Recover drawing orders from a Chinese handwriting image is a challenge issue. Most of English drawing order recovery( DOR) methods perform unsatisfactorily in Chinese. This paper proposes a novel image-to-sequence alg...
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ISBN:
(纸本)9781479981311
Recover drawing orders from a Chinese handwriting image is a challenge issue. Most of English drawing order recovery( DOR) methods perform unsatisfactorily in Chinese. This paper proposes a novel image-to-sequence algorithm to deal with Chinese DOR problem. The proposed method utilizes two regression convolution neural network(CNN) models to generate two corresponding pen-tip movement heat-maps. To estimate pen-tip movement for most of the normal states in writing process, the algorithm analyzes the above two heat-maps with a specifically designed framework. Then the drawing order is restored through a simple iteration process based on the proposed framework. Experiments on public online handwriting database show that our method have got a remarkable result for Chinese DOR tasks. In addition, for English tasks, our method performs superiorly among state-of-the-art methods.
Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imagin...
Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of satisfactory quality available in this cohort of sensitive subjects remains scarce, thus hindering the validation of advanced imageprocessing techniques. Numerical phantoms can mitigate these limitations by providing a controlled environment with a known ground truth. In this work, we present FaBiAN, an open-source Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates clinical T2-weighted fast spin echo sequences of the fetal brain. This unique tool is based on a general, flexible and realistic setup that includes stochastic fetal movements, thus providing images of the fetal brain throughout maturation comparable to clinical acquisitions. We demonstrate its value to evaluate the robustness and optimize the accuracy of an algorithm for super-resolution fetal brain magnetic resonance imaging from simulated motion-corrupted 2D low-resolution series compared to a synthetic high-resolution reference volume. We also show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation.
With the rapid growth of medical big data, medical signalprocessing measurement techniques are facing severe challenges. Enormous medical images are constantly generated by various health monitoring and sensing devic...
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With the rapid growth of medical big data, medical signalprocessing measurement techniques are facing severe challenges. Enormous medical images are constantly generated by various health monitoring and sensing devices, such as ultrasound, MRI machines. Hence, based on pulse coupled neural network (PCNN) and the classical visual receptive field (CVRF) with the difference of two Gaussians (DOG), a contrast enhancement of MRI image is suggested to improve the accuracy of clinical diagnosis for smarter mobile healthcare. As one premise, the parameters of DOG are estimated from the fundamentals of CVRF;then the PCNN parameters in image enhancement are estimated eventually with the help of DOG. As a result, the MRI images can be enhanced adaptively. Due to the exponential decay of the dynamic threshold and the pulses coupling among neurons, PCNN effectively enhances the contrast of low grey levels in MRI image. Moreover, because of the inhibitory effects from inhibitory region in CVRF, PCNN also effectively preserves the structures such as edges for enhanced results. Experiments on several MRI images show that the proposed method performs better than other methods by improving contrast and preserving structures well.
Recently, most image classification studies solicit the intervention of convolutional neural networks because these DL-based classification methods generally outperform other methodologies with higher accuracy. Howeve...
Recently, most image classification studies solicit the intervention of convolutional neural networks because these DL-based classification methods generally outperform other methodologies with higher accuracy. However, this type of deep learning networks require many parameters and have a complex structure with multiple convolutional and pooling layers depending on the objective. These layers compute a large volume of data and it may impact the processing time and the performance. Therefore, this paper proposes a new method of image classification based on the light convolutional neural network. It consists of replacing the feature extraction layers of standard convolutional neural network with a single pulse coupled neural network by introducing the notion of foveation. This module provides the feature map of input image and the data compression using Discrete Wavelet Transform which is an optional step depending on the information quantity of this signature. The fully connected neural network, which has six hidden layers, classifies the image. With this technique, the computation time is reduced, and the network architecture is identical and simple independent of the type of dataset. The number of parameter is less than that in current research. The proposed method was validated with different dataset such as Caltech-101, Caltech-256, CIFAR-10, CIFAR-100, and imageNet, and the accuracy reaches 92%, 90%, 99%, 94%, and 91%, respectively, which are better than the previous related works.
Iris-sclera biometry is one of the features that yields high accuracy in user recognition and liveness detection systems. In this study, segmentation processes the first stage of an iris-sclera user verification syste...
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ISBN:
(纸本)9781728119045
Iris-sclera biometry is one of the features that yields high accuracy in user recognition and liveness detection systems. In this study, segmentation processes the first stage of an iris-sclera user verification system have been considered. Traditional and convolutional neural network based deep learning methods have been used for iris-sclera segmentation. Performance of the investigated methods has been tested on two distinct eye image datasets (UBIRIS and self-collected data). Our experimental results show that deep learning based segmentation methods outperformed conventional methods in terms of dice score on both datasets.
Audio-Visual Segmentation (AVS) is a task that aims to predict pixel-level masks for sound-producing objects in videos. Recent advanced AVS methods primarily focus on cross-modal interaction while often neglecting the...
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Automatic building segmentation from remote sensing images is critical in the remote sensing image semantic segmentation. The success of deep neural networks has led to advances in using fully convolutional neural net...
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ISBN:
(数字)9781510630147
ISBN:
(纸本)9781510630147
Automatic building segmentation from remote sensing images is critical in the remote sensing image semantic segmentation. The success of deep neural networks has led to advances in using fully convolutional neural networks (FCN) to extract buildings from the high-resolution image. However, the downsampling processing inevitably leads to loss of details of the segmentation results. To solve this problem, some methods try to refine the results of FCN by using probability graph models such as fully connected CRF (Conditional Random Fields). Nevertheless, many fully connected CRF based methods are too time-consuming and not suitable for building segmentation tasks in some situations. In this paper, we propose a novel time- efficient end-to-end CRF model with the domain transform algorithm called DT-CRF. In the proposed model, in order to accelerate the message passing in the mean-field approximate inference algorithm, we take the edge maps as the joint image for DT-CRF and use the domain transformation algorithm to calculate the pair-wise potential instead of the Gaussian kernel function. Meanwhile, we design a multi-task network which can generate masks and edges simultaneously, and the network can make the DT-CRF to easily optimize the segmentation results using model information. The evaluation of remote sensing image datasets verifies the time and space efficiency of the proposed DTCRF and demonstrates a distinct improvement.
Many conventional demosaicking methods are based on hand-crafted filters. However, the filters yield false colors in salient regions like edges and textures. For acquisition of high quality images, we focus on neural ...
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ISBN:
(纸本)9781479981311
Many conventional demosaicking methods are based on hand-crafted filters. However, the filters yield false colors in salient regions like edges and textures. For acquisition of high quality images, we focus on neural networks. neural networks lead to high accuracy in many fields. However, there are few methods in demosaicking field. For adaptation to demosaicking, we consider not only network's architecture but also the input. In this research, we utilize a Bayer image as input of our networks. However, different filter is needed in estimation at different color pixels, for example, missing red value at green pixel and that at blue pixel. Therefore, we prepare four networks with downsampling operators classified by color patterns in Bayer images. This downsampling operator not only identifies the color pattern but also reduces the calculation cost in each network due to reduction of the size of feature maps. Besides, preparation of multi-networks instead of a deep single-network is suitable for today's parallel computing. Moreover, we utilize not missing color images but chrominance images as output. Compared to results with missing color images as output, the results with chrominance images obtains higher accuracy. Experimental results show our CNN-based approach produces high quality restored images.
Multi-label image classification has achieved remarkable progress thanks to deep convolutional neural networks (CNNs). In this paper, we propose a Decouple Network (DecoupleNet) which is an end-to-end CNN-based framew...
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ISBN:
(纸本)9781479981311
Multi-label image classification has achieved remarkable progress thanks to deep convolutional neural networks (CNNs). In this paper, we propose a Decouple Network (DecoupleNet) which is an end-to-end CNN-based framework able to trade off class-level feature independence and relevance during training. The proposed DecoupleNet is able to decouple category-wise independence and relevance with image-level supervision. We design a category-wise space-to-depth module with a spatial pooling strategy to exploit more meaningful convolutional features. They are integrated with class-wise correlated information which is automatically learned via a new self-attention mechanism. We conduct extensive experiments on two large-scale benchmarks: the MS-COCO and the NUS-WIDE, where the proposed DecoupleNet obtains impressive performance compared favorably against the state-of-the-art methods on multi-label image classification.
Human activity recognition (HAR) is recognized as one of the most critical key academic fields, focusing on the classification of human behaviors based on sensor data. It has gained increasing attention due to its bro...
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