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检索条件"任意字段=Proceedings - IEEE Computer Society Conference on Pattern Recognition and Image Processing."

共 8421 条记录，以下是561-570 订阅

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Histopathological image Classification with Cell Morphology Aware Deep Neural Networks

Histopathological Image Classification with Cell Morphology ...

引用

ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Andrey Ignatov Josephine Yates Valentina Boeva Computer Vision Lab ETH Zurich Computational Cancer Genomics Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Histopathological images are widely used for the analysis of diseased (tumor) tissues and patient treatment selection. While the majority of microscopy image processing.was previously done manually by pathologists, recent advances in computer vision allow for accurate recognition of lesion regions with deep learning-based solutions. Such models, however, usually require extensive annotated datasets for training, which is often not the case in the considered task, where the number of available patient data samples is very limited. To deal with this problem, we propose a novel DeepCMorph model pre-trained to learn cell morphology and identify a large number of different cancer types. The model consists of two modules: the first one performs cell nuclei segmentation and annotates each cell type, and is trained on a combination of 8 publicly available datasets to ensure its high generalizability and robustness. The second module combines the obtained segmentation map with the original microscopy image and is trained for the downstream task. We pre-trained this module on the Pan-Cancer TCGA dataset consisting of over 270K tissue patches extracted from 8736 diagnostic slides from 7175 patients. The proposed solution achieved a new state-of-the-art performance on the dataset under consideration, detecting 32 cancer types with over 82% accuracy and outperforming all previously proposed solutions by more than 4%. We demonstrate that the resulting pre-trained model can be easily fine-tuned on smaller microscopy datasets, yielding superior results compared to the current top solutions and models initialized with imageNet weights. The codes and pre-trained models presented in this paper are available at: https://***/aiff22/DeepCMorph

关键词： image segmentation computer vision Analytical models Accuracy Microscopy Microprocessors Computational modeling

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DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality

DeepVecFont-v2: Exploiting Transformers to Synthesize Vector...

引用

conference on computer Vision and pattern recognition (CVPR)

作者： Yuqing Wang Yizhi Wang Longhui Yu Yuesheng Zhu Zhouhui Lian Wangxuan Institute of Computer Technology Peking University China School of Electronic and Computer Engineering Peking University China

Vector font synthesis is a challenging and ongoing problem in the fields of computer Vision and computer Graphics. The recently-proposed DeepVecFont [27] achieved state-of-the-art performance by exploiting information of both the image and sequence modalities of vector fonts. However, it has limited capability for handling long sequence data and heavily relies on an image-guided outline refinement post-processing. Thus, vector glyphs synthesized by DeepVecFont still often contain some distortions and artifacts and cannot rival human-designed results. To address the above problems, this paper proposes an enhanced version of DeepVecFont mainly by making the following three novel technical contributions. First, we adopt Transformers instead of RNNs to process sequential data and design a relaxation representation for vector outlines, markedly improving the model's capability and stability of synthesizing long and complex outlines. Second, we propose to sample auxiliary points in addition to control points to precisely align the generated and target Bézier curves or lines. Finally, to alleviate error accumulation in the sequential generation process, we develop a context-based self-refinement module based on another Transformer-based decoder to remove artifacts in the initially synthesized glyphs. Both qualitative and quantitative results demonstrate that the proposed method effectively resolves those intrinsic problems of the original DeepVecFont and outperforms existing approaches in generating English and Chinese vector fonts with complicated structures and diverse styles.

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FairSSD: Understanding Bias in Synthetic Speech Detectors

FairSSD: Understanding Bias in Synthetic Speech Detectors

引用

ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Amit Kumar Singh Yadav Kratika Bhagtani Davide Salvi Paolo Bestagini Edward J. Delp Video and Image Processing Lab (VIPER) Purdue University West Lafayette Indiana USA Dipartimento di Elettronica Informazione e Bioingegneria Milano Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Methods that can generate synthetic speech which is perceptually indistinguishable from speech recorded by a human speaker, are easily available. Several incidents report misuse of synthetic speech generated from these methods to commit fraud. To counter such misuse, many methods have been proposed to detect synthetic speech. Some of these detectors are more interpretable, can generalize to detect synthetic speech in the wild and are robust to noise. However, limited work has been done on understanding bias in these detectors. In this work, we examine bias in existing synthetic speech detectors to determine if they will unfairly target a particular gender, age and accent group. We also inspect whether these detectors will have a higher misclassification rate for bona fide speech from speech-impaired speakers w.r.t fluent speakers. Extensive experiments on 6 existing synthetic speech detectors using more than 0.9 million speech signals demonstrate that most detectors are gender, age and accent biased, and future work is needed to ensure fairness. To support future research, we release our evaluation dataset, models used in our study and source code at https://***/viper-purdue/fairssd.

关键词： computer vision Source coding conferences Noise Detectors pattern recognition Fraud

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A Semantic Segmentation Method of Buildings in Remote Sensing image Based on Improved UNet 2

A Semantic Segmentation Method of Buildings in Remote Sensin...

引用

2nd International conference on Signal image processing.and Communication, ICSIPC 2022

作者： Li, Zhongyu Liu, Yang Kuang, Yin Wang, Huajun Liu, Cheng College of Computer Science Chengdu Normal University Chengdu611130 China College of Geophysics Chengdu University of Technology Chengdu610059 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China Key Laboratory of interior Layout optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Sichuan Chengdu611130 China College of Movie and Media Sichuan Normal University Chengdu610066 China

ISBN: (纸本)9781510657694

Aiming at the problem of model instability and overfitting of deep neural networks with the deepening of the number of network layers, the current mainstream method is to use batch normalization (BN) to alleviate them. However, since the BN method is more sensitive to batch size when the batch size is small, the model performance will be poor. For a relatively large model, due to the limitation of video memory, the batch size cannot take a large value, limiting the model's performance. Because of the dependence of BN on batch size, this paper adopts group normalization (GN) instead of batch normalization (BN) in the UNet network to alleviate the impact of the model on batch size. Then experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet-GN) improves the mean intersection over union (MIoU) and mean pixel accuracy (MPA) by 10.66% and 1.65% respectively compared with the original model (UNet-BN). © 2022 SPIE.

关键词： Deep neural networks

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Segment Anything Model for Road Network Graph Extraction

Segment Anything Model for Road Network Graph Extraction

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ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Congrui Hetang Haoru Xue Cindy Le Tianwei Yue Wenping Wang Yihui He Carnegie Mellon University Columbia University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We propose SAM-Road, an adaptation of the Segment Anything Model (SAM) [27] for extracting large-scale, vectorized road network graphs from satellite imagery. To predict graph geometry, we formulate it as a dense semantic segmentation task, leveraging the inherent strengths of SAM. The image encoder of SAM is fine-tuned to produce probability masks for roads and intersections, from which the graph vertices are extracted via simple non-maximum suppression. To predict graph topology, we designed a lightweight transformer-based graph neural network, which leverages the SAM image embeddings to estimate the edge existence probabilities between vertices. Our approach directly predicts the graph vertices and edges for large regions without expensive and complex post-processing.heuristics and is capable of building complete road network graphs spanning multiple square kilometers in a matter of seconds. With its simple, straightforward, and minimalist design, SAM-Road achieves comparable accuracy with the state-of-the-art method RNGDet++[57], while being 40 times faster on the City-scale dataset. We thus demonstrate the power of a foundational vision model when applied to a graph learning task. The code is available at https://***/htcr/sam_road.

关键词： Accuracy Network topology Roads image edge detection Computational modeling Semantic segmentation Transformers

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PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation

PatchFusion: An End-to-End Tile-Based Framework for High-Res...

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conference on computer Vision and pattern recognition (CVPR)

作者： Zhenyu Li Shariq Farooq Bhat Peter Wonka King Abdullah University of Science and Technology (KAUST)

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Single image depth estimation is a foundational task in computer vision and generative modeling. However, prevailing depth estimation models grapple with accom-modating the increasing resolutions commonplace in to-day's consumer cameras and devices. Existing high-resolution strategies show promise, but they often face limi-tations, ranging from error propagation to the loss of high-frequency details. We present PatchFusion, a novel tile-based framework with three key components to improve the current state of the art: (1) A patch-wise fusion network that fuses a globally-consistent coarse prediction with finer, in-consistent tiled predictions via high-level feature guidance, (2) A Global-to-Local (G2L) module that adds vital con-text to the fusion network, discarding the need for patch selection heuristics, and (3) A Consistency-Aware Training (CAT) and Inference (CAI) approach, emphasizing patch overlap consistency and thereby eradicating the neces-sity for post-processing. Experiments on Unreal. Stereo 4K, MVS-Synth, and Middleburry 2014 demonstrate that our framework can generate high-resolution depth maps with intricate details. PatchFusion is independent of the base model for depth estimation. Notably, our framework built on top of SOTA ZoeDepth brings improvements for a total of 17.3% and 29.4% in terms of the root mean squared error (RMSE) on UnrealStereo4K and MVS-Synth, respectively.

关键词： Training computer vision image resolution Fuses Computational modeling Estimation Network architecture

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Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal image Super Resolution

Multi-Scale Feature Fusion using Channel Transformers for Gu...

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ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Raghunath Sai Puttagunta Birendra Kathariya Zhu Li George York University of Missouri-Kansas City US Air Force Academy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal imaging, leveraging the infrared spectrum, offers a compelling alternative to visible spectrum (VIS) imagery in challenging environmental conditions like low- light, occlusions, and adverse weather. However, its widespread adoption in computer vision tasks is hampered by lower spatial resolution. We address this challenge by proposing a novel framework titled Multi-Scale Feature Fusion using Channel Transformers (MSFFCT) for Guided Thermal image Super-Resolution (GTISR).GTISR tackles the resolution limitations of thermal imagery. It leverages high-resolution RGB information as a guide to reconstruct high-resolution thermal imagery from low-resolution thermal inputs. At the core of MSFFCT lies a novel deep learning architecture that combines the strengths of two powerful approaches: channel-based transformers and multi-scale *** overcomes inherent limitations of Convolutional Neural Networks (CNNs) typically used in super-resolution tasks. CNNs often suffer from restricted receptive fields, limiting their ability to capture long-range dependencies within the image. Additionally, computational cost grows significantly with larger inputs. MSFFCT addresses these shortcomings by enabling efficient processing.of global information and offering superior scalability. MSFFCT achieved state-of-the-art results on the ×8 and × 16 GTISR tasks of the 2024 Perception Beyond Visual Spectrum (PBVS) challenge, winning 2 nd place in both tasks and demonstrating its effectiveness in real-world scenarios.

关键词： computer vision Visualization Limiting Superresolution Imaging Transformer cores Transformers

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Algorithm of Digital Dynamic Sculpture Expression Change Based on Virtual Reality Technology

Algorithm of Digital Dynamic Sculpture Expression Change Bas...

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2022 International conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2022

作者： Yu, Guangyong Liaoning Communication University Shenyang110000 China

ISBN: (数字)9781665454575

ISBN: (纸本)9781665454575

The creation, expression and application of dynamic sculpture public art in public environment space combines traditional dynamic sculpture art with contemporary public art in a specific angle, and then tries to sort out the contribution and function of dynamic sculpture art to contemporary public art based on actual creation. Dynamic sculpture has played an important role in modern sculpture art, and a large number of outstanding artists and works have emerged, which has revolutionized the form of sculpture art. Expression recognition technology is a challenging cross-cutting subject involving emotion calculation, image processing. machine vision, motion tracking, pattern recognition, biometric identification, physiology, psychology and other research fields. It is an important part of emotion calculation and human-computer intelligent interaction. However, because two-dimensional images are the projection of three-dimensional objects onto a two-dimensional plane, the texture, shape and other features will inevitably be lost during the projection process, so gesture recognition based on virtual reality (VR) has become the research focus and center in this field. This paper proposes a digital dynamic sculpture expression change algorithm based on VR technology, which involves the method of dynamic sculpture expression feature extraction, the sculpture expression change extraction algorithm based on VR, the analysis of stereo display algorithm based on projection transformation, and the analysis of stereo display algorithm. It can make people pay attention to the interactive communication with sculpture in the process of use, the feeling of sculpture artistic image and personalized recreation. Through the dynamics of sculpture structure and elements, we can meet people's growing aesthetic needs to a certain extent. © 2022 ieee.

关键词： Stereo image processing

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Multimodal Human Activity recognition for Smart Healthcare Applications

Multimodal Human Activity Recognition for Smart Healthcare A...

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2022 ieee International conference on Systems, Man, and Cybernetics, SMC 2022

作者： Islam, Md. Milon Nooruddin, Sheikh Karray, Fakhri University of Waterloo Centre for Pattern Analysis and Machine Intelligence Department of Electrical and Computer Engineering ONN2L 3G1 Canada Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Human Activity recognition (HAR) has emerged as a potential research topic for smart healthcare owing to the fast growth of wearable and smart devices in recent years. The significant applications of HAR in ambient assisted living environments include monitoring the daily activities of elderly and cognitively impaired individuals to assist them by observing their health status. In this research, we present a deep learning-based fusion approach for multimodal HAR that fuses the different modalities of data to obtain robust outcomes. Here, Convolutional Neural Networks (CNNs) retrieve the high-level attributes from the image data, and the Convolutional Long Short Term Memory (ConvLSTM) is utilized to capture significant patterns from the multi-sensory data. Finally, the extracted features from the modalities are fused through self-attention mechanisms that enhance the relevant activity data and inhibit the superfluous and possibly confusing information by measuring their compatibility. Lastly, extensive tests have been performed to measure the efficiency and robustness of the developed fusion approach using the UP-Fall detection dataset. It is evident from the experimental findings that the proposed fusion technique outperforms the existing state-of-the-art and achieves relatively better performance. © 2022 ieee.

关键词： Long short-term memory

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Real-Time Signal processing.using AI Integrated Framework for Color and Drawing in Gesture recognition 5

Real-Time Signal Processing using AI Integrated Framework fo...

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5th International conference on Contemporary Computing and Informatics, IC3I 2022

作者： Lavania, Geerija Arya, Vivek Sharma, Neha Rashid, Mamoon Akram, Shaik Vaseem JECRC Artificial Intelligence & Data Science Department Rajasthan Jaipur India Dept of Electronics and Communication Engineering Haridwar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Vishwakarma University Department of Computer Engineering Pune India Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (纸本)9798350398267

In the realm of image processing.and pattern recognition, writing with gestures in the air has grown in popularity and difficulty over the past few years. It contributes significantly to the creation of automation processes and can improve human-machine communication in a range of systems. Many studies focus on cutting-edge methods and techniques that will expedite processing.while preserving a high level of identification accuracy. Internally, the area of computer vision considers object tracking to be crucial. Object tracking techniques are credited to modern computers, easy access to good-looking, affordable video cameras, and automated video analytics requirements. The video analysis technique generally consists of three fundamental steps: first, recognizing the object, second, tracking its movement from frame to frame, and third, assessing the behavior of that object. Each of the four takes into account a separate issue: choosing the best item representation, choosing the tracking function, purchasing the item, and tracking the item. Numerous real-world applications, such as autonomous monitoring, video identification, and vehicle navigation, depend heavily on object-tracking algorithms. This area is utilized by the research, which focuses on it to create a prospective text-to-text converter transition. This Research occasionally works as a freelance journalist. Your fingerprint will be tracked by a computer. The created text can be copied and used for a variety of things, including emails and SMS. For the deaf, it will be a potent means to establish this connection. By removing the need for writing, an efficient communication system decreases the use of the phone and laptop. © 2022 ieee.

关键词： computer vision

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