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 ...
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 machinelearning in healthcare: an overview;VLSI implementation of accuracy configurable Radix-4 adder for digital imageprocessing 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.
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 s...
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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 ma...
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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 ne...
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.
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 tha...
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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 againststate-of-the-art methods. Finally, we propose the first synthetic-world dataset suited for both place recognition and segmentation tasks.
Artificial intelligence (AI) has experienced a recent increase in use across a wide variety of domains, such as imageprocessing for security applications. Deep learning, a subset of AI, is particularly useful for tho...
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ISBN:
(纸本)9783031133244;9783031133237
Artificial intelligence (AI) has experienced a recent increase in use across a wide variety of domains, such as imageprocessing for security applications. Deep learning, a subset of AI, is particularly useful for those imageprocessing 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 machinelearning 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.
3D human pose estimation is a research hotspot. The research on this technology can promote the development of other advanced artificial intelligence technologies based on computer vision, such as sports teaching such...
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In this work, we processed sets of images obtained by the light-sheet fluorescence microscopy method. We selected different cell groups and determined areas occupied by ensembles of cell groups in mouse brain tissue. ...
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ISBN:
(数字)9783030954673
ISBN:
(纸本)9783030954673;9783030954666
In this work, we processed sets of images obtained by the light-sheet fluorescence microscopy method. We selected different cell groups and determined areas occupied by ensembles of cell groups in mouse brain tissue. recognition of mouse neuronal populations was performed on the basis of visual properties of fluorescence-activated cells. Individual elements were selected based on their brightness in grayscale mode. Methods of spatial data processing were applied to identify border areas between ensembles and to calculate topological characteristics of cell groups. By applying cell statistics operations, we obtained the localization of the regions of interest, for subsequent identification of samples with specified topological characteristics. Based on the topological properties of the cell groups, we constructed training samples, and then used these to detect typical sets of ensembles in multi-page TIFF files with optogenetics datasets.
In comparison to other types of cancer, lung cancer has the highest fatality rate, making it one of the most dangerous illnesses in the world. In India, there are over 70,000 new cases recorded each year, which demons...
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The human body's largest organ is the skin. It weighs between six and nine pounds and has a size of about two square yards. The skin serves as a partition separating the body's internal organs from external en...
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ISBN:
(纸本)9798350359756
The human body's largest organ is the skin. It weighs between six and nine pounds and has a size of about two square yards. The skin serves as a partition separating the body's internal organs from external environment. Dermocopy is a technique designed to help diagnose melanoma, a type of cancer that usually starts in melanocytes, the cells that give the skin its color. Skin spots are made more noticeable by photographing and brightening a piece of skin with dermocopy, a non-invasive skin imaging technique. Throughout the world, skin conditions are the most common disorders. Even though it's ubiquitous, recognizing it can be challenging and takes a high level of specialized knowledge. Skin photographs undergo processing to improve their quality and eliminate extraneous noise. The diagnosis report's generation, the photos' classification via the SoftMax classifier algorithm, and the feature extraction procedure utilizing cutting-edge techniques like Convolutional Neural Networks (CNN). The second stage uses algorithms to identify diseases based on the histological features discovered during skin testing. One of the most intricate, unpredictable, and challenging medical specialties to diagnose is dermatology. Comprehensive testing is required in dermatology to determine the potential skin problem a patient may be experiencing on a regular basis. The time may vary depending on the practitioner. This is also based on the experience of that particular person. As a result, a system that can diagnose skin conditions without these restrictions is needed. We propose an automated image-based method that classifies skin disorders using machinelearning. Using computational approaches, this system will process, assess, and relegate the picture data based on various features of the photos. Skin photographs are filtered to remove unnecessary noise and processed to enhance the image quality. Create a diagnosis report by extracting features from the image using advanced techniques
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