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

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GPU based Near Data processing.for image processing.with pattern Aware Data Allocation and Prefetching 19

GPU based Near Data Processing for Image Processing with Pat...

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proceedings of the International conference on Parallel Architectures and Compilation Techniques

作者： Jungwoo Choi Boyeal Kim Ji-Ye Jeon Hyuk-Jae Lee Euicheol Lim Chae Eun Rhee Seoul National University SKHynix Inha University

ISBN: (纸本)9781728136134

This paper proposes a GPU-based Near-data-processing.(NDP) architecture as well as a well-matched programming model considering both the characteristics of image applications and NDP constraints. First, data allocation to the processing.unit is handled to keep the data locality considering the memory access pattern. Second, this predictable allocation enables to design a compact but efficient NDP architecture. By applying a prefetcher that leverages the pattern aware data allocation, the number of active warps and on-chip SRAM size of NDP are significantly reduced. This allows to satisfy the NDP constraints and increases the opportunity to integrate more processing.units on a memory logic die. The evaluation results for various image processing.benchmarks show that the proposed NDP GPU improves the performance compared to the baseline GPU.

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Dual-channel capsule shape detection system based on computer vision and optical imaging technologies 5

Dual-channel capsule shape detection system based on compute...

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5th International conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Zhang, Tinghao University of Nottingham University Park NottinghamNG7 2RD United Kingdom

ISBN: (纸本)9781510691667

With the rapid advancement of industrial automation and smart manufacturing, computer vision technology, enhanced by optical imaging techniques, has found extensive application in the pharmaceutical industry, particularly in capsule shape detection. The integrity of capsule appearance directly impacts drug efficacy, and optical imaging plays a critical role in capturing precise details of capsule shapes, ensuring enhanced detection accuracy. Traditional manual inspection methods are limited by low efficiency, poor reliability, and high costs, making them unsuitable for large-scale, high-precision production. Automated detection systems based on optical sensors, coupled with computer vision, enable real-time monitoring and defect detection through image acquisition, processing. and pattern recognition, significantly improving detection speed and accuracy while reducing human error. This paper proposes a dual-channel capsule shape detection system that utilizes high-resolution optical cameras for image acquisition and incorporates an optimized deep learning algorithm to improve detection efficiency and accuracy. The system integrates advanced optical imaging techniques, such as precise lighting and edge detection, to enhance feature extraction capabilities for complex capsule images. Experimental results show that the dual-channel system significantly improves detection accuracy, achieving over 98.5% accuracy and reducing false detection rates, while enhancing the speed and reliability of capsule inspection in high-speed production environments. The combination of optical imaging and deep learning makes this system a promising solution for quality control in the pharmaceutical industry, supporting the transition to intelligent manufacturing and flexible production. © 2025 SPIE.

关键词： Inspection

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Hierarchical Spatial Pyramid Network For Cervical Precancerous Segmentation By Reconstructing Deep Segmentation Networks

Hierarchical Spatial Pyramid Network For Cervical Precancero...

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

作者： Meng, Zhu Zhao, Zhicheng Su, Fei Guo, Limei Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China Peking Univ Hosp 3 Hlth Sci Ctr Beijing Peoples R China

ISBN: (纸本)9781665448994

Cervical cancer is one of the leading causes of cancer death in women aged 20 to 39 years, which emphasizes the importance of cervical precancerous diagnosis and treatment. Although there are many attempts on medical image processing. the research on the automatic diagnosis of cervical precancerous pathology is still scarce. In this paper, a challenging end-to-end automatic segmentation task for cervical precancerous diagnosis is focused. Specifically, considering that the diagnosis of cervical lesions relies heavily on spatial information, a hierarchical spatial pyramid network (HSP-Net) is proposed to enhance the representation ability of cervical structural features. First, a vertical hierarchical spatial pyramid (V-HSP) network is devised to aggregate the multiscale information during the feature extraction of the encoder. Second, a horizontal hierarchical spatial pyramid (H-HSP) network is designed to fuse information of multiscale receptive fields before and after cascading features from different branches. Experiments on the public dataset MTCHI demonstrate that HSP-Net achieves the state-of-the-art performance, reflecting the potential to assist doctors and patients clinically.

关键词： Pathology image segmentation Fuses conferences Medical services Feature extraction pattern recognition

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A Study of Vision Transformer for Lung Diseases Classification 6

A Study of Vision Transformer for Lung Diseases Classificati...

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6th International conference on Green Technology and Sustainable Development, GTSD 2022

作者： Nguyen, Manh-Hung Quang, Khai Ngo HCMC University of Technology and Education Faculty of Electrical Electronic Engineering Ho Chi Minh City Viet Nam

ISBN: (数字)9781665466288

ISBN: (纸本)9781665466288

Transformer models have gained much success in natural language processing. In the computer vision field, transformer-based backbones recently compete with CNN-based backbones in many tasks. The success of transformer-based backbones relies on a pre-trained model that is trained on huge datasets. However, the requirement may not be satisfied in medical image applications. Compared to imageNet 21K dataset, medical image datasets are very limited. Therefore, in this paper, we discover the performance of the Vision Transformer on medical image classification. The vision transformer will be fine-tuned on well-known medical datasets. Later, it will be fine-tuned again on the VinDr-CXR dataset. Comprehensive experiments show that the proposed method is slightly better than conventional convolution-based methods in terms of classification accuracy. However, in terms of model interpretability, ViT based models can handle the co-occurrence of multi-diseases in a medical image. © 2022 ieee.

关键词： image classification

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Fast Walsh-Hadamard Transform and Smooth-Thresholding Based Binary Layers in Deep Neural Networks

Fast Walsh-Hadamard Transform and Smooth-Thresholding Based ...

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

作者： Pan, Hongyi Badawi, Diaa Cetin, Ahmet Enis Univ Illinois Dept Elect & Comp Engn Chicago IL 60680 USA

ISBN: (纸本)9781665448994

In this paper, we propose a novel layer based on fast Walsh-Hadamard transform (WHT) and smooth-thresholding to replace 1 x 1 convolution layers in deep neural networks. In the WHT domain, we denoise the transform domain coefficients using the new smooth-thresholding non-linearity, a smoothed version of the well-known soft-thresholding operator. We also introduce a family of multiplication-free operators from the basic 2x2 Hadamard transform to implement 3 x 3 depthwise separable convolution layers. Using these two types of layers, we replace the bottleneck layers in MobileNet-V2 to reduce the network's number of parameters with a slight loss in accuracy. For example, by replacing the final third bottleneck layers, we reduce the number of parameters from 2.270M to 947K. This reduces the accuracy from 95.21% to 92.88% on the CIFAR-10 dataset. Our approach significantly improves the speed of data processing. The fast Walsh-Hadamard transform has a computational complexity of O(mlog(2)m). As a result, it is computationally more efficient than the 1 x 1 convolution layer. The fast Walsh-Hadamard layer processes a tensor in R-10x32x32x1024 about 2 times faster than 1 x 1 convolution layer on NVIDIA Jetson Nano computer board.

关键词： Deep learning computer vision Tensors Convolution conferences Transforms computer architecture

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Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial imagery

Superpixels and Graph Convolutional Neural Networks for Effi...

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

作者： Dadsetan, Saba Pichler, David Wilson, David Hovakimyan, Naira Hobbs, Jennifer Univ Pittsburgh Pittsburgh PA 15260 USA Intelinair Inc San Francisco CA USA Univ Illinois Urbana IL USA

ISBN: (纸本)9781665448994

Advances in remote sensing technology have led to the capture of massive amounts of data. Increased image resolution, more frequent revisit times, and additional spectral channels have created an explosion in the amount of data that is available to provide analyses and intelligence across domains, including agriculture. However, the processing.of this data comes with a cost in terms of computation time and money, both of which must be considered when the goal of an algorithm is to provide real-time intelligence to improve efficiencies. Specifically, we seek to identify nutrient deficient areas from remotely sensed data to alert farmers to regions that require attention;detection of nutrient deficient areas is a key task in precision agriculture as farmers must quickly respond to struggling areas to protect their harvests. Past methods have focused on pixel-level classification (i.e. semantic segmentation) of the field to achieve these tasks, often using deep learning models with tens-of-millions of parameters. In contrast, we propose a much lighter graph-based method to perform node-based classification. We first use Simple Linear Iterative Cluster (SLIC) to produce super-pixels across the field. Then, to perform segmentation across the non-Euclidean domain of superpixels, we leverage a Graph Convolutional Neural Network (GCN). This model has 4-orders-of-magnitude fewer parameters than a CNN model and trains in a matter of minutes.

关键词： image segmentation Semantics Agriculture Real-time systems Computational efficiency pattern recognition Convolutional neural networks

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proceedings - 2021 ieee/CVF conference on computer Vision and pattern recognition Workshops, CVPRW 2021

Proceedings - 2021 IEEE/CVF Conference on Computer Vision an...

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2021 ieee/CVF conference on computer Vision and pattern recognition Workshops, CVPRW 2021

ISBN: (纸本)9781665448994

The proceedings contain 516 papers. The topics discussed include: OmniLayout: room layout reconstruction from indoor spherical panoramas;boosting adversarial robustness using feature level stochastic smoothing;beyond joint demosaicking and denoising: an image processing.pipeline for a pixel-bin image sensor;assessment of deep learning based blood pressure prediction from PPG and rPPG signals;towards domain-specific explainable AI: model interpretation of a skin image classifier using a human approach;DAMSL: domain agnostic meta score-based learning;deep learning based spatial-temporal in-loop filtering for versatile video coding;automated tackle injury risk assessment in contact-based sports - a rugby union example;two-stage network for single image super-resolution;and ***: dataset for automatic mapping of buildings, woodlands, water and roads from aerial imagery.

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Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label image recognition

Diverse Instance Discovery: Vision-Transformer for Instance-...

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2022 ieee International conference on Multimedia and Expo, ICME 2022

作者： Hu, Yunqing Jin, Xuan Zhang, Yin Hong, Haiwen Zhang, Jingfeng Yan, Feihu He, Yuan Xue, Hui College of Computer Science and Technology Zhejiang University Hangzhou China Alibaba Group Hangzhou China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Previous works on multi-label image recognition (MLIR) usually use CNNs as a starting point for research. In this paper, we take pure Vision Transformer (ViT) as the research base and make full use of the advantages of Transformer with long-range dependency modeling to circumvent the disadvantages of CNNs limited to local receptive field. However, for multi-label images containing multiple objects from different categories, scales, and spatial relations, it is not optimal to use global information alone. Our goal is to leverage ViT's patch tokens and self-attention mechanism to mine rich instances in multi-label images, named diverse instance discovery (DiD). To this end, we propose a semantic category-aware module and a spatial relationship-aware module, respectively, and then combine the two by a re-constraint strategy to obtain instance-aware attention maps. Finally, we propose a weakly supervised object localization-based approach to extract multi-scale local features, to form a multi-view pipeline. Our method requires only weakly supervised information at the label level, no additional knowledge injection or other strongly supervised information is required. Experiments on three benchmark datasets show that our method significantly outperforms previous works and achieves state-of-the-art results under fair experimental comparisons. © 2022 ieee.

关键词： image recognition

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OkkhorNet: A Deep Convolutional Neural Network to Classify Bengali Handwritten Characters

OkkhorNet: A Deep Convolutional Neural Network to Classify B...

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2022 ieee Asia-Pacific conference on computer Science and Data Engineering, CSDE 2022

作者： Sikder, Shihab Uddin Muslebeen, Md. Shafiul Karim, Dewan Ziaul Saha, Ramkrishna Bushra, Tasfia Anika Brac University Computer Science and Engineering Dhaka Bangladesh Daffodil International University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665453059

Handwritten letter classification of any given language has the potential to be used in various fields such as literature, educational institutions, digitization of government records etc. Bengali language with its complex sets of mixed characters, poses significant complexities in terms of automatic recognition of characters. In the Bengali character set, there are over 360 distinct characters among which a lot of similarities are present between different characters. Thus, the classification of these characters gets harder as the recognition system incorporates all these distinct characters. In recent years, a lot of research has been done to solve this problem on isolated datasets with significant results. Continuing the advancement in image processing. In this paper, we have proposed a custom CNN model which has been trained on Bangla Lekha Isolated dataset containing 1,66,106 images belong to 84 distinct classes with the capability to detect individual handwritten Bengali letters including digits, vowels, consonants and compound characters. Our proposed model was able to achieve 94.73% accuracy while using less number of parameters compared to existing popular models © 2022 ieee.

关键词： Convolutional neural networks

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Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions

Feature Corrective Transfer Learning: End-to-End Solutions t...

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

作者： Chuheng Wei Guoyuan Wu Matthew J. Barth University of California Riverside

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A significant challenge in the field of object detection lies in the system’s performance under non-ideal imaging conditions, such as rain, fog, low illumination, or raw Bayer images that lack ISP processing. Our study introduces ‘Feature Corrective Transfer Learning’, a novel approach that leverages transfer learning and a bespoke loss function to facilitate the end-to-end detection of objects in these challenging scenarios without the need to convert non-ideal images into their RGB counterparts. In our methodology, we initially train a comprehensive model on a pristine RGB image dataset. Subsequently, non-ideal images are processed by comparing their feature maps against those from the initial ideal RGB model. This comparison employs the Extended Area Novel Structural Discrepancy Loss (EANSDL), a novel loss function designed to quantify similarities and integrate them into the detection loss. This approach refines the model’s ability to perform object detection across varying conditions through direct feature map correction, encapsulating the essence of Feature Corrective Transfer Learning. Experimental validation on variants of the KITTI dataset demonstrates a significant improvement in mean Average Precision (mAP), resulting in a 3.8-8.1% relative enhancement in detection under non-ideal conditions compared to the baseline model, and a less marginal performance difference within 1.3% of the mAP@[0.5:0.95] achieved under ideal conditions by the standard Faster RCNN algorithm.

关键词： Visualization computer vision Accuracy Computational modeling Surveillance Transfer learning Imaging

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