this paper describes a computer-aided system for analyzing immunohistochemically stained meningioma cancer cell images. Accurate segmentation of cells in such images plays a critical role in diagnosing diffrent type o...
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this book constitutes the refereed proceedings of the 6th International conference on Recent Trends in imageprocessing and Pattern Recognition, RTIP2R 2023, held in Derby, UK, during December 2023, in collaboration w...
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ISBN:
(数字)9783031530852
ISBN:
(纸本)9783031530845
this book constitutes the refereed proceedings of the 6th International conference on Recent Trends in imageprocessing and Pattern Recognition, RTIP2R 2023, held in Derby, UK, during December 2023, in collaboration withthe Applied AI Research Lab at the University of South Dakota.;the 62 full papers included in this book were carefully reviewed and selected from 216 submissions. the papers are organized in the following topical sections:;Volume I:;Artificial intelligence and applied machine learning; applied imageprocessing and pattern recognition; and biometrics and applications.;Volume II:;Healthcare informatics; pattern recognition in blockchain, IOT, cyber plus network security, and cryptography.
the image segmentation plays an important role in medical imageprocessing. Many segmentation algorithms exist. Most of them produce raster data which is not suitable for further 3D geometrical modeling of tissues. In...
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ISBN:
(纸本)0889865981
the image segmentation plays an important role in medical imageprocessing. Many segmentation algorithms exist. Most of them produce raster data which is not suitable for further 3D geometrical modeling of tissues. In this paper, a vector segmentation algorithm based on an adaptive Delaunay triangulation is proposed. Triangular meshes are used to divide an image into several non-overlapping regions whose characteristics are similar. Novel methods for improving quality of the mesh and its adaptation to the image structure are also presented.
Few-shot image classification is a critical issue in the field of computervision, facing challenges related to data scarcity and model generalization. Transformer models, representing self-attention mechanisms, have ...
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ISBN:
(纸本)9798350349122;9798350349115
Few-shot image classification is a critical issue in the field of computervision, facing challenges related to data scarcity and model generalization. Transformer models, representing self-attention mechanisms, have made significant strides in recent years in the domain of few-shot classification. this paper commences with an introduction to the background and challenges of few-shot classification, along with a description of the principles and structure of the Transformer model. Subsequently, the paper categorizes Transformer-based few-shot image classification methods into meta-learning-based, metric-learning-based, fine-tuning-based, and feature-enhancement-based approaches, whose theoretical foundations of each method are expounded and the comparative analysis of representative algorithms are also provided. Furthermore, the paper delves into prospective research directions in this field.
this book constitutes the refereed proceedings of the 6th International Symposium on Advances in Signal processing and Intelligent Recognition Systems, SIRS 2020, held in Chennai, India, in October 2020. Due to the CO...
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ISBN:
(数字)9789811604256
ISBN:
(纸本)9789811604249
this book constitutes the refereed proceedings of the 6th International Symposium on Advances in Signal processing and Intelligent Recognition Systems, SIRS 2020, held in Chennai, India, in October 2020. Due to the COVID-19 pandemic the conference was held online.
Brain tumour segmentation is a fundamental task in medical image analysis where each year a number of deep learning models are introduced to delineate the tumour regions with high precision. However, most of these wor...
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ISBN:
(纸本)9798400710759
Brain tumour segmentation is a fundamental task in medical image analysis where each year a number of deep learning models are introduced to delineate the tumour regions with high precision. However, most of these works rely on large number of parameters and higher computational cost, thus, rendering them ineffective in real world application. therefore, it is important to devise efficient models that can be easily deployed on resource-constrained devices and perform at par withthe existing large models. In this paper, we propose a semi-decoupled distillation technique which trains a lightweight "student" model using the features extracted from the decoder of the nnU-Net "teacher" model and its predictions on these features using a single point-wise convolution layer. the final classification layer remains the same for boththe models and is kept frozen while training the "student" network. Our approach follows a two stage training procedure where the tumour regions are detected and extracted in the first step, and then sent for second stage training to segment fine-grain classes, including edema, enhanced tumour and tumour core. Our extensive experimentation shows that a lightweight distilled model performs competitively with large models on brain tumour segmentation.
the prohibitive amounts of time required to review the large amounts of data captured by surveillance and other cameras has brought into question the very utility of large scale video logging. Yet, one recognizes that...
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Detecting motion pattern in dynamic crowd scenes is a challenging problem in computervision field. In this paper, we propose a novel approach to detect the motion patterns from global perspective. To extract the disc...
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this book constitutes the refereed proceedings of the 6th International conference, ICISP 2014, held in June/July 2014 in Cherbourg, France. the 76 revised full papers were carefully reviewed and selected from 164 sub...
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ISBN:
(数字)9783319079981
ISBN:
(纸本)9783319079974
this book constitutes the refereed proceedings of the 6th International conference, ICISP 2014, held in June/July 2014 in Cherbourg, France. the 76 revised full papers were carefully reviewed and selected from 164 submissions. the contributions are organized in topical sections on multispectral colour science, color imaging and applications, digital cultural heritage, document image analysis, graph-based representations, image filtering and representation, computervision and pattern recognition, computergraphics, biomedical, and signal processing.
Scene Graph Generation has gained much attention in computervision research withthe growing demand in image understanding projects like visual question answering, image captioning, self-driving cars, crowd behavior ...
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