Polarization is the asymmetry of a light wave's electric field vibration direction with respect to its propagation direction, as a crucial clue for describing the light, which is one of the most apparent signs tha...
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In the realm of patternrecognition, decoding handwritten characters remains a focal point, drawing enduring interest from researchers across various domains. Optical Character recognition (OCR) stands as a pivotal te...
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ISBN:
(纸本)9789819720811;9789819720828
In the realm of patternrecognition, decoding handwritten characters remains a focal point, drawing enduring interest from researchers across various domains. Optical Character recognition (OCR) stands as a pivotal technique in imageprocessing, extracting text, and identifying language from digital images. While OCR has made strides in Arabic and Chinese, Indian scripts lack comprehensive exploration. this paper addresses this gap, delving into OCR methodologies for Indian scripts and multiple languages. By scrutinizing each language, it unravels the intricacies and challenges of character recognition. the study offers a comprehensive view of OCR techniques' adaptability and effectiveness in diverse linguistic contexts. this research marks a significant contribution by extensively exploring OCR's application to Indian scripts and their languages, filling a notable void in current understanding within this domain.
Accurate building footprint extraction from optical remote sensing images remains challenging due to the diverse appearance and complex scenarios. Although recent deep learning-based methods have been shown to greatly...
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Machine vision target detection can be mainly divided into traditional detection methods represented by Hof circle detection and template matching, and detection methods based on deep learning. Traditional target dete...
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the growing number of satellites in orbit has resulted in a rise in defunct satellites and space debris, posing a significant risk to valuable spacecraft like normal satellites and space stations. therefore, the remov...
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this research discusses the pre-trained deep learning architecture for the multimodal learning and representation in surveillance system. this framework generates a single image from the integration of the multi senso...
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ISBN:
(纸本)9783031235986;9783031235993
this research discusses the pre-trained deep learning architecture for the multimodal learning and representation in surveillance system. this framework generates a single image from the integration of the multi sensor information, which includes the infrared and visible. We use visible and infrared as the information in different spectrum of light, in term of contrast ratio and visibility. We start withimage registration to align coordinates so the decomposition of the source image into the sub bands is possible. the VGG-19 and the weighted averaging are utilized for the feature extraction and transfer learning task. this is conducted thorough empirical research by implementing a series of methodology studies to evaluate how pre-trained deep learning techniques enhance overall fusion performance and improve recognition and detection capability. this study also contains a comparison of the performance of spatial and frequency algorithms in contrast to the deep learning based method for the surveillance system. the research work is concluded by evaluating the performance measure of the proposed fusion algorithm withthe traditional algorithm.
the limitations of domain dependence in neural networks and data scarcity are addressed in this paper by analyzing the problem of semi-supervised medical image classification across multiple visual domains using a sin...
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ISBN:
(纸本)9783031235986;9783031235993
the limitations of domain dependence in neural networks and data scarcity are addressed in this paper by analyzing the problem of semi-supervised medical image classification across multiple visual domains using a single integrated framework. Under this premise, we learn a universal parametric family of neural networks, which share a majority of their weights across domains by learning a few adaptive domain-specific parameters. We train these universal networks on a suitable pretext task that captures a meaningful representation for image classification and further fine-tune the networks using a small fraction of training data. We perform our experiments on five medical datasets spanning breast, cervical, and colorectal cancer. Extensive experiments on architectures of domain-adaptive parameters demonstrate that our data-deficient universal model performs equivalently to a fully supervised setup, rendering a semi-supervised multi-domain setting with lower numbers of training samples for medical data extremely feasible in the real world.
A machine needs to recognize orientation in an image to address various rotation related problems. To calculate this rotation, one must require the information about different objects that present into the image. Henc...
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ISBN:
(纸本)9783031235986;9783031235993
A machine needs to recognize orientation in an image to address various rotation related problems. To calculate this rotation, one must require the information about different objects that present into the image. Hence this becomes a patternrecognition task. By using Deep Learning this issue of calculation of image rotation can be addressed as deep learning possess excellent ability of feature extraction. this paper proposes a novel deep learning-based approach to estimate the angle of rotation very efficiently. Kaggle dataset (Rotated Coins) and Caltech256 has been used for this research, but the data available was limited hence this research utilize data augmentation by rotating the given dataset at random angles. Initially the unlabeled image has been rotated at different angles and store the values to be used as training dataset. Finally at the output a regression layer has been used to identify the angle of rotation for input image. the proposed deep learning approach provides a better result in terms of validation parameters like R-square, MSE, MAE. With proposed approach the value of R-square, MSE, and MAE for Kaggle dataset (Rotated Coins) obtained is 0.9846, 0.0013 and 0.0127 respectively. While forCaltech-256 Dataset proposed approach reportedR-square, MSE, and MAE of 0.9503, 0.0039 and 0.0240 respectively. the proposed approach also helps in finding the position of an object by calculating the angle of rotation in an image.
In a military scenario, targets release decoy projectiles to achieve cover, then escape, evading the attack range of the guidance system. therefore, recognizing target behaviors such as releasing decoys, escaping, and...
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