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检索条件"任意字段=13th International Conference on Computer Analysis of Images and Patterns"

共 3117 条记录，以下是11-20 订阅

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Highresolution Remote Sensing Image Classification With Limited Training Data 13

Highresolution Remote Sensing Image Classification With Limi...

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13th Iranian/3rd international Machine Vision and Image Processing conference (MVIP)

作者： Ariaei, Mehdi Ghassemian, Hassan Imani, Maryam Tarbiat Modares Univ Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Accurate classification of land cover from aerial images is one of the research topics in remote sensing and is also in high demand in industry. However, obtaining labeled data for training different classifiers that heavily depend on supervision is still a challenging and resource-intensive task. Unsupervised methods have emerged as a powerful alternative to overcome the limitations associated with labeled data. Such methods have a high ability to discover hidden patterns and structures in multi-spectral images and have the possibility of classifying various types of land cover without relying on labeled samples. Our research primarily involved the analysis of World-View3 satellite imagery. Our strategy involved creating an advanced pipeline that extracted features using autoencoders. through this approach, the multi-spectral images' key characteristics are efficiently extracted. Subsequently, we implement transfer learning to re-train the model with a limited number of labeled data. By applying transfer learning, our pipeline significantly enhances the capability of multispectral image processing, enabling a more comprehensive and accurate interpretation of satellite imagery data. Finally, we evaluate our results not only by providing a confusion matrix but also through a visual comparison between the class map and the RGB composition of the MSI image.

关键词： World-View3 Autoencoders Multi-Spectral images Deep Learning Transfer Learning Limited Training Data

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Predicting Joint Movement Trajectories from the ECoG Signal patterns of Apes 13

Predicting Joint Movement Trajectories from the ECoG Signal ...

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Joint 13th international conference on Soft Computing and Intelligent Systems and 25th international Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Park, Chanjin Lee, Jungwoo Hyun, Seungmin Jung, Ho-Jin Yoon, Han Ul Yonsei University Graduate School Department of Computer Science Wonju Korea Republic of Yonsei University Division of Software Mirae Campus Wonju Korea Republic of

ISBN: (纸本)9798350373332

Due to a strong heterogeneity between two signals, it is often a challenging problem to obtain an analytical model between brain signals and joint movements. this paper proposes an approach to predicting joint movement from electrocorticography via a convolutional neural network. First, spectrogram images were obtained in sequence from the measured ECoG of an ape by sliding a time-window. Next, the corresponding joint movement of the ape was extracted from video recording by applying DeepLabCut. Finally, the spectrogram images and the joint movement were utilized as an input and a desired output to train the convolutional neural network, respectively. the result showed the feasibility of the proposed approach to model a relationship between the electrocorticography and the joint movements. © 2024 IEEE.

关键词： Convolutional neural networks

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CHARACTERIZING RESIDENTIAL BUILDING patterns IN HIGH-DENSITY CITIES USING GRAPH CONVOLUTIONAL NEURAL NETWORKS 29th

CHARACTERIZING RESIDENTIAL BUILDING PATTERNS IN HIGH-DENSITY...

引用

29th international conference of the Association-for-computer-Aided-Architectural-Design-Research-in-Asia (CAADRIA)

作者： Jia, Muxin Zhang, Kaiheng Narahara, Taro New Jersey Inst Technol Newark NJ 07102 USA

ISBN: (纸本)9789887891826

In urban morphology studies, accurately classifying residential building patterns is crucial for informed zoning and urban design guidelines. While machine learning, particularly neural networks, has been widely applied to urban form taxonomy, most studies focus on grid-like data from street-view images or satellite imagery. Our paper provides a novel framework for graph classification by extracting features of clustering buildings at different scales and training a spectral-based GCN model on graph-structured data. Furthermore, from the perspective of urban designers, we put forward corresponding design strategies for different building patterns through data visualization and scenario analysis. the findings indicate that GCN has a good performance and generalization ability in identifying residential building patterns, and this framework can aid urban designers or planners in decision-making for diverse urban environments in Asia.

关键词： Urban morphology Machine learning Building pattern classification Graph convolutional neural networks

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Classification of Various Daily Behaviors in Lifelog Using Deep Learning 18

Classification of Various Daily Behaviors in Lifelog Using D...

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18th international conference on Future Networks and Communications (FNC) / 20th international conference on Mobile Systems and Pervasive Computing (MobiSPC) / 13th international conference on Sustainable Energy Information Technology (SEIT)

作者： Liu, Guoqi Cui, Wenchao Niu, Siqi Ma, Jian Wang, Ziyi Shenyang Jianzhu Univ Sch Comp Sci & Engn Shenyang 110168 Peoples R China Shenyang Jianzhu Univ Sch Design & Art Shenyang Peoples R China Shenyang Jianzhu Univ Coll Engn Training & Innovat Shenyang Peoples R China Shenyang Jianzhu Univ Sch Transportat & Geomat Engn Shenyang Peoples R China

In the information age, the analysis of human behavioral patterns is of significant interest, and extensive data collection is crucial in context of privacy protection. From 2011 to 2023, the authors collaborated with more than 20 volunteers to construct and collect 30,791 lifelog using mobile devices;these lifelog included a sizable amount of multi-type data containing behavioral activity information, temporal location information, textual description information, and media images. In this paper, we propose a DCP-BiLSTM (Description + Cluster + Position) And Bidirectional Long-Short Term Memory (BiLSTM) personal big data behavior classification model for the lifelog dataset, which uses text description information + GPS information classified by DB-SCAN + location information, it is validated on Liulifelog to better address the behavior prediction problem of lifelog data. Unlike the traditional text classification approach, which is based on a unique lifelog dataset, BiLSTM can combine bidirectional semantic variables comprising description information and location information into the DCP-BiLSTM model. Evaluation of the DCP-BiLSTM demonstrates that can increase the performance and accuracy of behavior prediction, and the BiLSTM that incorporates description and location information to build, not only identifies daily user activity but also corrects the bias caused by manual analysis's one-sidedness and has greater prediction performance. (c) 2023 the Authors. Published by Elsevier B.V.

关键词： Activity analysis personal big data lifelog deep bidirectional long-shot term memory

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Hyperspectral Image analysis Using Maximum Abundance Classification 13

Hyperspectral Image Analysis Using Maximum Abundance Classif...

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13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Zhang, Nian Rouamba, Sougre-Nonma Mahmoud, Wagdy H. thompson, Lara A. Department of Electrical and Computer Engineering University of the District of Columbia WashingtonDC United States

ISBN: (纸本)9798331516147

this paper presents a satellite hyperspectral image processing method that utilizes a maximum abundance classifier to categorize different regions of hyperspectral images into ground truth classes. First, the class names for each endmember and their corresponding columns in the signature matrix are identified, followed by the visualization of their spectral profiles. Abundance maps for the endmembers are then generated using the fully constrained least squares (FCLS) method. Afterward, the maximum abundance classifier is applied, and the resulting classified image is displayed with color-coded pixels. the abundance maps illustrate the spatial distribution of endmembers across the hyperspectral image, where the abundance values of each pixel represent the proportion of each endmember present. By determining the highest abundance value for each pixel and assigning it to the corresponding endmember class, the pixels within the hyperspectral images are classified. Experimental results demonstrate that the proposed MAC method effectively handles mixed pixels. In addition, it can effectively deal with the mixed pixel problem in hyperspectral images because it identifies components by calculating the abundance values for each pixel rather than relying solely on single spectral features. © 2025 IEEE.

关键词： Image segmentation

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Real Data Center Network Traffic Dataset and analysis 13

Real Data Center Network Traffic Dataset and Analysis

引用

13th IEEE international conference on Cloud Networking, CloudNet 2024

作者： Polak, Michal Sedlak, Daniel Fesl, Jan Tvrdik, Pavel Fit Ctu in Prague Department of Computer Systems Prague Czech Republic

ISBN: (纸本)9798350376562

Data centers create the backbone of the modern Internet. However, internal network traffic characteristics are closed know-how of data centers. We have collected an internal network traffic analysis based on the data from one of major world data centers. We have analysed 4 internal network traffic characteristics (clustering of IP addresses, application traffic patterns, frequency of changes in cluster topologies and histograms of communicating IP address pairs). the data set has been published at Github. © 2024 IEEE.

关键词： Data centers

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Protein Secondary Structure Prediction using N-Grams and 1-Dimensional Convolutional Neural Network 13

Protein Secondary Structure Prediction using N-Grams and 1-D...

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Joint 13th international conference on Soft Computing and Intelligent Systems and 25th international Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Rizqiana, Annisa Afiahayati Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350373332

In this study, we initiated an approach to predict Protein Secondary Structure Prediction (PSSP) by using n-grams modeling, namely n adjacent amino acid sequences, to represent the amino acid sequence and 1-Dimensional Convolutional Neural Network (CNN). We compare n-grams with a one-hot encoding approach and find that n-grams provide better performance. the analysis results show that bigrams are more effective than trigrams in describing protein amino acid sequence patterns. We also combine it with the PSSM profile feature to improve model performance. the results of this study reveal that the n-grams approach, especially bigrams, in combination with PSSM, can produce more accurate protein secondary structure predictions than previous models, with superior accuracy evaluations of Q8 68.1 % and Q3 82.75% on the CB513 dataset. © 2024 IEEE.

关键词： Prediction models

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Deep Learning Based CAD System for Early Detection of Brain Tumor 13

Deep Learning Based CAD System for Early Detection of Brain ...

引用

13th international conference on Information Communication and Management (ICICM)

作者： Abdel-Malak, Ereny Magdy Hassan, Ashraf Yehya Mohamed, Wael Abdel-Rahman Tanta Higher Inst Engn & Technol Tanta Egypt Univ Benha Banha Egypt

ISBN: (纸本)9798400708114

A Brain tumor is an uncontrolled development of synapses that happens in mind malignant growth on the off chance that it isn't identified at a beginning phase. Early brain tumor identification is essential for both patient survival and treatment planning. Brain tumors arrive in various shapes, sizes, properties, and treatments. As a result, Manual brain tumor detection is time-consuming, complicated, and error-prone. As a result of this, high-precision automated computer-aided diagnosis (CAD) is currently in demand. Using image processing techniques in magnetic resonance imaging (MRI) is a difficult but important task for a variety of medical analysis applications. Because each brain imaging method provides unique and important details about every part of the tumor, this paper used deep learning technology, especially the Convolutional Neural Network (CNN) model, to aid in the early detection of brain tumors and rapid diagnosis due to the importance of this disease and the increasing number of people infected with it annually, which helps reduce the death rate. this technology applied to MRI images via two methods. We used two types of datasets consisting of 3517 images. We increased the accuracy rate in detecting this tumor, and we have recorded the results and obtained an accuracy of 99.8% in this research paper.

关键词： Brain Tumor computer-Aided Diagnosis Magnetic Resonance Imaging Deep Learning Convolutional Neural Network

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Non-Invasive Breast Cancer Screening: Leveraging InceptionV3 and GoogLeNet for thermal Image analysis 13

Non-Invasive Breast Cancer Screening: Leveraging InceptionV3...

引用

13th international conference on computer Technologies and Development, TechDev 2024

作者： Jalloul, Reem Chethan, H.K. Alkhatib, Ramez Maharaja Institute of Technology Mysore Department of Computer Science & Engineering Karnataka India Leibniz Lung Center Forschungszentrum Borstel Biomaterial Bank Nord Borstel Germany

ISBN: (纸本)9798331539771

Breast cancer remains a leading cause of mortality worldwide, highlighting the need for early detection. this research demonstrates the potential of combining thermal imaging and deep learning for non-invasive, cost-effective breast cancer screening. this study applies deep learning models, specifically InceptionV3 for feature extraction and classifiers such as GoogLeNet, DenseNet121, and MobileNetV2, to analyze thermal images for breast cancer diagnosis. Using the Database for Mastology Research (DMR), thermal images were preprocessed and augmented to improve the dataset. Transfer learning with pre-trained models enhanced diagnostic accuracy. the InceptionV3 + GoogLeNet model achieved the highest performance with an accuracy of 96.49%, an AUC of 99.72%, a precision of 98.25%, and an F1-score of 96.55%, outperforming other combinations. Despite the promising results, the study has limitations, including a relatively small dataset and the need for further validation. Nevertheless, it paves the way for improved early detection strategies, potentially reducing breast cancer mortality rates globally. © 2024 IEEE.

关键词： Lung cancer

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Bi-clustering of Binary Data via Neurodynamics-driven Binary Matrix Factorization 13

Bi-clustering of Binary Data via Neurodynamics-driven Binary...

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13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Li, Hongzong Wang, Jun Department of Computer Science City University of HongKong Kowloon Hong Kong Department of Computer Science Department of Data Science City University of HongKong Kowloon Hong Kong

ISBN: (纸本)9798331516147

Bi-clustering, also known as co-clustering, is a powerful data analysis technique that simultaneously clusters rows and columns of a data matrix, revealing hidden patterns. In this paper, we propose a neurodynamics-driven binary matrix factorization approach for bi-clustering binary data. the proposed method utilizes multiple discrete Hopfield networks operating concurrently to explore local optimal solutions. Furthermore, a particle swarm optimization rule is iteratively applied to reinitialize the neuronal states for directing the search toward better solutions. Comparative evaluations across six benchmark datasets reveal that the proposed method outperforms five existing methods in terms of five internal and external indices. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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