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检索条件"任意字段=IEEE Workshop on Visualization and Machine Vision"

共 120 条记录，以下是31-40 订阅

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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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Test Automation with Grad-CAM Heatmaps - A Future Pipe Segment in MLOps for vision AI?

Test Automation with Grad-CAM Heatmaps - A Future Pipe Segme...

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ieee International Conference on Software Testing Verification and Validation workshop, ICSTW

作者： Markus Borg Ronald Jabangwe Simon Åberg Arvid Ekblom Ludwig Hedlund August Lidfeldt RISE Research Institutes of Sweden Lund University Lund Sweden

machine Learning (ML) is a fundamental part of modern perception systems. In the last decade, the performance of computer vision using trained deep neural networks has outperformed previous approaches based on careful feature engineering. However, the opaqueness of large ML models is a substantial impediment for critical applications such as in the automotive context. As a remedy, Gradient-weighted Class Activation Mapping (Grad-CAM) has been proposed to provide visual explanations of model internals. In this paper, we demonstrate how Grad-CAM heatmaps can be used to increase the explainability of an image recognition model trained for a pedestrian underpass. We argue how the heatmaps support compliance to the EU's seven key requirements for Trustworthy AI. Finally, we propose adding automated heatmap analysis as a pipe segment in an MLOps pipeline. We believe that such a building block can be used to automatically detect if a trained ML-model is activated based on invalid pixels in test images, suggesting biased models.

关键词： Heating systems Image segmentation visualization Automation Conferences Neural networks Pipelines

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IC-ChipNet: Deep Embedding Learning for Fine-grained Retrieval, Recognition, and Verification of Microelectronic Images

IC-ChipNet: Deep Embedding Learning for Fine-grained Retriev...

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Applied Imagery Pattern Recognition workshop (AIPR)

作者： Md Alimoor Reza David J. Crandall Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana

ISBN: (纸本)9781728182445

Modern electronic devices consist of a wide range of integrated circuits (ICs) from various manufacturers. Ensuring that an electronic device functions correctly requires verifying that its ICs and other component parts are correct and legitimate. Towards this goal, we investigate using machine learning and computer vision to identify and verify integrated circuit packages using visual features alone. We propose a deep metric learning approach to learn a feature embedding to capture important visual features of the external packages of ICs. We explore several variations of Siamese networks for this task, and learn an embedding using a joint loss function. To evaluate our approach, we collected and manually annotated a large dataset of 6,387 IC images, and tested our embedding on three challenging tasks: (1) fine-grained retrieval, (2) fine-grained IC recognition, and (3) verification. We believe this to be among the first papers targeting the novel application of fine-grained IC visual recognition and retrieval, and hope it establishes baselines to advance research in this area.

关键词： Integrated circuits Training Measurement visualization Computer vision Image recognition Target recognition

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Iterative and Adaptive Sampling with Spatial Attention for Black-Box Model Explanations

Iterative and Adaptive Sampling with Spatial Attention for B...

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ieee workshop on Applications of Computer vision (WACV)

作者： Bhavan Vasu Chengjiang Long Kitware Inc. Clifton Park NY USA

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

Deep neural networks have achieved great success in many real-world applications, yet it remains unclear and difficult to explain their decision-making process to an enduser. In this paper, we address the explainable AI problem for deep neural networks with our proposed framework, named IASSA, which generates an importance map indicating how salient each pixel is for the models prediction with an iterative and adaptive sampling module. We employ an affinity matrix calculated on multi-level deep learning features to explore long-range pixel-to-pixel correlation, which can shift the saliency values guided by our long-range and parameter-free spatial attention module. Extensive experiments on the MS-COCO dataset show that the proposed approach matches or exceeds the performance of state-of-the-art black-box explanation methods. Our source code is available at https://***/vbhavank/IASSA-Saliency.

关键词： Adaptation models Neural networks Feature extraction Mathematical model Decision making machine learning visualization

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See the Sound, Hear the Pixels

See the Sound, Hear the Pixels

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ieee workshop on Applications of Computer vision (WACV)

作者： Janani Ramaswamy Sukhendu Das Visualization and Perception Lab IIT Madras India

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

For every event occurring in the real world, most often a sound is associated with the corresponding visual scene. Humans possess an inherent ability to automatically map the audio content with visual scenes leading to an effortless and enhanced understanding of the underlying event. This triggers an interesting question: Can this natural correspondence between video and audio, which has been diminutively explored so far, be learned by a machine and modeled jointly to localize the sound source in a visual scene? In this paper, we propose a novel algorithm that addresses the problem of localizing sound source in unconstrained videos, which uses efficient fusion and attention mechanisms. Two novel blocks namely, Audio Visual Fusion Block (AVFB) and Segment-Wise Attention Block (SWAB) have been developed for this purpose. Quantitative and qualitative evaluations show that it is feasible to use the same algorithm with minor modifications to serve the purpose of sound localization using three different types of learning: supervised, weakly supervised and unsupervised. A novel Audio Visual Triplet Gram Matrix Loss (AVTGML) has been proposed as a loss function to learn the localization in an unsupervised way. Our empirical evaluations demonstrate a significant increase in performance over the existing state-of-the-art methods, serving as a testimony to the superiority of our proposed approach.

关键词： visualization Feature extraction Helicopters Task analysis Streaming media Fuses Gold

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Audio-Visual Model Distillation Using Acoustic Images

Audio-Visual Model Distillation Using Acoustic Images

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ieee workshop on Applications of Computer vision (WACV)

作者： Andrés F. Pérez Valentina Sanguineti Pietro Morerio Vittorio Murino Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

In this paper, we investigate how to learn rich and robust feature representations for audio classification from visual data and acoustic images, a novel audio data modality. Former models learn audio representations from raw signals or spectral data acquired by a single microphone, with remarkable results in classification and retrieval. However, such representations are not so robust towards variable environmental sound conditions. We tackle this drawback by exploiting a new multimodal labeled action recognition dataset acquired by a hybrid audio-visual sensor that provides RGB video, raw audio signals, and spatialized acoustic data, also known as acoustic images, where the visual and acoustic images are aligned in space and synchronized in time. Using this richer information, we train audio deep learning models in a teacher-student fashion. In particular, we distill knowledge into audio networks from both visual and acoustic image teachers. Our experiments suggest that the learned representations are more powerful and have better generalization capabilities than the features learned from models trained using just single-microphone audio data.

关键词： Acoustics visualization Data models Training Microphones machine learning Synchronization

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L*ReLU: Piece-wise Linear Activation Functions for Deep Fine-grained Visual Categorization

L*ReLU: Piece-wise Linear Activation Functions for Deep Fine...

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ieee workshop on Applications of Computer vision (WACV)

作者： Mina Basirat Peter M. Roth Institute of Computer Graphics and Vision Graz University of Technology

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

Deep neural networks paved the way for significant improvements in image visual categorization during the last years. However, even though the tasks are highly varying, differing in complexity and difficulty, existing solutions mostly build on the same architectural decisions. This also applies to the selection of activation functions (AFs), where most approaches build on Rectified Linear Units (Re- LUs). In this paper, however, we show that the choice of a proper AF has a significant impact on the classification accuracy, in particular, if fine, subtle details are of relevance. Therefore, we propose to model the degree of absence and the degree presence of features via the AF by using piecewise linear functions, which we refer to as L*ReLU. In this way, we can ensure the required properties, while still inheriting the benefits in terms of computational efficiency from ReLUs. We demonstrate our approach for the task of Fine-grained Visual Categorization (FGVC), running experiments on seven different benchmark datasets. The results do not only demonstrate superior results but also that for different tasks, having different characteristics, different AFs are selected.

关键词： Task analysis visualization Robustness Training Computational modeling Convergence machine learning

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Visual Illusion Induced by Uncertain Environment in Neural Dynamics Cognitive System

Visual Illusion Induced by Uncertain Environment in Neural D...

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ieee International workshop on Anti-counterfeiting, Security, Identification, ASID

作者： Yang You Niansheng Liu Xiangmin Zhou Jianwei Zheng Donghui Guo Xiamen University Xiamen China School of Computer Engineering Jimei University Xiamen China

ISBN: (数字)9781728168791

ISBN: (纸本)9781728168807

The visual signal processing mechanism of humans and mammals is precise and complex, better than most current machine vision systems. Visual illusion can show the cognitive functions and mechanisms of the visual system from a unique perspective which are of great significance to the development of computer vision. Firstly, the Gabor filter perfectly simulates the direction and spatial frequency selectivity of the visual cortex cells, thus successfully explaining why the Einstein-Monroe picture has two interpretations when viewing distance is changed and verifying the multi-scale characteristics of the visual neural network. Secondly, by means of nonlinear dynamics, the visual neural dynamics system based on the multi-scale background environment is established to reproduce optical illusions like Herman grid illusion. By understanding the mechanism of visual illusion, it can provide convenient experimental means for realizing the multi-modal information integration of multi-scale neural network and theoretical research of memory fusion model.

关键词： visualization Hafnium Gabor filters Filtering Dynamical systems

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“All Right, Mr. DeMille, I’m Ready for My Closeup:” Adding Meaning to User Actions from Video for Immersive Analytics

“All Right, Mr. DeMille, I’m Ready for My Closeup:” Addin...

引用

machine Learning from User Interaction for visualization and Analytics (MLUI), ieee workshop on

作者： Andrea Batch Niklas Elmqvist College of Information Studies University of Maryland College Park MD USA

While the use of machine learning and computer vision to classify human behavior has grown into a large, well-established, interdisciplinary area of research, one area that is somewhat overlooked is the intersection of computer vision as a tool for evaluating user behavior in Virtual Reality, particularly in the context of immersive analytics and visualization. We draw on the literature from pattern recognition, computer vision, and machine learning to compose a simple, comparatively resource-cheap pipeline for camera-based extraction of features of professional analyst users and of their sessions in an existing VR visualization system, ImAxes. Our results show high accuracy in predicting self-reported features of the users, even as survey responses about user experience with the immersive interface are somewhat ambiguous in varying based on these features.

关键词： Human computer interaction Computer vision Visual analytics Design methodology Pipelines Virtual reality Feature extraction

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Indoor Augmented Reality Using Deep Learning for Industry 4.0 Smart Factories 42

Indoor Augmented Reality Using Deep Learning for Industry 4....

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42nd Annual ieee-Computer-Society Computers, Software and Applications (COMPSAC) Conference - Staying Smarter in a Smartening World

作者： Subakti, Hanas Jiang, Jehn-Ruey Natl Cent Univ Dept Comp Sci & Elect Engn Taoyuan Taiwan

ISBN: (纸本)9781538626672

This paper proposes to design, develop and implement a fast and markerless mobile augmented reality system to achieve the registration for, the visualization of, and the interaction with machines in indoor smart factories with Industry 4.0 vision. A lightweight deep-learning image detection module based on MobileNets running on mobile devices is used to detect/recognize different machines and different portions of machines. Internet of Things (IoT) networking allows machines and sensors in machines to report data, such as machine settings and machine states, to the cloud-side server. Thus, augmented information associated with a machine portion can be derived from the server and superimposed with the portion image shown on the device display. Furthermore, interaction methods based on touch gestures and distance calculation are also implemented. A prototype system is developed and tested in a mechanical workshop for the purpose of validation and evaluation. The system is shown to achieve high detection accuracy, intuitive visualization, and unique interaction modes.

关键词： augmented reality deep learning indoor positioning Industry 4.0 Internet of Things smart factory

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