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International workshop on Frontiers of computer vision, IW-FCV 2020

International Workshop on Frontiers of Computer Vision, IW-F...

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International workshop on Frontiers of computer vision, IW-FCV 2020

ISBN: (纸本)9789811548178

The proceedings contain 27 papers. The special focus in this conference is on Frontiers of computer vision. The topics include: Identifying People Using Body Sway in Case of Self-occlusion;action Recognition in Sports Video Considering Location Information;adaptive Feature Selection Siamese Networks for Visual Tracking;Faster R-CNN with Attention Feature Map for Robust Object Detection;indoor Visual Re-localization based on Confidence Score Using Omni-Directional Camera;analysis of Information Flow in Hidden Layers of the Trained Neural Network by Canonical Correlation Analysis;Study of GANs Using a Few Images for Sealer Inspection Systems;Consistency Ensured Bi-directional GAN for Anomaly Detection;unsupervised Adversarial Learning for Dynamic Background modeling;early Wildfire Detection Using Convolutional Neural Network;transfer Learning by Cascaded Network to Identify and Classify Lung Nodules for Cancer Detection;hybrid Deep Learning and Data Augmentation for Disease Candidate Extraction;multispectral Photometric Stereo Using Intrinsic Image Decomposition;In-Plane Rotation-Aware Monocular Depth Estimation Using SLAM;uncalibrated Photometric Stereo Using Quadric Surfaces with Two Cameras;gaussian Processes for Efficient Plane-based Camera Calibration;leveraging Pyramidal Feature Hierarchy for 3D Reconstruction;inverse Lighting from Cast Shadows Under Unknown Radiometric Response Function;Deep Matting for AR based Interior Design;examination and Issues of Kumamoto Castle Ishigaki Region Extraction Focusing on Stone Contour Features;detection of Speech Impairments in Parkinson Disease Using Handcrafted Feature-based Model on Spanish Speech Corpus;short-Term Action Recognition by 3D Convolutional Neural Network with Pixel-Wise Evidences;discriminative Metric Learning with Convolutional Feature Descriptors for Age-Invariant Face Recognition and Verification;Dilated CNN based Human Verifier for Intrusion Detection.

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Development of Infrared Scene Model in a Wide Wavelength Range

Development of Infrared Scene Model in a Wide Wavelength Ran...

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Annual Siberian Russian workshop on Electron Devices and Materials (EDM)

作者： Alexey V. Zverev Dmitry E. Ipatov “Motiv NT” LLC Moscow Russia A.V. Rzhanov Institute of Semiconductor Physics SB RAS Novosibirsk Russia

At present, machine vision systems demand rapid visual sensors for an environmental analysis. However, modern frame-by-frame photodetectors can hardly provide satisfying characteristics to robotics. Although there are some photodetector design techniques, no software to simulate and analyze photodetector models at infrared (IR) wavelengths are available. In this paper we show the design of the 3D IR scene simulation software as well as the IR scene model using some 3D bodies with thermal radiation characteristics. A GPU-based Vulkan IR render is presented. It allows scene rendering in a wide range of IR wavelengths. An example of several IR scene simulations was also shown.

关键词： Training Solid modeling Visualization Three-dimensional displays Computational modeling vision sensors Rendering (computer graphics)

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Variable photorealistic image synthesis for training dataset generation 8

Variable photorealistic image synthesis for training dataset...

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8th International Scientific Conference on Computing in physics and Technology, CPT 2020

作者： Sanzharov, V.V. Frolov, V.A. Voloboy, A.G. Gubkin Russian State University of Oil and Gas Moscow Russia Moscow State University Moscow Russia Keldysh Institute of Applied Mathematics Moscow Russia

Photorealistic rendering systems have recently found new applications in artificial intelligence, specifically in computer vision for the purpose of generation of image and video sequence datasets. The problem associated with this application is producing large number of photorealistic images with high variability of 3d models and their appearance. In this work, we propose an approach based on combining existing procedural texture generation techniques and domain randomization to generate large number of highly variative digital assets during the rendering process. This eliminates the need for a large pre-existing database of digital assets (only a small set of 3d models is required), and generates objects with unique appearance during rendering stage, reducing the needed post-processing of images and storage requirements. Our approach uses procedural texturing and material substitution to rapidly produce large number of variations of digital assets. The proposed solution can be used to produce training datasets for artificial intelligence applications and can be combined with most of state-of-the-art methods of scene generation. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： computer vision

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Sketch2Code: Automatic hand-drawn UI elements detection with Faster R-CNN 11th

Sketch2Code: Automatic hand-drawn UI elements detection with...

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11th Conference and Labs of the Evaluation Forum, CLEF 2020

作者： Zita, Aleš Picek, Lukáš Říha, Antonín Czech Academy of Sciences Institute of Information Theory and Automation Czech Republic Faculty of Mathematics and Physics Charles University Czech Republic Dept. of Cybernetics Faculty of Applied Sciences University of West Bohemia Czech Republic Faculty of Information Technology Czech Technical University Czech Republic PiVa AI

Transcription of User Interface (UI) elements hand drawings to the computer code is a tedious and repetitive task. Therefore, a need arose to create a system capable of automating such process. This paper describes a deep learning-based method for hand-drawn user interface elements detection and localization. The proposed method scored 1st place in the ImageCLEFdrawnUI competition while achieving an overall precision of 0.9708. The final method is based on Faster R-CNN object detector framework with ResNet-50 backbone architecture trained with advanced regularization techniques. The code has been made available at: https://***/picekl/ImageCLEF2020-DrawnUI. Copyright © 2020 for this paper by its authors.

关键词： computer vision

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16th European Conference on computer vision, ECCV 2020

16th European Conference on Computer Vision, ECCV 2020

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16th European Conference on computer vision, ECCV 2020

ISBN: (纸本)9783030584511

The proceedings contain 27 papers. The special focus in this conference is on computer vision. The topics include: Extract and Merge: Superpixel Segmentation with Regional Attributes;spatial-Adaptive Network for Single Image Denoising;physics-based Feature Dehazing Networks;learning Surrogates via Deep Embedding;an Asymmetric modeling for Action Assessment;high-Quality Single-Model Deep Video Compression with Frame-Conv3D and Multi-frame Differential Modulation;instance-Aware Embedding for Point Cloud Instance Segmentation;self-Paced Deep Regression Forests with Consideration on Underrepresented Examples;manifold Projection for Adversarial Defense on Face Recognition;weakly Supervised Learning with Side Information for Noisy Labeled Images;structure-Aware Human-Action Generation;not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision;SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection;modeling the Space of Point Landmark Constrained Diffeomorphisms;pieNet: Personalized Image Enhancement Network;rotational Outlier Identification in Pose Graphs using Dual Decomposition;Speech-Driven Facial Animation Using Cascaded GANs for Learning of Motion and Texture;solving Phase Retrieval with a Learned Reference;dual Grid Net: Hand Mesh Vertex Regression from Single Depth Maps;towards Efficient Coarse-to-Fine Networks for Action and Gesture Recognition;S3 Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data;leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learning;weight Excitation: Built-in Attention Mechanisms in Convolutional Neural Networks;UNITER: UNiversal Image-TExt Representation Learning;oscar: Object-Semantics Aligned Pre-training for vision-Language Tasks;improving Face Recognition from Hard Samples via Distribution Distillation Loss.

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16th European Conference on computer vision, ECCV 2020

16th European Conference on Computer Vision, ECCV 2020

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16th European Conference on computer vision, ECCV 2020

ISBN: (纸本)9783030586201