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检索条件"任意字段=IEEE Symposium on Computational Intelligence for Multimedia Signal and Vision Processing, CIMSVP"

共 113 条记录，以下是11-20 订阅

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JPEG AI Compressed Domain Face Detection

JPEG AI Compressed Domain Face Detection

引用

ieee Workshop on multimedia signal processing

作者： Ayman Alkhateeb Alessandro Gnutti Fabrizio Guerrini Riccardo Leonardi João Ascenso Fernando Pereira Dep. of Information Engineering CNIT - University of Brescia Italy Dep. of Electronics Engineering University of Rome Tor Vergata Italy Instituto Superior Técnico Universidade de Lisboa - Instituto de Telecomunicações Lisbon Portugal

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Learning-based image coding has achieved competitive performance in terms of compression efficiency, while also gaining a key advantage in the ability to carry out computer vision tasks directly in the compressed domain. In fact, the latent representation which is generated using deep learning techniques may natively encapsulate all visual features needed for processing tasks, thereby eliminating the need to perform the expensive synthesis transform process at the decoder side. In this paper, it is proposed to perform face detection using the latent code present in the JPEG AI architecture. First, some experiments show how decoded images can be efficiently processed for face detection without retraining, albeit with some performance degradation. Then, for the first time a compressed domain RetinaFace-based detector applied to JPEG AI latent representations is competitively proposed. The performance achieved is comparable to the performance of the original RetinaFace applied to the reconstructed JPEG AI images, while reducing computational complexity since it bypasses the image decoding process. It is expected that this approach might be extended to other vision tasks since the JPEG AI representation format is not tailored specifically for any computer vision task.

关键词： Visualization Image coding Transform coding Transforms signal processing Decoding Face detection Artificial intelligence Image reconstruction Faces

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Efficient vision Transformer Deployment on Google Coral Through Knowledge Distillation

Efficient Vision Transformer Deployment on Google Coral Thro...

引用

East-West Design & Test symposium (EWDTS)

作者： Vazgen Melikyan Tigran Khachatryan Davit Galstyan Eduard Harutyunyan National Polytechnic University of Armenia Synopsys Armenia CJSC Yerevan Armenia Synopsys Armenia Educational Department National Polytechnic University of Armenia Yerevan Armenia

ISBN: (数字)9798331515768

ISBN: (纸本)9798331515775

With the development of AI, the models are becoming more complex and resource intensive. There is a need to create smaller but at the same time satisfactory performance models that will run on resource-constrained devices. This paper explores enhancing the efficiency of the MobileNetV2 architecture using the power of the vision Transformer through knowledge distillation. The goal was to leverage the complex insights of the vision Transformer to improve the capabilities of MobileNetV2. As a result, the refined model outperformed its original version by about 5% (from 81.7% to 86.5%). The model was then placed on the Coral Dev Board edge device and due to the weight adjustments, there was a slight decrease in accuracy to 84.3%. Despite this, it reduced its processing latency by more than half, from 0.0076 seconds to 0.0037 seconds, proving a valuable approach for efficient and real-time edge computing applications.

关键词： Performance evaluation Computer vision Accuracy Quantization (signal) computational modeling Transformers Real-time systems Internet Artificial intelligence Edge computing

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Deep Learning-Based computational Model for Disease Identification in Cocoa Pods

Deep Learning-Based Computational Model for Disease Identifi...

引用

symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Cristian Zambrano-Vega Darlyn Buenaño Vera Washington Chiriboga Casanova Byron Oviedo Department of Engineering Science State Technical University of Quevedo Quevedo Ecuador

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

The early identification of diseases in cocoa pods is an important task to guarantee the production of high-quality cocoa. The use of artificial intelligence techniques such as machine learning, computer vision and deep learning are promising solutions to help identify and classify diseases in cocoa pods. In this paper we introduce the development and evaluation of a deep learning computational model applied to the identification of diseases in cocoa pods, focusing on “monilia” and “black pod” diseases. An exhaustive review of state-of-the-art of computational models was carried out, based on scientific articles related to the identification of plant diseases using computer vision and deep learning techniques. As a result of the search, EfficientDet-Lite4, an efficient and lightweight model for object detection, was selected. A dataset, including images of both healthy and diseased cocoa pods, has been utilized to train the model to detect and pinpoint disease manifestations with considerable accuracy. Significant enhancements in the model training and evaluation demonstrate the capability of recognizing and classifying diseases through image analysis. Furthermore, the functionalities of the model were integrated into an Android native mobile with an user-friendly interface, allowing to younger or inexperienced farmers a fast and accuracy identification of health status of cocoa pods.

关键词： Deep learning Training Computer vision Image recognition Accuracy Reviews computational modeling Computer architecture Search problems Diseases

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Mask Inpaints Using Downsampled Fast Fourier Convolution

Mask Inpaints Using Downsampled Fast Fourier Convolution

引用

symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Xiaoyang Gao Yifan Huang Penghui Gao Yong Zhang Tao Yang School of Information and Intelligence Engineering University of Sanya Hainan China Academician Workstation of Chumming Rong University of Sanya Hainan China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

In recent years, image inpainting has seen significant advancements, with the LaMa model., introduced in 2021, standing out for its improvements in large-area inpainting. Yet, challenges such as inpainting complex geometries, processing high-resolution images swiftly, and achieving realism in filled areas persist. Existing solutions that incorporate diffusion models to enhance realism are hardware-intensive and unsuitable for high-resolution images. We propose a new image inpainting method, Downsampled Fast Fourier Convolution (DFFC), which addresses these challenges. Unlike LaMa's reliance on Fast Fourier Convolution (FFC) for early-stage global context capture without explicit downsampling, DFFC introduces a deep learning-driven downsampling module to reduce computational complexity and improve high-resolution image processing efficiency. This module allows for data volume reduction during processing while preserving global context, comprising: i) deep learning-based image downsampling, ii) an FFC-based inpainting architecture, iii) a high-perceptual-domain loss function, and iv) dynamic large-area mask training. Our method maintains the original model's performance, enhances processing speed, and reduces computational load.

关键词： Training Geometry Convolution Image processing computational modeling Computer architecture Diffusion models Informatics computational complexity Load modeling

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Profiling the Arion rufus snails with computer vision

Profiling the Arion rufus snails with computer vision

引用

International symposium on computational intelligence and Informatics

作者： Grega Vrbančič Rok Kukovec Vili Podgorelec Sancho Salcedo-Sanz Iztok Fister Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia Dept. of Signal Processing and Commun. Universidad de Alcalá Alcalá de Henares Spain

ISBN: (纸本)9798350398830

Smart agriculture takes advantage of modern computational approaches that vary from IoT, cloud computing, and artificial intelligence. The primary aim is to assist the farming process. Pest detection is one of the objectives within the area of smart agriculture. It is mainly solved by computer vision approaches, usually combined with machine learning (ML) algorithms. In this paper, we propose a solution for detecting Arion rufus snails that have emerged in Central Europe and are one of the most prolific threats to agriculture in that place. Practical experiments reveal that our method is helpful in this real-world application and opens several future challenges and lines of research.

关键词： Smart agriculture Computer vision Mechatronics Machine learning algorithms Robot vision systems Machine learning Object detection

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Constructing Multi-granular Knowledge Graph with Structure Similarity and Hierarchical Clustering

Constructing Multi-granular Knowledge Graph with Structure S...

引用

symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Ke Yang Jingyun Feng Zhiyu Xi Qiuke Zheng Huan Zhang Wei Deng China Railway Eryuan Engineering Group Co. Ltd. Chengdu China Dept. of Statistics Southern University of Science and Technology Shenzhen China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China School of Statistics Southwestern University of Finance and Economics Chengdu China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

The structure of knowledge graph is basically flat and lack of hierarchies to make knowledge graph too general to deal with concrete reasoning problems. There are few work about hierarchical knowledge graph. In this paper, the entity and relation combined method is proposed to divide the entities into groups based on structure similarity. The hierarchical cluster method is proposed with the entity and relation combined embedding representation to construct a multi-granular knowledge graph. The experimental results show that the hierarchical division relied on combined embedding are significantly better than those based on the existing entity embedding methods in three evaluation indexes. The combined embedding can capture the structure information which is helpful to hierarchical knowledge graph accurately and reasonably.

关键词： Accuracy Large language models Clustering algorithms Knowledge graphs Cognition Classification algorithms Indexes Informatics

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An Inter-Layer Volumetric Attention Network for Teeth Segmentation in Cross-FOV CBCT Dental Images

An Inter-Layer Volumetric Attention Network for Teeth Segmen...

引用

symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Fan Qin Xiaoguang Li Muqing Liu Zhiwei Huang Jianhui Han Li Zhuo School of Information Science Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China Department of Oral & Maxillofacial Radiology Peking University School & Hospital of Stomatology Beijing China Department of Oral and Maxillofacial Radiology School of Stomatology Capital Medical University Beijing China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Accurate teeth segmentation is the foundation of intelligent analysis in dental CBCT images and has significant application value for oral clinical practice. The existing teeth segmentation algorithms mainly focus on the oral image with a global field of view. However, in clinical data, CBCT oral data has significant differences in the field of views. This paper proposes a cross-view oral CBCT teeth segmentation network based on an inter-layer volumetric attention network to address the challenges of different perspectives. Firstly, a 3D inter-layer cross-attention mechanism was designed to handle different visual field features effectively. Secondly, a target scale normalization strategy based on physical resolution is introduced to align teeth in various field of views. Finally, a local random cropping training strategy is adopted to improve the robustness of the teeth segmentation network. In addition, this paper also excludes areas unrelated to teeth through ROI detection. The experimental results show that the proposed method achieves robust and accurate cross-field segmentation in clinical CBCT oral images.

关键词： Training Location awareness Image segmentation Visualization Accuracy Attention mechanisms Three-dimensional displays Teeth Robustness Dentistry

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LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel Attention-Aided U-Net Model for Microscopic Medical Image Segmentation

LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel...

引用

symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Sanchita Das Gouranga Maity Diptarka Mandal Dmitrii Kaplun Alexander Voznesensky Ram Sarkar Dept. of CSE Jadavpur University Kolkata India Department of Applied Mathematics Defence Instituite of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Dept. of Automation and Control Process St. Petersburg Electrotechnical University “LETI” St. Petersburg Russian Federation

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Microscopic medical image segmentation is essential for detailed cellular-level analysis in computational pathology, offering insights into disease mechanisms. However, this task is particularly challenging due to the high similarity between different cellular classes and variability within each class, especially in densely packed or overlapping cell regions. To this end, we present LEECAU-Net (Latent Entropy-quantized and Efficient Channel Attention-aided U-Net), a U-Net-based model designed to improve segmentation accuracy through a dual-attention mechanism. Built on an Inceptionv4 backbone, LEECAU-Net's encoder integrates Latent Entropy-quantized Channel Attention (LECA) and Efficient Channel Attention (ECA) modules. The ECA module enhances the network's ability to focus on essential channel dependencies, while LECA leverages entropy quantification to prioritize channels rich in information, leading to better differentiation between similar structures. We have tested LEECAU-Net on three challenging publicly available datasets: Triple Negative Breast Cancer (TNBC), Multi-organ Nuclei Segmentation (MoNuSeg), and Cervical Nucleus Segmentation (CNS). Across these datasets, our model achieves higher performance metrics than numerous state-of-the-art approaches reported in the literature. The improved results underscore the effectiveness of LEECAU-Net's architecture in handling the complexity of microscopic medical images, making it a valuable tool for advancing automated cellular analysis in biomedical research. The source code and more results are made available at https://***/Cmatermedicalimageanalysis/LEECAU-Net.

关键词： Image segmentation Accuracy Microscopy computational modeling Biological system modeling Source coding Computer architecture Entropy Robustness Medical diagnostic imaging

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Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework

Part-level Action Parsing via a Pose-guided Coarse-to-Fine F...

引用

ieee International symposium on Circuits and Systems

作者： Xiaodong Chen Xinchen Liu Wu Liu Kun Liu Dong Wu Yongdong Zhang Tao Mei Universiy of Science nd Technoogy of Chin Hefei ChinJD.co Beijing ChinJD.co Beijing ChinJD.co Beijing ChinJD.co Beijing ChinUniversiy of Science nd Technoogy of Chin Hefei ChinJD.co Beijing Chin

ISBN: (数字)9781665484855

ISBN: (纸本)9781665484862

Action recognition from videos, i.e., classifying a video into one of the pre-defined action types, has been a popular topic in the communities of artificial intelligence, multimedia, and signal processing. However, existing methods usually consider an input video as a whole and learn models, e.g., Convolutional Neural Networks (CNNs), with coarse video-level class labels. These methods can only output an action class for the video, but cannot provide fine-grained and explainable cues to answer why the video shows a specific action. Therefore, researchers start to focus on a new task, Part-level Action Parsing (PAP), which aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video. To this end, we propose a coarse-to-fine framework for this challenging task. In particular, our framework first predicts the video-level class of the input video, then localizes the body parts and predicts the part-level action. Moreover, to balance the accuracy and computation in part-level action parsing, we propose to recognize the part-level actions by segment-level features. Furthermore, to overcome the ambiguity of body parts, we propose a pose-guided positional embedding method to accurately localize body parts. Through comprehensive experiments on a large-scale dataset, i.e., Kinetics-TPS, our framework achieves state-of-the-art performance and outperforms existing methods over 31.10% ROC score.

关键词： Circuits and systems Detectors Streaming media signal processing computational efficiency Convolutional neural networks Task analysis

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Computer vision for Astronomical Image Analysis

Computer Vision for Astronomical Image Analysis

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symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Sotiria Karypidou Ilias Georgousis George A. Papakostas MLV Research Group International Hellenic University Kavala Greece

ISBN: (纸本)9781665426565

Computer vision (CV) is undoubtedly one of the most popular forms of Artificial intelligence (AI) and its implementation has gained considerable ground in all aspects of our lives, from security and automotive, to the night sky observation and astronomy. In general, CV uses pattern recognition techniques for identifying objects in visual media (both static and moving images). The current archetype in CV is largely based on supervised AI, which uses large data sets of human-labelled images for training. Machine Learning (ML) and Deep Learning (DL) models in computer vision have undergone a period of extremely rapid development in recent past years; in particular for object recognition and localisation tasks. An area of study with great interest in practical applications that concerns this essay, is astronomical images analysis. However, one of the main challenges facing researchers these days is the existence of large quantities of annotated data sets, in the appropriate resolution and scale. This challenge consequently asks for huge amounts of storage and high computational power. In this paper, we systematically review and analyze different challenges faced by astronomers and continue with state-of-the-art methodologies that were conducted over the last decade.

关键词： Deep learning Training Computer vision Analytical models Visualization Image analysis Image resolution

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