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$k$-Subspace Method

$k$-Subspace Method

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Consumer Technology (ICCT-Pacific), international conference on

作者： Yasuhito Fujii Yoriaki Murata Hitoshi Sakano Interdisciplinary Faculty of Science and Engineering Shimane University Matsue Japan Graduate School of Natural Science and Technology Shimane University Matsue Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

We propose the k-subspace method, a clustering method based on the subspace method, and the k-subspace classification method, a pattern recognition method that utilizes multiple subspaces for each class. Conventional clustering algorithms, such as the k-means method, operate based on centroids. However, in image recognition, subspaces provide a more essential representation. Existing techniques, such as CLAFIC, define a single subspace per class. However, real-world data often exhibit significant intra-class variations, such as diverse character styles within the same class. We hypothesize that partitioning subspaces into subsets, which account for intra-class variations, will enhance classification accuracy. In this study, we examine the feasibility of partitioning a sub-space into $k$ subclasses. We apply the proposed method to image recognition and experimentally demonstrate its effectiveness.

关键词： Handwriting recognition image recognition machine learning algorithms Clustering algorithms Training data machine learning Solids Classification algorithms Partitioning algorithms Convergence

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U-Net Architecture for Detecting Glaucoma with Retinal Fundus images 1

U-Net Architecture for Detecting Glaucoma with Retinal Fundu...

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1st international conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Nandhini, S. Jagadeesan, J. Anitha, E. Vinayaka Mission's Research Foundation Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Tamil Nadu India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India Loyola - ICAM College of Engineering and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350342796

Ophthalmologists use retinal images to determine eye disease, for example glaucoma disease. This diagnosis is made by measuring changes in the cup-to-disc ratio. Eye Cup Segmentation Helps Fossil Ophthalmologists Calculate CDRs of Retinal Background images. In this article, we propose a machine learning approach to perform segmentation tasks using the U-Net architecture. This proposed technique was evaluated using 650 colour retinal fundus images. Next, U-Net has 160 opportunities. Additionally, we compared the segmentation results with ground reality images. According to the test results, the eyecup's segmentation performance reaches 98.42% of the membrane, with a loss of 0.15. Fundus images help detect glaucoma. In the past, several experiments have been conducted to detect this disease. This paper proposes an ideal framework aimed at building intelligent solutions that help identify glaucoma using MATLAB software tools. The diagnosis of glaucoma is made by measuring changes in the bowl-to-disc ratio. Eye Cup Segmentation Helps Fossil Ophthalmologists Calculate CDRs of Retinal Background images. As we propose a deep learning method using U-Net architecture to perform segmentation tasks, we collected many fundus images as data. Subsequent pre-processing of the occlusion figure is accompanied by classification and segmentation. The pre-processing module uses several filters such as Gaussian filter and guided image filter to remove noise. The division into healthy and sick eyes helps with further administration. © 2023 IEEE.

关键词： Deep learning Glaucoma detection KNN Algorithm machine learning U-Net Architecture

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Diabetic Retinopathy Segmentation in IDRiD using Enhanced U-Net 1

Diabetic Retinopathy Segmentation in IDRiD using Enhanced U-...

引用

1st IEEE international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Agarwal, Rashi Harcourt Butler Technical University Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350316469

Diabetes Retinopathy is the primary reason for avoidable vision loss, especially among people of working age. Diabetes is expected to affect 600 million people worldwide by 2040, with one-third developing Diabetic Retinopathy [1] Recent studies have highlighted the need for more cost-effective and efficient methods of diagnosing, managing, and treating retinal diseases in clinical eye care settings. Before the development of a computer-aided diagnosis tool, the difficulty of achieving an accurate early diagnosis at an affordable cost must be considered. It may be possible to expand diabetic retinopathy screening efforts and allow physicians to use their time more efficiently by utilizing computer-aided disease detection in retinal image processing. Biomedical engineers and computer scientists now have a unique opportunity to meet the demands of clinical practice, thanks to advances in edge computing and machine learning techniques. We have used Enhanced U-Net to segment typical pathological features of DR which include hard exudates (EX), soft exudates (SE), microaneurysms (MA), and haemorrhages (HE) in the Indian Diabetic Retinopathy image Dataset (IDRiD) with better accuracy than other techniques used in this domain. The Enhanced U-Net is able to segment all the features at an early stage, hence, this method can be used to aid diagnosis of DR at an early stage, thereby preventing vision loss at later stages. © 2023 IEEE.

关键词： Deep learning Diabetic Retinopathy U-Net with ResNet

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Proceedings of 2022 1st IEEE international conference on Industrial Electronics: Developments and Applications, ICIDeA 2022

Proceedings of 2022 1st IEEE International Conference on Ind...

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1st IEEE international conference on Industrial Electronics: Developments and Applications, ICIDeA 2022

ISBN: (纸本)9781665421492

The proceedings contain 44 papers. The topics discussed include: real-time dog detection and alert system using Tensorflow lite embedded on edge device;square wave generator using second generation current controlled conveyor (CCCII);ensembled transfer learning models for real time face recognition;a detailed review on battery cooling systems for electric vehicles;applications of machine learning in healthcare: an overview;VLSI implementation of accuracy configurable Radix-4 adder for digital image processing applications;a universal controller for grid-forming inverters in microgrid during islanding for low transient current;on projection of safe operation for grid-following inverters - grid parameter estimation;dynamic load inrush current mitigation in islanded microgrids powered by grid-forming inverters;a comparative review on electric vehicles and hybrid vehicles;and implementation of multi bit error detection and correction using low density parity check codes.

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image processing Based Security Management System 1

Image Processing Based Security Management System

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1st IEEE international Interdisciplinary Humanitarian conference for Sustainability, IIHC 2022

作者： Gehlot, Anita Singh, Rajesh Negi, Poonam Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun248007 India School of Applied and Life Science Uttaranchal University Uttarakhand Dehradun248007 India

ISBN: (纸本)9781665456876

Many industries nowadays struggle with serious security issues, thus hiring security guards is necessary to accomplish security. People make many mistakes as human beings, such as missing targets, and they only have so much energy (they are not able to monitor your environment continuously). These factors may have an impact on the level of security. The methods used worldwide to acquire facial detection and facial recognition also fall under the category of image processing, which is a subset of machine learning and is classified as an aspect of artificial intelligence. In security-sensitive regions, the facial recognition technology may identify a person and lessen the need for men to patrol. A series of photos are taken by the camera sensor as soon as a person enters a highly protected location, and these snapshots are analysed and compared to any previous images in the data set of an authorised individual. The key reason for image processing is that, in the future, this system will handle identification and verification for improved security problem accuracy and transparency. This project is being developed to let authorised users access to restricted locations. © 2022 IEEE.

关键词： Face recognition

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Back to the Future: Challenges of Sparse and Irregular Medical image Time Series

Back to the Future: Challenges of Sparse and Irregular M...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Disch, Nico Albert Peretzke, Robin Roy, Saikat Ulrich, Constantin Zimmerer, David stiefelhagen, Rainer Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Karlsruhe Institute of Technology Karlsruhe Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany

ISBN: (纸本)9783031845246

In longitudinal medical image analysis, most work focuses on regularly sampled images, or on tasks like regression or classification. However, in the clinical context, images are frequently generated irregularly due to factors such as cost constraints. This work tackles the problem of irregularly sampled longitudinal medical imaging by evaluating methods in segmentation and reconstruction tasks. We examine the reconstruction for real-life MRI from patients with Alzheimer’s Disease (AD), where the model predicts future MRI scans from an arbitrary number of input images sampled at irregular intervals. However, experiments show that most models cannot surpass the performance of using an image from the same patient at a different time point as a baseline. Therefore, we conducted experiments on a synthetic dataset to better isolate effects in temporal learning. These experiments offer insights into model behavior and serve as a benchmark for validating models’ longitudinal learning capability, thus providing a straightforward method to assess temporal understanding in a controlled environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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machine learning Approaches to Classify Indian Mango Varieties 1st

Machine Learning Approaches to Classify Indian Mango Varieti...

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1st international conference on Recent Advancements in Materials Science and Technology, ICRAMst 2024

作者： Febin, U. Pradeep, R. Nishath, S. M. Haji Arafat, I. Sheik Department of ECE College of Engineering Trivandrum Kerala Thiruvananthapuram India Department of AI DS Mohamed Sathak Engineering College Tamil Nadu Kilakarai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9783031699658

Mango is one of the most common tropical fruits and is known as king of fruits. It is the most loved fruit in India. Currently, the classification of mangoes is still primarily done through manual inspection, which makes it difficult to automate, time-consuming, and unreliable. So, the classification of mango fruit using image processing, computer vision, and artificial intelligence methods is needed. This methodology helps the mango industry in increasing productivity, profitability, and export potential. In this study, we introduce an effective machine vision system for better classification which can reduce the wastage and at the same time increase the marketing of mango export. In this paper, the mangoes are classified using a convolutional neural network (CNN). In this study, we classify mango images into 10 categories: Alphonso, Banganapalli, Chausa, Dasheri, Kesar, Malgova, Mallika, Neelam, Raspuri, and Totapuri. Natural image preprocessing is essential to image processing since unprocessed natural images might lead to inaccurate classification if they are fed straight into the CNN model. Thus, an improper classification can result. In the experiment section, the natural photos of mangoes were first preprocessed, and the convolution neural network was then fed with these preprocessed images as input for the classification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fruits

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VIPERC 2022 - Proceedings of the 1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding

VIPERC 2022 - Proceedings of the 1st International Virtual C...

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1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2022

The proceedings contain 12 papers. The topics discussed include: Abjad numerals recognition in medieval Arabic mathematical texts;Ancient coin classification based on recent trends of deep learning;a shallow neural net with model-based learning for the virtual restoration of recto-verso manuscripts;cultural heritage image classification using transfer learning for feature extraction: a comparison;empirical performance analysis of classification methods on cultural heritage database;searching for cultural relationships through deep learning models;exploring digital tourism application for medieval period reconstruction;application of the digital twin concept in cultural heritage;BIM-FM integrated solution resourcing to digital techniques;usability impact of adaptive culture in smart phones;and a knowledge representation framework for managing Leonardo Da Vinci's Mona Lisa: case study of the hidden painting.

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Embedded Intelligence for Safety and Security machine Vision Applications 21st

Embedded Intelligence for Safety and Security Machine Vision...

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21st international conference on image Analysis and processing (ICIAP)

作者： Lioupis, Panagiotis Dadoukis, Aris Maltezos, Evangelos Karagiannidis, Lazaros Amditis, Angelos Gonzalez, Maite Martin, Jon Cantero, David Larranaga, Mikel Inst Commun & Comp Syst ICCS Zografos 15773 Greece Fdn Tekniker Inaki Goenaga 5 Eibar 20600 Spain

ISBN: (纸本)9783031133244;9783031133237

Artificial intelligence (AI) has experienced a recent increase in use across a wide variety of domains, such as image processing for security applications. Deep learning, a subset of AI, is particularly useful for those image processing applications. Deep learning methods can achieve state-of-the-art results on computer vision for image classification, object detection, and face recognition applications. This allows to automate video surveillance reducing human intervention. At the same time, although deep learning is a very intensive task in terms of computing resources, hardware and software improvements have emerged, allowing embedded systems to implement sophisticated machine learning algorithms at the edge. Hardware manufacturers have developed powerful co-processors specifically designed to execute deep learning algorithms. But also, new lightweight open-source middleware for constrained resources devices such as EdgeX foundry have emerged to facilitate the collection and processing of data at sensor level, with communication capabilities to cloud enterprise applications. The aim of this work is to show and describe the development of Smart Camera Systems within S4AllCities H2020 project, following the edge approach.

关键词： Horizon2020 Edge EdgeX foundry machine vision Artificial intelligence Deep learning

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learning Semantics for Visual Place recognition Through Multi-scale Attention 21st

Learning Semantics for Visual Place Recognition Through Mult...

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21st international conference on image Analysis and processing (ICIAP)

作者： Paolicelli, Valerio Tavera, Antonio Masone, Carlo Berton, Gabriele Caputo, Barbara IIT Genoa Italy Politecn Torino Turin Italy CINI Rome Italy

ISBN: (纸本)9783031064302;9783031064296

In this paper we address the task of visual place recognition (VPR), where the goal is to retrieve the correct GPS coordinates of a given query image against a huge geotagged gallery. While recent works have shown that building descriptors incorporating semantic and appearance information is beneficial, current state-of-the-art methods opt for a top down definition of the significant semantic content. Here we present the first VPR algorithm (Code and dataset are available at: https:// ***/valeriopaolicelli/SegVPR) that learns robust global embeddings from both visual appearance and semantic content of the data, with the segmentation process being dynamically guided by the recognition of places through a multi-scale attention module. Experiments on various scenarios validate this new approach and demonstrate its performance against state-of-the-art methods. Finally, we propose the first synthetic-world dataset suited for both place recognition and segmentation tasks.

关键词： Visual place recognition Segmentation Attention

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