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Information management and monitoring system for a grapes harvesting robot

IOP Conference Series: Materials science and Engineering 2021年第1期1032卷

作者： K Tziridis A Nikolaou T Kalampokas E Vrochidou T Pachidis G A Papakostas V G Kaburlasos Human-Machines Interaction Laboratory (HUMAIN-Lab) Department of Computer Science International Hellenic University Agios Loukas 65404 Kavala Greece

In recent years, there has been an increasing integration of high technology in the execution of agricultural tasks towards improving the quality of the produced products, while reducing production costs. In this context, there is a special interest in the development of automatic agricultural robots (Agrobots) with advanced techniques of collecting, processing data, decision making and applying actions. A key element of the successful integration of such autonomous robots is the information management and monitoring system (IM2S) that accompanies it. This work presents the IM2S developed for an autonomous grapes harvesting robot, as part of a national project. Details regarding the system's design are presented, covering system requirements, functional specifications, Graphical User Interface (GUI) design, database design and system-robot communication. Along with the presentation of the IM2S design, the special functional and technological requirements that must be met, are also discussed. Thus, the system could be a useful tool for the human operator. Moreover, the designed system can be easily adapted to collaborate with any robot meeting specific communication requirements. This makes the proposed IM2S a universal tool for any grape harvesting Agrobot already deploy or will be developed in the future.

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Assessment of multiple-biomarker classifiers: Fundamental principles and a proposed strategy

arXiv

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arXiv 2019年

作者： Yousef, Waleed A. Computer Science Department Faculty of Computers and Information Helwan University Egypt Human Computer Interaction Laboratory [HCI Lab. Egypt

The multiple-biomarker classifier problem and its assessment are reviewed against the background of some fundamental principles from the field of statistical pattern recognition, machine learning, or the recently so-called "data science". A narrow reading of that literature has led many authors to neglect the contribution to the total uncertainty of performance assessment from the finite training sample. Yet the latter is a fundamental indicator of the stability of a classifier;thus its neglect may be contributing to the problematic status of many studies. A three-level strategy is proposed for moving forward in this field. The lowest level is that of construction, where candidate features are selected and the choice of classifier architecture is made. At that point, the effective dimensionality of the classifier is estimated and used to size the next level of analysis, a pilot study on previously unseen cases. The total (training and testing) uncertainty resulting from the pilot study is, in turn, used to size the highest level of analysis, a pivotal study with a target level of uncertainty. Some resources available in the literature for implementing this approach are reviewed. Although the concepts explained in the present article may be fundamental and straightforward for many researchers in the machine learning community they are subtle for many practitioners, for whom we provided a general advice for the best practice in [1] and elaborate here in the present paper. Copyright © 2019, The Authors. All rights reserved.

关键词： Uncertainty analysis

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SketchGAN: Joint Sketch Completion and Recognition with Generative Adversarial Network

SketchGAN: Joint Sketch Completion and Recognition with Gene...

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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Fang Liu Xiaoming Deng Yu-Kun Lai Yong-Jin Liu Cuixia Ma Hongan Wang State Key Laboratory of Computer Science and Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Cardiff University Tsinghua University

ISBN: (纸本)9781728132945

Hand-drawn sketch recognition is a fundamental problem in computer vision, widely used in sketch-based image and video retrieval, editing, and reorganization. Previous methods often assume that a complete sketch is used as input;however, hand-drawn sketches in common application scenarios are often incomplete, which makes sketch recognition a challenging problem. In this paper, we propose SketchGAN, a new generative adversarial network (GAN) based approach that jointly completes and recognizes a sketch, boosting the performance of both tasks. Specifically, we use a cascade Encode-Decoder network to complete the input sketch in an iterative manner, and employ an auxiliary sketch recognition task to recognize the completed sketch. Experiments on the Sketchy database benchmark demonstrate that our joint learning approach achieves competitive sketch completion and recognition performance compared with the state-of-the-art methods. Further experiments using several sketch-based applications also validate the performance of our method.

关键词： Sketches images computer vision Recognition (Psychology) hand-drawn

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Generating Synthetic Medical Images by Using GAN to Improve CNN Performance in Skin Cancer Classification

Generating Synthetic Medical Images by Using GAN to Improve ...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Pooyan Sedigh Rasoul Sadeghian Mehdi Tale Masouleh Science and Research Branch Islamic Azad University Tehran Iran Human and Robot Interaction Laboratory Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9781728166049

ISBN: (纸本)9781728166056

One of the main reasons of slow progress in using deep learning methods for cancer detection is the lack of data, especially the annotated data which is usually used for supervised learning algorithms. This paper presents a Convolutional Neural Network (CNN) to detect skin cancer. The primary database which is used to train the designed CNN algorithm has 97 members (50 benign and 47 malignant), which are collected from the International Skin Imaging Collaboration (ISIC). In order to compensate the lack of data for training the proposed CNN algorithm, a Generative Adversarial Network (GAN) is designed to produce synthetic skin cancer images. The classification performance of the designed trained CNN without the obtained synthetic images is near 53%, but by adding the synthetic images to the primary database the performance of the model is increased to 71%.

关键词： Gallium nitride Generative adversarial networks Databases Skin cancer Medical diagnostic imaging

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Publisher Correction: Propagation of errors in citation networks: a study involving the entire citation network of a widely cited paper published in, and later retracted from, the journal Nature

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Research integrity and peer review 2024年第1期9卷 14页

作者： Paul E van der Vet Harm Nijveen Human Media Interaction Group Department of Computer Science University of Twente Drienerlolaan 5 7522 NB Enschede the Netherlands. ZGT Academy Ziekenhuisgroep Twente Zilvermeeuw 1 7609 PP Almelo the Netherlands. Bioinformatics Laboratory Wageningen University Droevendaalsesteeg 1 6708 PB Wageningen the Netherlands. harm.nijveen@wur.nl. Wageningen Seed Lab Laboratory of Plant Physiology Wageningen University Droevendaalsesteeg 1 6708 PB Wageningen the Netherlands. harm.nijveen@wur.nl.

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MS-net: Multi-site network for improving prostate segmentation with heterogeneous MRI data

arXiv

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arXiv 2020年

作者： Liu, Quande Dou, Qi Yu, Lequan Heng, Pheng Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Radiation Oncology Stanford University StanfordCA94305 United States Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Automated prostate segmentation in MRI is highly demanded for computer-assisted diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress in this task, usually relying on large amounts of training data. Due to the nature of scarcity for medical images, it is important to effectively aggregate data from multiple sites for robust model training, to alleviate the insufficiency of single-site samples. However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training. In this paper, we propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations, leveraging multiple sources of data. To compensate for the inter-site heterogeneity of different MRI datasets, we develop Domain-Specific Batch Normalization layers in the network backbone, enabling the network to estimate statistics and perform feature normalization for each site separately. Considering the difficulty of capturing the shared knowledge from multiple datasets, a novel learning paradigm, i.e., Multi-site-guided Knowledge Transfer, is proposed to enhance the kernels to extract more generic representations from multi-site data. Extensive experiments on three heterogeneous prostate MRI datasets demonstrate that our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning. Copyright © 2020, The Authors. All rights reserved.

关键词： Urology

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A labanotation based ontology for representing Vietnamese folk dances 3

A labanotation based ontology for representing Vietnamese fo...

引用

3rd International Conference on Digital Arts, Media and Technology, ICDAMT 2018

作者： Chau, Ma Thi Thuy, Nguyen Thanh Human Machine Interaction Laboratory University of Engineering and Technology Hanoi Viet Nam Computer Science Department University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9781509052097

This paper aims at designing and constructing an ontology, representing and allowing Vietnamese folk dances annotation. An effective approach proposed is based on movement analysis. It is possible to divide Vietnamese folk dances into basic units, then movement phrases, movement primitives and dance poses. Studying deeply Vietnamese folk dances features and characteristics allows to find out 5 different types of relationships between main dance components. Typical relationships could be written in the form of logical predicates. For enriching ontologies, a formal encoding of the ontology expertise knowledge in the domain is performed by DL-Lite. The paper uses OWL-2 for building, organizing and visualizing ontologies. A case study-Mo folk dance with a knowledge base, is structured simply but effectively, with reasoning in DL-Lite and answering queries in SPARQL. © 2018 IEEE.

关键词： Ontology

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What is that in your hand? Recognizing grasped objects via forearm electromyography sensing

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018年第4期2卷 1–24页

作者： Fan, Junjun Fan, Xiangmin Tian, Feng Li, Yang Liu, Zitao Sun, Wei Wang, Hongan State Key Laboratory of Computer Science Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Google Research and Machine Intelligence Mountain ViewCA United States TAL AI Lab Beijing China

Knowing the object in hand can offer essential contextual information revealing a user's fine-grained activities. In this paper, we investigate the feasibility, accuracy, and robustness of recognizing the uninstrumented object in a user's hand by sensing and decoding her forearm muscular activities via off-the-shelf electromyography (EMG) sensors. We present results from three studies to advance our fundamental understanding of the opportunities that EMG brings in object interaction recognition. In the first study, we investigated the influence of physical properties of objects such as shape, size, and weight on EMG signals. We also conducted a thorough exploration of the feature spaces and sensor positions which can provide a solid base to rely on for future designers and practitioners for such interactive technique. In the second study, we assessed the feasibility and accuracy of inferring the types of grasped objects via using forearm muscular activity as a cue. Our results indicate that the types of objects can be recognized with up to 94.2% accuracy by employing user-dependent training. In the third study, we investigated the robustness of this approach in a realistic office setting where users were allowed to interact with objects as they would naturally. Our approach achieved up to 82.5% accuracy in discriminating 15 types of objects, even when training and testing phrases were purposefully performed on different days to incorporate changes in EMG patterns over time. Overall, this work contributes a set of fundamental findings and guidelines on using EMG technologies for object-based activity tracking. © 2018 Association for Computing Machinery.

关键词： Object detection

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Modeling a target-selection motion by leveraging an optimal feedback control mechanism

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science China(Information sciences) 2018年第4期61卷 209-211页

作者： Jin HUANG Xiaolan PENG Feng TIAN Hongan WANG Guozhong DAI Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences

Target selection has always been a popular research topic in the human-computer interaction(HCI)*** with continuous interactive spaces,such as video games,augmented reality(AR),and virtual reality(VR),are becom-

关键词： Modeling a target-selection motion by leveraging an optimal feedback control mechanism

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Inception classification and object detection based joint-CNN for indoor scene classification

Inception classification and object detection based joint-CN...

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2018 International MultiConference of Engineers and computer Scientists, IMECS 2018

作者： Tian, Yarding Zhang, Weitong Zhang, Qieshi Lu, Gang School of Computer Science Shaanxi Normal University Xi'an China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9789881404886

While convolutional neural network (CNN) has been successfully used in many fields including single-label scene classification, it is vital to note that real world scenes generally contain multiple semantics and multi-label, especially in the indoor scene classification due to its content complexity. At the same time, most approaches try to make the network much deeper to make sure that they can extract more detail information. However, the deeper network will cause a lot of problems such as the increase of computational costs and network costs and so on. In order to solve these problems, this paper presents a novel framework which called Joint-CNN based on the proposed special label extraction and network structure. Extensive experiments on various data sets show that our method has enhanced the performance on MIT indoor67 and SUN397 data sets. © 2018 Newswood Limited. All rights reserved.

关键词： Object detection

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