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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

共 52944 条记录，以下是4461-4470 订阅

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Integrating IoT Module for Real-time Finger Vein Detection: A Novel Approach

Integrating IoT Module for Real-time Finger Vein Detection: ...

引用

2024 Asia Pacific conference on Innovation in Technology, APCIT 2024

作者： Samuel, Roxanna Deepika, T. Abinav, P. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350361537

A biometric detection system named finger vein recognition studies the vein patterns in human fingers that are concealed under the skin's surface through pattern recognition algorithms. It involves comparing the vascular pattern of a person's finger with previously collected data. This technology is currently being applied and developed in various applications, including credit card authentication, vehicle security, employee time and attendance tracking, computer and network authentication, endpoint security, and automated teller machines. The article presents a novel approach to segmenting finger veins in real-time using an embedded terminal technique. Due to the limitations of current finger vein segmentation networks, which are too large and impractical for mobile terminals, as well as the time-consuming nature of deep networks on hardware platforms, the segmentation index decreases when the lightweight network parameters are reduced. © 2024 ieee.

关键词： Authentication

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StylePeople: A Generative Model of Fullbody Human Avatars

StylePeople: A Generative Model of Fullbody Human Avatars

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Grigorev, Artur Iskakov, Karim Ianina, Anastasia Bashirov, Renat Zakharkin, Ilya Vakhitov, Alexander Lempitsky, Victor Samsung AI Ctr Moscow Russia Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (纸本)9781665445092

We propose a new type of full-body human avatars, which combines parametric mesh-based body model with a neural texture. We show that with the help of neural textures, such avatars can successfully model clothing and hair, which usually poses a problem for mesh-based approaches. We also show how these avatars can be created from multiple frames of a video using backpropagation. We then propose a generative model for such avatars that can be trained from datasets of images and videos of people. The generative model allows us to sample random avatars as well as to create dressed avatars of people from one or few images. The code for the project is available at ***/style-people.

关键词： Hair Geometry computer vision Avatars Computational modeling Clothing Video sequences

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ALPS: Adaptive Quantization of Deep Neural Networks with GeneraLized PositS

ALPS: Adaptive Quantization of Deep Neural Networks with Gen...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Langroudi, Hamed F. Karia, Vedant Carmichael, Zachariah Zyarah, Abdullah Pandit, Tej Gustafson, John L. Kudithipudi, Dhireesha Univ Texas San Antonio Neuromorph AI Lab San Antonio TX 78249 USA Rochester Inst Technol Rochester NY 14623 USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665448994

In this paper, a new adaptive quantization algorithm for generalized posit format is presented, to optimally represent the dynamic range and distribution of deep neural network parameters. Adaptation is achieved by minimizing the intra-layer posit quantization error with a compander. The efficacy of the proposed quantization algorithm is studied within a new low-precision framework, ALPS, on ResNet-50 and EfficientNet models for classification tasks. Results assert that the accuracy and energy dissipation of low-precision DNNs using generalized posits outperform other well-known numerical formats, including standard posits.

关键词： Deep learning Quantization (signal) Adaptive systems Upper bound Numerical analysis Heuristic algorithms pattern recognition

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Touchless User Interface for Sketching Using Hand Gesture recognition 2

Touchless User Interface for Sketching Using Hand Gesture Re...

引用

2nd ieee International conference on Futuristic Technologies, INCOFT 2023

作者： Babu, Tharunaa Shoban Fathima, G. Shubhakar, B.C. Kumaresan, Bhuvanesh Adhiyamaan College of Engineering Department of Computer Science and Engineering Hosur India Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram India

ISBN: (纸本)9798350308846

Artificial intelligence (AI) technologies have a significant impact on developments in research and creative processes. With the quick development of computer vision, the extensive need for interaction with machines and humans is growing. Hand gesture recognition is often used in intelligent furniture, robot control and other meaningful use gestures using hand can be represented to give meaningful data or information. With current development in computing, the traditional writing and teaching methods are insufficient because it still uses mouse, keyboard, and touch pens. This paper proposes a solution by creating a Touchless User Interface (TUI). TUI uses hand gestures to sketch on the air without using the keyboard or mouse which is captured and reflected on the screen. The app developed for the same consists of tools such as circles, rectangles, lines, freehand drawing, and eraser which can be selected and drawn in the air, the system captures the respective shape or structure and reflects it on the screen. Human hands may be split from a complex background and identified in real time which is done using convolutional neural network. Thus, touchless user interface is developed with experimental accuracy of 98.48%. © 2023 ieee.

关键词： Artificial Intelligence computer vision Hand gesture recognition Touchless User Interface

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FrameExit: Conditional Early Exiting for Efficient Video recognition

FrameExit: Conditional Early Exiting for Efficient Video Rec...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ghodrati, Amir Bejnordi, Babak Ehteshami Habibian, Amirhossein Qualcomm AI Res Nijmegen Netherlands

ISBN: (纸本)9781665445092

In this paper, we propose a conditional early exiting framework for efficient video recognition. While existing works focus on selecting a subset of salient frames to reduce the computation costs, we propose to use a simple sampling strategy combined with conditional early exiting to enable efficient recognition. Our model automatically learns to process fewer frames for simpler videos and more frames for complex ones. To achieve this, we employ a cascade of gating modules to automatically determine the earliest point in processing where an inference is sufficiently reliable. We generate on-the-fly supervision signals to the gates to provide a dynamic trade-off between accuracy and computational cost. Our proposed model outperforms competing methods on three large-scale video benchmarks. In particular, on ActivityNet1.3 and mini-kinetics, we outperform the state-of-the-art efficient video recognition methods with 1.3x and 2.1x less GFLOPs, respectively. Additionally, our method sets a new state of the art for efficient video understanding on the HVU benchmark.

关键词： computer vision Costs Computational modeling Logic gates Benchmark testing Network architecture pattern recognition

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Specular Streaks in Stereo 26

Specular Streaks in Stereo

引用

26th International conference on pattern recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Langer, Michael S. Bourque, David McGill Univ Sch Comp Sci Montreal PQ Canada

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

When a compact light source illuminates a horizontal shiny ground plane at an oblique angle, the resulting highlight is vertically oriented and highly elongated. We refer to such highlights as specular streaks. Specular streaks occur commonly on wet roadways, especially at night, for example the reflections of street lamps or car headlights. Here we present a 3D model of specular streaks seen by a binocular observer. We show that specular streaks produce binocular disparities that are consistent with 3D near-vertical columns of light beneath the roadway. We validate this model with results on both synthetic and real images. Specular streaks are an important problem for vision-in-badweather, in particular, for autonomous driving systems or driver assistance systems that rely on stereo to estimate scene depth.

关键词： computer vision Solid modeling Three-dimensional displays Machine vision Snow Observers Reflection

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Sample-free white-box out-of-distribution detection for deep learning

Sample-free white-box out-of-distribution detection for deep...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Begon, Jean-Michel Geurts, Pierre Univ Liege Liege Belgium

ISBN: (纸本)9781665448994

Being able to detect irrelevant test examples with respect to deployed deep learning models is paramount to properly and safely using them. In this paper, we address the problem of rejecting such out-of-distribution (OOD) samples in a fully sample-free way, i.e., without requiring any access to in-distribution or OOD samples. We propose several indicators which can be computed alongside the prediction with little additional cost, assuming white-box access to the network. These indicators prove useful, stable and complementary for OOD detection on frequently-used architectures. We also introduce a surprisingly simple, yet effective summary OOD indicator. This indicator is shown to perform well across several networks and datasets and can furthermore be easily tuned as soon as samples become available. Lastly, we discuss how to exploit this summary in real-world settings.

关键词： Deep learning computer vision Filtering Computational modeling conferences computer architecture Data models

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Cross-Domain Multi-task Learning for Object Detection and Saliency Estimation

Cross-Domain Multi-task Learning for Object Detection and Sa...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Khattar, Apoorv Hegde, Srinidhi Hebbalaguppe, Ramya TCS Res Mumbai Maharashtra India

ISBN: (纸本)9781665448994

Multi-task learning (MTL) is a learning paradigm that aims at joint optimization of multiple tasks using a single neural network for better performance and generalization. In practice, MTL rests on the inherent assumption of availability of common datasets with ground truth labels for each of the downstream tasks. However, collecting such a common annotated dataset is laborious for complex computer vision tasks such as the saliency estimation which would require the eye fixation points as the ground truth data. To this end, we propose a novel MTL framework in the absence of common annotated dataset for joint estimation of important downstream tasks in computer vision - object detection and saliency estimation. Unlike many state-of-the-art methods, that rely on common annotated datasets for training, we consider the annotations from different datasets for jointly training different tasks, calling this setting as cross-domain MTL. We adapt MUTAN [3] framework to fuse features from different datasets to learn domain invariant features capturing the relatedness of different tasks. We demonstrate the improvement in the performance and generalizability of our MTL architecture. We also show that the proposed MTL network offers a 13% reduction in memory footprint due to parameter sharing between the related tasks.

关键词： Training computer vision Neural networks Estimation Object detection computer architecture pattern recognition

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Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency

Self-Supervised Video GANs: Learning for Appearance Consiste...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hyun, Sangeek Kim, Jihwan Heo, Jae-Pil Sungkyunkwan Univ Seoul South Korea

ISBN: (纸本)9781665445092

A video can be represented by the composition of appearance and motion. Appearance (or content) expresses the information invariant throughout time, and motion describes the time-variant movement. Here, we propose self-supervised approaches for video Generative Adversarial Networks (GANs) to achieve the appearance consistency and motion coherency in videos. Specifically, the dual discriminators for image and video individually learn to solve their own pretext tasks;appearance contrastive learning and temporal structure puzzle. The proposed tasks enable the discriminators to learn representations of appearance and temporal context, and force the generator to synthesize videos with consistent appearance and natural flow of motions. Extensive experiments in facial expression and human action public benchmarks show that our method outperforms the state-of-the-art video GANs. Moreover, consistent improvements regardless of the architecture of video GANs confirm that our framework is generic.

关键词： computer vision Force computer architecture Benchmark testing Generative adversarial networks Generators pattern recognition

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An Extensive Analysis of pattern Identification and Categorization

An Extensive Analysis of Pattern Identification and Categori...

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2024 International conference on Advances in Computing Research on Science Engineering and Technology, ACROSET 2024

作者： Vajpayee, Amit Kaur, Palak Preet Sharma, Ankit Tiwari, Abhishek Computer Science and Engineering Parul University Vadodara India Computer Science and Engineering Apex Institute of Technology Mohali India

ISBN: (纸本)9798350388800

pattern recognition and classification standards are important for data interpretation and decision-making processes in many fields. This article provides a comprehensive review of these two intersections, exploring their evolution, approaches, recent developments, and practical applications. We take a closer look at various applications in information modelling and classification, highlighting their advantages and limitations. We discuss simple processes such as supervised and unsupervised learning, emphasizing the role of features in effective representation. Classification algorithms their advantages, and limitations are described. The review continues to discuss the intersection of these areas and shows how they complement each other in solving problems. We also explore their diverse uses, from medical to autonomous driving, to demonstrate their transformative potential. This review aims to provide insight into recognition and classification standards, encouraging further research and innovation in these important areas. © 2024 ieee.

关键词： Adversarial machine learning

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