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检索条件"任意字段=International Conference on Pattern Analysis and Intelligence Robotics"

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VCA-Net: Visual Creativity analysis using Deep Neural Network✱ 22

VCA-Net: Visual Creativity Analysis using Deep Neural Networ...

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Proceedings of the Thirteenth Indian conference on Computer Vision, Graphics and Image Processing

作者： Darshil Shah Gopika Gopan K Neelam Sinha International Institute of Information Technology (IIITB) India

ISBN: (纸本)9781450398220

Electrical signals generated in the brain, known as Electroencephalographic signals (EEG) are used to measure electrical activities in the brain. EEG methods being non-invasive, affordable, and portable, are used in this work. analysis of Visual Creativity has clinical application in monitoring the progress of patients suffering from neurodegenerative diseases. In this work, EEG is used to analyze Visual Creativity by classifying the two brain states (1) Creative and (2) Non-creative using the in-house collected data. Data is collected from the participants performing the following tasks: (1) Planning to draw, (2) Drawing, and (3) Monotonous Tasks. Drawing and Monotonous Tasks are considered for the classification in the proposed work. Two types of analysis are carried out (1) converting EEG time-series data into a sequence of topology preserving feature maps and (2) feature maps generated using Spectral Entropy. The EEG data is first pre-processed to down-sample and band-limit the signals followed by ICA to remove the artifact. The artifact-removed EEG data is decomposed into three sub-bands. The decomposed time-series data is converted to a sequence of feature maps that preserves spatial, spectral, and temporal information. Another set of feature maps is also generated using Spectral Entropy from artifact-removed EEG data. Two types of Deep Learning frameworks are proposed (1) VCA-Net video - a deep learning framework consisting of temporal convolution that extracts spatial information and preserves temporal information which is extracted by a combination of LSTM and 1D convolution and (2) VCA-Net image - a deep learning framework which extracts the spatial information using Feature Pyramid Network (FPN) and Atrous Spatial Pyramid Pooling (ASPP). Drawing and Monotonous tasks are classified with a mean accuracy of 70.87% and 65.62% using VCA-Net image and VCA-Net video respectively.

关键词： Classification

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4th international conference on Innovative Technologies and Learning, ICITL 2021

4th International Conference on Innovative Technologies and ...

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4th international conference on Innovative Technologies and Learning, ICITL 2021

ISBN: (纸本)9783030915391

The proceedings contain 61 papers. The special focus in this conference is on Innovative Technologies and Learning. The topics include: A Study of Learner’s Scientific Thinking Using Constructivist Virtual Learning Environment for Grade 11 Students;Regarding the Virtual Reality Environment Design and Evaluation Based on STEAM Learning;Using STEAM-6E Model in AR/VR Maker Education Teaching Activities to Improve High School Students’ Learning Motivation and Learning Activity Satisfaction;The Use of E-learning Tools in a Basic Logic Course During the COVID-19 Lockdown;improve University Humanities Students’ Problem-Solving Ability Through Computational Thinking Training;the Development of a Computational Thinking Learning Package that Integrates a Learning Experience Design for Grade K;development of Online Learning to Enhancing Computational Thinking for High School Students;exploring the Usability of Web-Based Java Compiler as a Learning Tool;young Kids’ Basic Computational Thinking: An analysis on Educational robotics Without Computer;Amending Dynamic Capability Theory for Information Systems Research on the Reskilling of Coal Miners in an AI-Driven Era;the Validation of Constructivist Web-Based Learning Environment Model to Enhance Creativity Thinking for Undergraduate Student with Integration of Pedagogy and Neuroscience;resource Designing Framework of Constructivist Web-Based Learning Environment to Enhance the Problem-Solving for Robot Programming in Secondary Grade 3;AHP4Edu: An AHP-Based Assessment Model for Learning Effectiveness of Education;a Designing Framework for Flipped Classroom Learning Environment Model Combined with Augmented Reality to Enhance Creative Thinking in Product Design for High School Students;Using Design patterns to Teach Conceptual Entity Relationship (ER) Data Modelling;digital Application Literacy.

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Network training method based on Parallel Vision theory for object detection in battlefield environment 1

Network training method based on Parallel Vision theory for ...

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1st IEEE international conference on Digital Twins and Parallel intelligence, DTPI 2021

作者： Yuan, Xingsheng Yu, Penglu School of Intelligence Science and Technology National University of Defense Technology Changsha China School of Education for Basic Military National University of Defense Technology Changsha China

ISBN: (纸本)9781665433372

Visual perception and understanding in complex battlefield environments have few data samples, which cannot meet the need of deep network model training in variable and complex environments. In this paper, based on parallel vision theory, deep learning network training for object detection is conducted using artificial virtual battlefield environment. Virtual battlefield environment data are collected for experimental training and validation analysis, and further training tests are conducted in real scene data. The experiments verified that the deep network model training with virtual-real interaction can effectively solve the data deficient problem of object detection in the battlefield. © 2021 IEEE.

关键词： Object detection

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Nonlinear Control for Dual-Rope Aerial Transportation System by Tilt-Rotor

Nonlinear Control for Dual-Rope Aerial Transportation System...

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IEEE international conference on robotics and Biomimetics

作者： Yang Wang Hai Yu Xiao Liang Jianda Han Yongchun Fang Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (纸本)9781665405362

With high maneuverability and strong flexibility, unmanned aerial vehicles (UAVs) have been widely used, especially in the field of transporting payload. However, existing studies mainly study the transportation mode of connecting the payload with a single rope. In order to transport bulky payload while increase the transportation efficiency and guarantee safety, this paper focus on dual-rope tilt-rotor transportation system. The dynamic model is established through Lagrange’s modeling technique and a nonlinear control method is further proposed, which incorporates more swing-related information into the control law. Subsequently, the stability of the closed-loop system is proved by utilizing LaSalle’s invariance principle. Finally, the simulation results are provided.

关键词： Simulation conferences Transportation Stability analysis Safety Nonlinear dynamical systems Closed loop systems

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Low-light image enhancement model based on self-calibration illumination and color adjustment

Low-light image enhancement model based on self-calibration ...

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Algorithms, High Performance Computing and Artificial intelligence (AHPCAI), international conference on

作者： Guiwen Zhang Qishen Li Qiufeng Li Taida Wu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

to improve the clarity of objects in a dark-light environment, and to facilitate the identification and detection of targets behind. People perceive the color and brightness of a point not only depending on the pixel value of the point but also the absolute light entering the human eye is related to the color and brightness around the point. The self-calibration model we used considers the surrounding information, which avoids the information pollution, such as large regions of dark background information. In this model, we introduce a heterogeneous convolution filter, which makes reasonable use of different parts of the filter. Through this operation, information from multiple different scale spaces can be fused, and the field of view when applying the convolution layer is greatly increased without increasing the hyper-parameters, thus producing a more distinctive feature representation. Then the self-calibration model is combined with the backbone reinforcement network, which can not only retain the information of the original scale space but also efficiently collect the latent space information to guide the feature transformation in the original space. After the different channels of the image are processed separately, the dependency between the channels can be established by using the heterogeneous convolution filter. Finally, the test on the ExDark data set proves that our dark target enhancement effect has been significantly improved.

关键词： Training Pollution Image color analysis Convolution Brightness Lighting Information filters

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Design and analysis of a Yoshimura Continuum Actuator

Design and Analysis of a Yoshimura Continuum Actuator

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international conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Jingyu Zhang Jie Chen Yu Dang Jianda Han College of Artificial Intelligence Nankai University Tianjin China College of Artificial Intelligence Shenzhen Research Institute of Nankai University Nankai University Tianjin Shenzhen China

ISBN: (纸本)9798350334340

Origami has been applied in robotic design thanks to its lightweight structures and versatile applications. In this paper, we propose and analyze a novel cable-driven Yoshimura continuum actuator. The origami module inspired by the Yoshimura pattern can bend and contract under the actuation of the cable. In addition, we analyze the kinematics of the Yoshimura continuum actuator using the assumption of constant curvature. Based on the kinematic model, a scheme of closed-loop control is designed and validated using the motion capture system. The results show that the kinematic model can predict the tip position of the actuator with the error of 2.14 mm and 3.51mm when following a spiral and a square trajectory. The Yoshimura continuum actuator can track spiral and square trajectories with an error of 1.92 mm and 2.93 mm by the aid of the control method, demonstrating reductions of 10.3% and 16.5% for the average tracking errors. It demonstrates the Yoshimura continuum actuator has the potential to achieve versatile movement.

关键词： Actuators Spirals Mechatronics Trajectory tracking Machine vision Prototypes Motion capture

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Mechanical intelligence for Adaptive Precision Grasp

Mechanical Intelligence for Adaptive Precision Grasp

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IEEE international conference on robotics and Automation (ICRA)

作者： Qiujie Lu Nicholas Baron Guochao Bai Nicolas Rojas REDS Lab Dyson School of Design Engineering Imperial College London London UK Wangjing Science & Technology Innovation Park ChangingTek Ltd Chaoyang District Beijing China

Mechanical intelligence is the use of mechanical and other physical properties to create robotic systems adaptable to new external situations using simple control schemes. Designs of robot hands have successfully been developed and optimised following this principle to produce self-adaptive and versatile power grasps via implementations based on underactuated fingers, elastic components, and open-loop motor control. However, these characteristics, and mechanical-intelligent strategies in general, have been seldom leveraged for precision grasping. This paper proposes a mechanical-intelligent technique to facilitate not only spiral caging power grasp, but also self-adaptive precision grasp with error tolerance. This approach is exemplified by the rigorous analysis, development, and testing of a novel three-fingered, two-actuator, underactuated robot hand, called the helical hand, which is capable of self-adaptive precision grasping, and of generating spiral helical power grasps of unknown objects by simply setting two actuators at a constant speed.

关键词： Motor drives Actuators Spirals Automation conferences Grasping Mechanical factors

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Artifactual Affordances in Playful robotics 9th

Artifactual Affordances in Playful Robotics

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9th international conference on Games and Learning Alliance, GALA 2020

作者： Kalmpourtzis, George Romero, Margarida Laboratoire d’Innovation et Numérique pour l’Education Université Côte d’Azur Nice France

ISBN: (纸本)9783030634636

Programmable toys present interesting tools for the creation and facilitation of learning experiences through gaming and robotics. In order though for educators and game designers to use these tools to their full potential and provide pedagogical opportunities to support problem-solving activities, we should identify and characterise artifactual affordances in playful robotics. In problem-solving activities, supported by educational robotics artifacts, learners need to identify the artifactual affordances through their interaction with the artifact. This study aims to identify artifactual affordances through the analysis of 15 participants’ engagement in the CreaCube task. In this playful problem-solving activity, based on the use of modular robotics, players need to manipulate and assemble robotic cubes in order to create independently moving vehicles. This study aims at providing an initial analysis for the study of artifactual affordances in playful educational robotics. © 2020, Springer Nature Switzerland AG.

关键词： Human computer interaction

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Improving Lens Flare Removal with General-Purpose Pipeline and Multiple Light Sources Recovery

Improving Lens Flare Removal with General-Purpose Pipeline a...

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international conference on Computer Vision (ICCV)

作者： Yuyan Zhou Dong Liang Songcan Chen Sheng-Jun Huang Shuo Yang Chongyi Li MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Imaging Technology Group DJI Innovations Co. Ltd. Shanghai China School of Computer Science Nankai University Tianjin China

When taking images against strong light sources, the resulting images often contain heterogeneous flare artifacts. These artifacts can importantly affect image visual quality and downstream computer vision tasks. While collecting real data pairs of flare-corrupted/flare-free images for training flare removal models is challenging, current methods utilize the direct-add approach to synthesize data. However, these methods do not consider automatic exposure and tone mapping in image signal processing pipeline (ISP), leading to the limited generalization capability of deep models training using such data. Besides, existing methods struggle to handle multiple light sources due to the different sizes, shapes and illuminance of various light sources. In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy. The new pipeline approaches realistic imaging by discriminating the local and global illumination through convex combination, avoiding global illumination shifting and local over-saturation. Our strategy for recovering multiple light sources convexly averages the input and output of the neural network based on illuminance levels, thereby avoiding the need for a hard threshold in identifying light sources. We also contribute a new flare removal testing dataset containing the flare-corrupted images captured by ten types of consumer electronics. The dataset facilitates the verification of the generalization capability of flare removal methods. Extensive experiments show that our solution can effectively improve the performance of lens flare removal and push the frontier toward more general situations.

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Data analytics for traffic flow prediction in Custom using Long Short Term Memory (LSTM) networks 7

Data analytics for traffic flow prediction in Custom using L...

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7th international conference on Man Machine Systems, ICoMMS 2021

作者： Loon, Lee Pin Refaie, Elbaraa Faudzi, Ahmad Athif Mohd School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Centre for Artificial Intelligence and Robotics Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia

This paper proposes data analysis for traffic flow prediction of customs to help the officer in Customs, Immigration, and Quarantine (CIQ) Complex to understand more about the traffic situation in CIQ. Currently in CIQ, the traffic behaviour for car is unpredictable;sometimes the traffic is very heavy while there are times where all the lanes are cleared. There is a plan to have installation of cameras for smart traffic management system in the future. Therefore, this research aims to have prediction of traffic flow based on time and visualize the trend of traffic data for the officer. The data consist of traffic flow and the respective timestamp. To analyse it with time-series data, Long Short-Term Memory (LSTM) Recurrent Network is used as deep learning approach for prediction. The data pre-processing and training of model would be done using Python. To organize the data, Tableau Prep Builder is used and integrate with Python to publish the data to Tableau Server for storage. An interactive dashboard would be designed on Tableau and made available online for the usage of the officer. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Python

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