this paper presents a method for segmentation of human brain magnetic resonance (MR) image sequences based on a 3D human brain model (triangulated mesh). the brain model is composed of four components, namely, cerebru...
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ISBN:
(纸本)1595930361
this paper presents a method for segmentation of human brain magnetic resonance (MR) image sequences based on a 3D human brain model (triangulated mesh). the brain model is composed of four components, namely, cerebrum, cerebellum, brain stem and pituitary gland. Synthesized image sequences are extracted from the model at regular intervals for sagittal and coronal views as done in MR imaging. To align a series of real MR images withthese synthesized cross-sections, an efficient dynamic programming based computational technique has been used that obtains the optimal synthesized cross-section sequence corresponding to the series of MR images. For automatic segmentation of anatomical structures from the MR images, each aligned synthesized cross-section is overlaid on the corresponding physical MR image by carrying out appropriate geometric transformation. this transformation produces model guided boundaries for four segments corresponding to cerebrum, cerebellum, brain stem and pituitary gland. Subsequently, these initial contours are further refined by the method of active contouring, which provides segmentation of 3D MR images into the above mentioned four parts. the proposed method compares well withthe recently proposed Charged Fluid Model (CFM) based approach and level set segmentation method in terms of accuracy at a significantly lower computational cost. Copyright 2014 ACM.
Most of the past document image watermarking schemes focus on providing same level of integrity and copyright protection for information present in the source document image. However, in a document imagethe informati...
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Since handwriting recognition is very sensitive to structural noise, like superimposed objects such as straight lines and other marks, it is necessary to remove noise in a preprocessing stage before recognition. Altho...
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ISBN:
(纸本)1595930361
Since handwriting recognition is very sensitive to structural noise, like superimposed objects such as straight lines and other marks, it is necessary to remove noise in a preprocessing stage before recognition. Although numerous denoising approaches have been proposed, it remains a challenge. the difficulties are due to non-locality of structural noise and hard discernment between spurious and the meaningful regions. We propose a supervised approach using deep learning to remove structural noise. Specifically, we generalize the deep autoencoder into the deep denoising autoencoder (DDAE), which consists in training a neural network with noisy and clean pairs to minimize cross-entropy error. Inspired by recurrent neural networks, we introduce feedback loop from the output to enhance the "repaired" image well in the reconstruction stage in our framework. We test the DDAE model on three handwritten image data sets, and show advantages over Wiener filter, robust Boltzmann machines and deep autoencoder. Copyright 2014 ACM.
In this paper, a dynamic stochastic resonance (DSR)-based technique has been proposed for contrast enhancement of dark and low contrast images in discrete wavelet transform (DWT) domain. Traditionally, the performance...
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this book constitutes the refereed proceedings of the 6th International conference on Information processing, ICIP 2012, held in Bangalore, India, in August 2012. the 75 revised full papers presented were carefully re...
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ISBN:
(数字)9783642316869
ISBN:
(纸本)9783642316852
this book constitutes the refereed proceedings of the 6th International conference on Information processing, ICIP 2012, held in Bangalore, India, in August 2012. the 75 revised full papers presented were carefully reviewed and selected from 380 submissions. the papers are organized in topical sections on wireless networks; imageprocessing; pattern recognition and classification; computer architecture and distributed computing; software engineering, information technology and optimization techniques; data mining techniques; computer networks and network security.
For challenging visual recognition tasks such as scene classification and object detection there is a need to bridge the semantic gap between low-level features and the semantic concept descriptors. this requires mapp...
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the problem of road segment extraction from high resolution satellite or aerial images has been considered in this paper. Efficient extraction of road segments is a difficult task due to the problems regarding image a...
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Background-Foreground classification is a well-studied problem in computervision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. th...
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We focus on domain and class generalization problems in analyzing optical remote sensing images, using the large-scale pre-trained vision-language model (VLM), CLIP. While contrastively trained VLMs show impressive ze...
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the finite rate of innovation (FRI) framework has proved that it is possible to reconstruct the analog signals which have a finite number of parameters. FRI framework is used to reconstruct the images from undersample...
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