There are many problems inside the field of biomedical image analysis that can be dealt from a topological (and geometric) point of view. One of them refers to the way in which cells are self-organised inside a tissue...
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ISBN:
(纸本)9783030766566;9783030766573
There are many problems inside the field of biomedical image analysis that can be dealt from a topological (and geometric) point of view. One of them refers to the way in which cells are self-organised inside a tissue, mainly motivated by the changes that may occur in such an organization in case of disease. This problem can be faced from different perspectives in terms, first, of how to model the cells and their 'connections' and second, how to computationally characterise their organization. We will discuss some topological approaches to this topic as well as future lines of research.
Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians. With a large number of well-defined classes, cluttered and noisy samples, different types of representation...
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ISBN:
(纸本)9781728188089
Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians. With a large number of well-defined classes, cluttered and noisy samples, different types of representations, both subtle differences between classes and high intra-class variation, historical watermarks are also challenging for patternrecognition. In this paper, overcoming the difficulty of data collection, we present a large public dataset with more than 6k new photographs, allowing for the first time to tackle at scale the scenarios of practical interest for scholars: one-shot instance recognition and cross-domain one-shot instance recognition amongst more than 16k fine-grained classes. We demonstrate that this new dataset is large enough to train modern deep learning approaches, and show that standard methods can be improved considerably by using mid-level deep features. More precisely, we design both a matching score and a feature fine-tuning strategy based on filtering local matches using spatial consistency. This consistency-based approach provides important performance boost compared to strong baselines. Our model achieves 55% top-1 accuracy on our very challenging 16,753-class one-shot cross-domain recognition task, each class described by a single drawing from the classic Briquet catalog. In addition to watermark classification, we show our approach provides promising results on fine-grained sketch-based image retrieval.
Human Body Dimensions Estimation (HBDE) is a task that an intelligent agent can perform to attempt to determine human body information from images (2D) or point clouds or meshes (3D). More specifically, if we define t...
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ISBN:
(纸本)9783031133244;9783031133237
Human Body Dimensions Estimation (HBDE) is a task that an intelligent agent can perform to attempt to determine human body information from images (2D) or point clouds or meshes (3D). More specifically, if we define the HBDE problem as inferring human body measurements from images, then HBDE is a difficult, inverse, multitask regression problem that can be tackled with machinelearning techniques, particularly convolutional neural networks (CNN). Despite the community's tremendous effort to advance human shape analysis, there is a lack of systematic experiments to assess CNNs estimation of human body dimensions from images. Our contribution lies in assessing a CNN estimation performance in a series of controlled experiments. To that end, we augment our recently published neural anthropometer dataset by rendering images with different camera distance. We evaluate the network inference absolute and relative mean error between the estimated and actual HBDs. We train and evaluate the CNN in four scenarios: (1) training with subjects of a specific gender, (2) in a specific pose, (3) sparse camera distance and (4) dense camera distance. Not only our experiments demonstrate that the network can perform the task successfully, but also reveal a number of relevant facts that contribute to better understand the task of HBDE.
The proceedings contain 35 papers. The special focus in this conference is on Mining Humanistic Data. The topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Canc...
ISBN:
(纸本)9783031083402
The proceedings contain 35 papers. The special focus in this conference is on Mining Humanistic Data. The topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable Interactive Medical image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular Systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of stake;Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making;employing Natural Language processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-Data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative Software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic images;An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI images;The Use of Robotics in Critical Use Cases: The 5G-ERA Project Solution;fundamental Features of the Smart5Grid Platform Towards Realizing 5G Implementation;experimentation Scenarios for machinelearning-Based Resource Management;efficient Data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power Grids: Millisecond Level Precise Distributed Generation Monitoring and Real-Time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep learning Based Facial Expressions An
In order to recognize patterns in images, this study tests the performance of many “machinelearning algorithms” and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare ...
In order to recognize patterns in images, this study tests the performance of many “machinelearning algorithms” and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machinelearning methods (“deep learning, support vector machines, decision trees, and random forests”) and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). The efficacy of each algorithm is measured in terms of its “accuracy, precision, recall, and F1 score”, among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. The results of our research have significant ramifications for the future of machinelearning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machinelearning algorithms.
Speech is a mode for humans to express their emotions. We recognize its ease of use while opting to other means of communication like text, where we frequently utilize emotion to convey our feelings. Emotion detection...
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In this period, X-rays are the main instruments used to examine suspected human fractures. It takes a lot of time to manually examine X-rays, which calls for experienced radiologists or skilled orthopedic surgeons. Th...
In this period, X-rays are the main instruments used to examine suspected human fractures. It takes a lot of time to manually examine X-rays, which calls for experienced radiologists or skilled orthopedic surgeons. The primary care physicians' excessive referrals contribute to the challenging diagnostic environment and the shortage of radiologists at primary/community health centre. Additionally, the lack of skilled radiologists and orthopedic surgeons in medically underdeveloped regions, such as rural India, has driven us to create an automated fracture identification model. Digital imageprocessing is a technique of processing an image with the help of a digital computer. In computer imageprocessing, an edge detection algorithm looks for any kind of edge modifications. In this work, edge detection is used to first identify edges, and it is discovered that canny edge detection is the best option. In second stage, HOG feature extraction method is used to extract features from X-ray images. The images are then categorise using classifiers to distinguish between bones with and without fractures. For classification, the classifiers like SVM (Support Vector machine), KNN (K-Nearest Neighbour), and RF (Random Forest) are utilised. This study strongly suggests SVM for bone fracture detection.
The main objective is to reduce medical and related errors in the detection of brain tumors from MRI scans and to improve the predictive accuracy. This is aimed to be achieved by highly effective tuned models. Accurac...
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Handwritten character segmentation plays a pivotal role in the performance of Optical Character recognition (OCR) systems. This paper introduces an innovative approach to enhancing segmentation accuracy using Region-B...
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ISBN:
(数字)9798350355611
ISBN:
(纸本)9798350355628
Handwritten character segmentation plays a pivotal role in the performance of Optical Character recognition (OCR) systems. This paper introduces an innovative approach to enhancing segmentation accuracy using Region-Based Convolutional Neural Networks (R-CNN). Traditional methods often struggle with issues such as varying character sizes, overlapping, and inconsistent writing styles, leading to suboptimal recognition results. The proposed R-CNN model addresses these challenges by effectively localizing and segmenting individual characters from handwritten text images. The methodology involves training the R-CNN on a comprehensive dataset of handwritten characters, annotated to capture diverse styles and complexities. The model leverages deep learning's feature extraction capabilities and region-based localization to accurately segment characters, even in challenging scenarios like connected or touching characters. Comparative experiments show that the proposed approach outperforms conventional techniques in terms of segmentation accuracy and robustness. Results demonstrate significant improvements, with the R-CNN model achieving higher precision and recall rates across multiple handwritten datasets, including different languages and scripts. This advancement holds promise for enhancing OCR systems used in applications such as automated document processing and digital archiving. Future work will focus on integrating this segmentation method with advanced recognition models to further boost overall handwritten text recognition performance.
Generally, cysts are painless. When ovaries gets enlarged there is a possible for torsion and infertility. To detect the ovarian cyst,ultrasound are used. In this work ultrasound image dataset of ovaries of different ...
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