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4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018

作者： Zhang, Yinshu Chen, Jiayi Si, Xiangyu Tian, Zhiqiang Lan, Xuguang School of Software Engineering Xi’an Jiaotong University Xi’anShaanxi710049 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’anShaanxi710049 China

ISBN: (纸本)9789811379826

Style transfer is an increasingly popular field that can capture the styles of a particular artwork and use them to synthesize a new image with specific content. Previous NST algorithms have the limitation to transfer styles to correct regions in the output image. Therefore, some regions in the output image have deformed structures of the source image. In this paper, we propose a color preprocessing-based neural style transfer method to overcome the limitation. To reduce impacts caused by color differences between source image and style, we propose three models based on a color iterative distribution transform algorithm (IDT). The first one is named original color-preprocessed (OCp) model, which uses IDT to transform the color probability density function (PDF) of source image into that of style image. The second one is named exposure-corrected original color-preprocessed (EC-OCp) model, which adds an automatic detail-enhanced exposure correction module before OCp model. When source image is underexposed, EC-OCp model can achieve better results than OCp model. The third one is style color-preprocessed (SCp) model. It uses IDT to transform the color PDF of style image into that of source image. The original structures are well protected in the output image. According to experiments, the proposed models are robust to the source images with more conditions. Therefore, they have more usage values than the original method. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Color

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FDA: Feature decomposition and aggregation for robust airway segmentation

arXiv

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arXiv 2021年

作者： Zhang, Minghui Yu, Xin Zhang, Hanxiao Zheng, Hao Yu, Weihao Pan, Hong Cai, Xiangran Gu, Yun Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Department of Computer Science and Software Engineering Swinburne University of Technology VIC Australia Medical Image Centre The First Affiliated Hospital of Jinan University Guangzhou China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China

3D Convolutional Neural Networks (CNNs) have been widely adopted for airway segmentation. The performance of 3D CNNs is greatly influenced by the dataset while the public airway datasets are mainly clean CT scans with coarse annotation, thus difficult to be generalized to noisy CT scans (e.g. COVID-19 CT scans). In this work, we proposed a new dual-stream network to address the variability between the clean domain and noisy domain, which utilizes the clean CT scans and a small amount of labeled noisy CT scans for airway segmentation. We designed two different encoders to extract the transferable clean features and the unique noisy features separately, followed by two independent decoders. Further on, the transferable features are refined by the channel-wise feature recalibration and Signed Distance Map (SDM) regression. The feature recalibration module emphasizes critical features and the SDM pays more attention to the bronchi, which is beneficial to extracting the transferable topological features robust to the coarse labels. Extensive experimental results demonstrated the obvious improvement brought by our proposed method. Compared to other state-of-the-art transfer learning methods, our method accurately segmented more bronchi in the noisy CT scans. Copyright © 2021, The Authors. All rights reserved.

关键词： Computerized tomography

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A bottom-up paradigm for traffic scene graph representation 9

A bottom-up paradigm for traffic scene graph representation

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9th International Conference on Computing and Pattern Recognition, ICCPR 2020

作者： Zhang, Zhixuan Zhang, Chi Liu, Yuehu Su, Yuanqi Li, Ping Zheng, Jinzi School of Software Engineering Xi'An Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University Xi'an China Shaanxi Key Lab of Digital Technology and Intelligent Systems Xi'an China School of Computer Science and Technology Xi'An Jiaotong University Xi'an China China Academy of Railway Sciences Corporation Limited Beijing China

ISBN: (纸本)9781450387835

With increasing hardware computing power and model capacity, visual tasks for scene cognitive understanding have attracted more attention, such as visual relationships inference. The scene graph representation formed by a coupling of objects, attributes and relationships nodes displayed by different modalities of information, including original image, foreground things, background stuff and scene attributes, strongly promotes the progress of research area. In this paper, we address the scene graph representation of traffic scenarios for autonomous driving. It should be noted that the universal representation are the specific needs of cognitive understanding of traffic scenes: on the one hand, there is a lack of fine-grained description of key objects and attributes;on the other hand, there are redundant descriptions of objects and relationships. To tackle these problems, we take advantage of the fine-grained instance-level annotation of the traffic scene, proposing a bottom-up representation paradigm. It makes full use of the hierarchical structure of the traffic scene and the sparsity of element classes. In addition, on the basis of the existing methods, we optimize the relationship list of traffic scene graph representation. Moreover, we improve the scene graph annotation methods, proposing a "ground-vision joint location method"to better describe the spatially-distributed visual knowledge. The case analysis showed that compared with existing methods, our paradigm for scene graph can represent more abundant traffic scene information. © 2020 ACM.

关键词： Knowledge representation

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A C Code Generator for Fast Inference and Simple Deployment of Convolutional Neural Networks on Resource Constrained Systems

A C Code Generator for Fast Inference and Simple Deployment ...

引用

IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE International

作者： Oliver Urbann Simon Camphausen Arne Moos Ingmar Schwarz Sören Kerner Maximilian Otten Automation and Embedded Systems Fraunhofer IML Dortmund Germany Robotics Research Institute TU Dortmund University Dortmund Germany Software and Information Engineering Fraunhofer IML Dortmund Germany

ISBN: (数字)9781728196152

ISBN: (纸本)9781728196169

Inference of Convolutional Neural Networks in time critical applications usually requires a GPU. In robotics or embedded devices these are often not available due to energy, space and cost constraints. Furthermore, installation of a deep learning framework or even a native compiler on the target platform is not possible. This paper presents a neural network code generator (NNCG) that generates from a trained CNN a plain ANSI C code file that encapsulates the inference in single a function. It can easily be included in existing projects and due to lack of dependencies, cross compilation is usually possible. Additionally, the code generation is optimized based on the known trained CNN and target platform following four design principles. The system is evaluated utilizing small CNN designed for this application. Compared to TensorFlow XLA and Glow speed-ups of up to 11.81 can be shown and even GPUs are outperformed regarding latency.

关键词： Robots Artificial neural networks Machine learning Libraries Generators Embedded systems Optimization

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Pamls Alignment Based On Two-Stage Convolutional Network with a Large in-Plane Rotation

Pamls Alignment Based On Two-Stage Convolutional Network wit...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaoli Li Yang Yang Wentao Yang Guobin Zhang Wenting Cui Shaoyi Du School of Software Engineering Xi’an Jiaotong Unversity Xi’an China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Palms alignment is an important work for palmprint recognition in uncontrolled environment. Many methods have made progress to achieve alignment. But most of them ignore the palm's angles, which could not satisfy the alignment initialization when the hand has a large in-plane rotation. In this paper, we propose a palms alignment with affine transformation method based on a two-stage convolutional neural network (CNN). The basic idea is to rotate the target palm into the same angle category to avoid the following affine registration has a big matching error at the beginning. At the stage I, the given target palm is classified into two angle categories. At the stage II the upside down palm is firstly rotated 180 degrees, and then inputted into the subsequent feature extraction network, feature matching layer and regression network to achieve the affine alignment. Experimental results have proved the effectiveness of our method.

关键词： Palmprint recognition Conferences Neural networks Feature extraction Convolutional neural networks Cybernetics

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OBB-based 4D Manipulation of Traffic Sensory Data for Generating Corner Driving Scenarios

OBB-based 4D Manipulation of Traffic Sensory Data for Genera...

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2019 IEEE International Conference on Unmanned Systems, ICUS 2019

作者： Liu, Yu Zhang, Chi Li, Yaochen Li, Li Liu, Yuehu Zheng, Nanning Xi'An Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an China Xi'An Jiaotong University School of Software Engineering Xi'an China Tsinghua University Department of Automation Beijing China

ISBN: (纸本)9781728137926

To validate the intelligent vehicles' cognitive ability sufficiently and efficiently in an off-line test, sensor data of corner scenarios like dangerous driving are necessary and of great importance. However, such scenarios are not easy to be encountered and recorded due to their rare and unpredictable characteristics. In this paper, we propose to manipulate on the multi-sensor data temporarily and spatially, i.e. 4D manipulation, to generate these corner driving scenarios. In order to make manipulation more convenient and accurate, we introduce oriented bounding box (OBB) representing manipulation space. With the cross-modal consistency of existing data, we change contents of semantic images while manipulating contents of point cloud, and then new realistic image sequences of corner driving scenarios are generated with the help of image translation network. However, the newly generated images may exist wrong visual relationships because of the absence of road geometric constraints. To avoid this, in this paper, we propose a gradual construction of bounding box for regional road area method to add road geometric constraints. Finally, in experiments, our proposed 4D manipulation method is validated by generating corner driving scenarios. With the assistance of road geometric constraints, the visual effect of generated images is improved. Besides, experimental results prove that our generated corner driving scenarios impair the performance of the vehicle detection algorithm and are challenging for intelligent vehicle's cognitive ability, with the quality of generated scenarios nearly unchanged. © 2019 IEEE.

关键词： Intelligent vehicle highway systems

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Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside"

arXiv

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arXiv 2024年

作者： Santina, Cosimo Della Corbato, Carlos Hernandez Sisman, Burak Leiva, Luis A. Arapakis, Ioannis Vakalellis, Michalis Vanderdonckt, Jean D’Haro, LuisFernando Manzi, Guido Becchio, Cristina Elamrani, Aïda Alirezaei, Mohsen Castellano, Ginevra Dimarogonas, Dimos V. Ghosh, Arabinda Haesaert, Sofie Soudjani, Sadegh Stroeve, Sybert Verschure, Paul Bacciu, Davide Deroy, Ophelia Bahrami, Bahador Gallicchio, Claudio Hauert, Sabine Sanz, Ricardo Lanillos, Pablo Iacca, Giovanni Sigg, Stephan Gasulla, Manel Steels, Luc Sierra, Carles Department of Cognitive Robotics Delft University of Technology Delft Netherlands Oberpfaffenhofen Germany Department of Computer Science University of Luxembourg Luxembourg 4 Telefónica I+D Spain AEGIS IT Research GmbH Germany Université catholique de Louvain Belgium Speech Technology and Machine Learning Group ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid Spain Alien Venture Studio Alien Technology Transfer Rome Italy Hamburg Germany Institut Jean Nicod École Normale Supérieure Paris France Research Technology and Development Department Digital Industry Software Helmond Netherlands Department of Information Technology Uppsala University Uppsala Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Max Planck Institute for Software Systems Kaiserslautern Germany Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Amsterdam Netherlands Donders Centre for Neuroscience Radboud University Nijmegen Netherlands Computer Science Department University of Pisa Pisa Italy Faculty of Philosophy Philosophy of Science The Study of Religion Ludwig Maximilian University of Munich Munich Germany School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Autonomous Systems Laboratory Universidad Politécnica de Madrid Spain Madrid Spain Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Information and Communications Engineering Aalto University Espoo Finland Department of Electronic Engineering Universitat Politècnica de Catalunya Barcelona Spain Studio Stelluti Brussels Belgium Barcelona Spain

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices that are connected with projects funded by the EIC Pathfinder Challenge called "Awareness Inside", a nonrecurring call for proposals within Horizon Europe that was designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems. © 2024, CC BY.

关键词： robotics

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

引用

arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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Caption Generation from Road Images for Traffic Scene Construction

Caption Generation from Road Images for Traffic Scene Constr...

引用

IEEE Symposium on Intelligent Vehicle

作者： Chuan Wu Yaochen Li Ling Li Le Wang Yuehu Liu School of Software Engineering Xi'an Jiaotong University Xi'an China Xi'an Jiaotong University Suzhou Research School Suzhou China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

ISBN: (数字)9781728166735

ISBN: (纸本)9781728166742

In this paper, an image captioning network is proposed for traffic scene modeling, which incorporates element attention into the encoder-decoder mechanism to generate more reasonable scene captions. Firstly, the traffic scene elements are detected and segmented according to their clustered locations. Then, the image captioning network is applied to generate the corresponding caption of each subregion. The static and dynamic traffic elements are appropriately organized to construct a 3D corridor scene model. The semantic relationships between the traffic elements are specified according to the captions. The constructed 3D scene model can be utilized for the offline test of unmanned vehicles. The evaluations and comparisons based on the TSD-max and COCO datasets prove the effectiveness of the proposed framework.

关键词： Generators Semantics Roads Feature extraction Solid modeling Three-dimensional displays Image segmentation

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Capability-Based Process Modeling and Control

Capability-Based Process Modeling and Control

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Michael Mayrhofer Christoph Mayr-Dorn Ouijdane Guiza Georg Weichhart Alexander Egyed Cognitive Robotics and Shopfloors Pro2Future GmbH Linz Austria Institute of Software Systems Engineering Johannes Kepler University Linz Austria Flexible Production Systems ProFactor GmbH Steyr Austria

ISBN: (数字)9781728189567

ISBN: (纸本)9781728189574

Cyber physical production systems (CPPS) focus on increasing the flexibility and adaptability of industrial production systems, systems that comprise hardware such as sensors and actuators in machines as well as software controlling and integrating these machines. The requirements of customised mass production imply that control and integration software needs to be adaptable after deployment in a shop floor (factory), possibly even without interrupting production. Today, software frameworks provide support to model and execute manufacturing processes. They, however, provide little support for reuse. In this paper, we present a framework based on capabilities, which supports manufacturing process templates. These templates are bound/allocated to a specific shopfloor setup, a specific set of machines, and executed using a distributed set of process engines. This enables the reuse of manufacturing processes, as well as transmitting and executing changed processes. The framework is implemented using the Eclipse Milo implementation of OPC UA in Java. It is used to control a lab-scale modular shopfloor programmed in IEC61499 and Java.

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