, imaged using confocal microscopy. We extended a geometric flow based superpixel algorithm to 3D and then adaptively merged the resulting supervoxels to recover individual myocytes. In parallel we also applied recent popular deep learning based cell segmentation methods to the same data. Our experiments revealed a close alignment of myocyte orientation with nucleus orientation, with a median difference of approximately 10 ∘, and also showed that most cardiomyocytes contain only one or two nuclei. These findings pave the way for future investigations of the effect of specific cardiac diseases on nuclear shape, elongation and number. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tissue

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Redundant persistent acyclic formations for vision-based control of distributed multi-agent formations 31

Redundant persistent acyclic formations for vision-based con...

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31st canadian conference on Computational Geometry, CCCG 2019

作者： Burns, Alyxander Klemperer, Peter Solyst, Jaemarie John, Audrey st. College of Information and Computer Sciences UMass Amherst United States Department of Computer Science and Engineering Mount Holyoke College United States Department of Computer Science Mount Holyoke College United States

We present theoretical and experimental results on the application of acyclic persistent leader-follower formations with redundancy to a distributed multi-agent system. A leader-follower formation is defined on a set of point agents constrained by fixed distance assignments for following other agents;if satisfying the constraints results in the distances between all pairs of agents being maintained, the formation is persistent. The (generic) persistence of a leader-follower formation in 2D is combinatorially characterized by a directed graph with one "leader" vertex having no out-edges, one "co-leader" vertex having exactly one out-edge (to the leader), all other "follower" vertices having out-degree at least 2, and an underlying minimally rigid (undirected) graph. We provide theoretical results for three types of persistent formations with redundancy, including an inductive construction for generating redundantly persistent graphs (the strongest notion of redundancy). We apply redundant persistence to multi-robot systems as a mechanism for incorporating robustness to sensing failure. In particular, we implement the approach on a vision-based distributed multi-robot platform. Using acyclic orientations permits a simple, "wave"-based control that converges reliably, and redundant edges allow the control to recover effectively from sensing limitations (e.g., a camera's limited field of view or obstruction by another robot). © CCCG 2019. All rights reserved.

关键词： Redundancy

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RoboFI: Autonomous Path Follower robot for Human Body Detection and Geolocalization for Search and Rescue Missions using computer vision and IoT 1

RoboFI: Autonomous Path Follower Robot for Human Body Detect...

引用

1st International conference on Advances in Science, Engineering and robotics Technology, ICASERT 2019

作者： Imteaj, Ahmed Isfar Jubair Chowdhury, M.A. Farshid, Mohammad Shahid, Abdur R Florida International University School of Computing and Information Sciences MiamiFL33199 United States International Islamic University Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9781728134451

Numerous loss of lives is seen each year and the sum is consistently expanding because of destruction, fiasco, cataclysm and so forth these incorporate natural and man-made fiascoes. To counter this we have structured a rescue robot that is autonomous, it pursues track way by dint of 3 ultrasonic sensors in the front. For the processing work Raspberry Pi Model B is utilized which incorporates a grouping of single board processors. The smaller card size is handful as we are utilizing 4wd Car Chassis and a motor controller to drive it through. The robot features computer vision and raspberry pi camera module is used to help with the vision. We trained the camera utilizing algorithm to empower it identify movement, consequently after detecting movement the robot utilizes PIR to detect and affirm human presence. When human life is recognized in a destructed place the web application helps us to observe the live streaming of that found human and the closest environment. The coordination of GPS Module gives us a chance to see the area of where rescue activity is occurring. The web application that is designed by us likewise gives us a chance to do as such. After rescuing the detected individual, it goes on its merry way in repeating the flow of work that is assigned to it helping achieve maximum amount of success and lives saved. The reality to stun is, all these are incorporated in a solitary car chassis and the expense behind the work is insignificant. The reason is utilizing raspberry pi and our work to make a pi camera module ready to recognize movement by help of raspberry pi while incredible and expensive processors are required by and large. © 2019 IEEE.

关键词： Collision avoidance

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Implementation of Internet Connected Industrial robot Using Hog Algorithm based Object Recognition 1

Implementation of Internet Connected Industrial Robot Using ...

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1st International conference on Frontiers in Materials and Smart System Technologies, FMSst 2019

作者： George, Anupriya Felix Joseph, X. ECE Department NICHE TN India Bule Hora University Ethiopia

The main focus is on object searching and recognition which is a popular area which increases tremendously in industries and research labs. The proposed work is based searching of an object which is done with the help of computer vision techniques. But in real time object searching will have many difficulties when the recognizing of an object is been done. To overcome this problem there is an improved technique called HOG Algorithm is been used in the real time situations. As the result the performance of the object searchingin the present experimental scenario is evaluated through the integrated hardware, which is synchronized with a camera and embedded system based wheeled robot. © Published under licence by IOP Publishing Ltd.

关键词： Cameras

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Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3 1

Car Detection using Unmanned Aerial Vehicles: Comparison bet...

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1st International conference on Unmanned Vehicle Systems-Oman (UVS)

作者： Benjdira, Bilel Khursheed, Taha Koubaa, Anis Ammar, Adel Ouni, Kais Prince Sultan Univ Riyadh Saudi Arabia Gaitech Robot Shanghai Peoples R China Polytech Inst Porto INESC CISTER TECISEP Porto Portugal Al Imam Mohamed bin Saud Univ Riyadh Saudi Arabia Univ Carthage Res Lab Smart Elect Tunis Tunisia Univ Carthage Natl Engn Sch Carthage ICT SEICTLR18ES44 Tunis Tunisia

ISBN: (纸本)9781538693681

Unmanned Aerial Vehicles are increasingly being used in surveillance and traffic monitoring thanks to their high mobility and ability to cover areas at different altitudes and locations. One of the major challenges is to use aerial images to accurately detect cars and count-them in real-time for traffic monitoring purposes. Several deep learning techniques were recently proposed based on convolution neural network (CNN) for real-time classification and recognition in computer vision. However, their performance depends on the scenarios where they are used. In this paper, we investigate the performance of two state-of-the art CNN algorithms, namely Faster R-CNN and YOLOv3, in the context of car detection from aerial images. We trained and tested these two models on a large car dataset taken from UAVs. We demonstrated in this paper that YOLOv3 outperforms Faster R-CNN in sensitivity and processing time, although they are comparable in the precision metric.

关键词： Car detection convolutional neural networks You Only Look Once Faster R-CNN unmanned aerial vehicles object detection and recognition

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