The heart is one of the most important organs in our body and many critical diseases are associated with its malfunctioning. To assess the risk for heart diseases, Magnetic Resonance Imaging (MRI) has become the golde...
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The heart is one of the most important organs in our body and many critical diseases are associated with its malfunctioning. To assess the risk for heart diseases, Magnetic Resonance Imaging (MRI) has become the golden standard imaging technique, as it provides to the clinicians stacks of images for analyzing the heart structures, such as the ventricles, and thus to make a diagnosis of the patient's health. The problem is that examination of these stacks, often based on the delineation of heart structures, is tedious and error prone due to inter-and intra-variability among manual delineations. For this reason, the investigation of fully automated methods to support heart segmentation is paramount. Most of the successful methods proposed to solve this problem are based on deep-learning solutions. Especially, encoder-decoder architectures, such as the U-Net [1], have demonstrated to be very effective architectures for medical image segmentation. In this paper, we propose to use long-range skip connections on the decoder-part to incorporate multi-context information onto the predicted segmentation masks and also to improve the generalization of the models. In addition, our method obtains smoother segmentations through the combination of feature maps from different stages onto the final prediction layer. We evaluate our approach in the ACDC [2] and LVSC [3] heart segmentation challenges. Experiments performed on both datasets demonstrate that our approach leads to an improvement on both the total Dice score and the Ejection Fraction Correlation, when combined with state-of-the-art encoder-decoder architectures.
We investigate a method to obtain accurate 3D measurements and we compare it with a standard photogrammetry method. The method utilizes a closed-loop to bring the point under measure in the middle of the two images of...
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ISBN:
(纸本)9539676924
We investigate a method to obtain accurate 3D measurements and we compare it with a standard photogrammetry method. The method utilizes a closed-loop to bring the point under measure in the middle of the two images of a stereo pair. It uses poor calibrated cameras and the accuracy of the measurements relies on the high resolution of the stereo head encoders and on its calibration as well. Experimental results have shown that the proposed method is more accurate than the standard photogrammetry method, as well as it is more robust to small variations of the initial position of the cameras.
Video segmentation consists of a frame-by-frame selection process of meaningful areas related to foreground moving objects. Some applications include traffic monitoring, human tracking, action recognition, efficient v...
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Video segmentation consists of a frame-by-frame selection process of meaningful areas related to foreground moving objects. Some applications include traffic monitoring, human tracking, action recognition, efficient video surveillance, and anomaly detection. In these applications, it is not rare to face challenges such as abrupt changes in weather conditions, illumination issues, shadows, subtle dynamic background motions, and also camouflage effects. In this work, we address such shortcomings by proposing a novel deep learning video segmentation approach that incorporates residual information into the foreground detection learning process. The main goal is to provide a method capable of generating an accurate foreground detection given a grayscale video. Experiments conducted on the Change Detection 2014 and on the private dataset PetrobrasROUTES from Petrobras support the effectiveness of the proposed approach concerning some state-of-the-art video segmentation techniques, with overall F-measures of 0.9535 and 0.9636 in the Change Detection 2014 and PetrobrasROUTES datasets, respectively. Such a result places the proposed technique amongst the top 3 state-of-the-art video segmentation methods, besides comprising approximately seven times less parameters than its top one counterpart.
Robustness and invisibility are two contrary constraints for robust invisible watermarking. Instead of the conventional strategy with human visual system (HVS) model, this paper presents a content-adaptive approach to...
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Robustness and invisibility are two contrary constraints for robust invisible watermarking. Instead of the conventional strategy with human visual system (HVS) model, this paper presents a content-adaptive approach to further optimize the constraint between them. To reach this target, the entropy-based and integrated HVS (IHVS) based measures are constructed so as to adaptively choose the suitable components for watermark insertion and detection. Such a kind of scheme potentially gives rise to synchronization problem between the encoder and decoder under the framework of blind watermarking, which is then solved by incorporating the repeat-accumulate (RA) code with erasure and error correction. Moreover, a new hidden Markov model (HMM) based detector in wavelet domain is introduced to reduce the computation complexity and is further developed into a posterior one to avoid the transmission of HMM parameters with only a little sacrifice of detection performance. Experimental results show that the proposed algorithm can obtain considerable improvement in robustness performance with the same distortion as the traditional one.
Principal component (PCA) and multiple discriminant analysis (MDA) are applied to magnetization transfer ratio (MTR) images in multiple sclerosis (MS). PCA and MDA are used to characterise subtle diffuse changes in MS...
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ISBN:
(纸本)9539676924
Principal component (PCA) and multiple discriminant analysis (MDA) are applied to magnetization transfer ratio (MTR) images in multiple sclerosis (MS). PCA and MDA are used to characterise subtle diffuse changes in MS. PCA is applied to MTR histograms to identify regions of significant variation. These areas are indicated as possible lesion areas. We compare two classifiers to recognise differences between normal controls and different types of MS disease; a Bayesian classifier is trained in PC space, and the histogram space is transformed to the optimal discriminant space for a nearest neighbor classifier.
The aim of this paper is to create a software that enables users to setup devices using augmented reality. Such software could serve educational purposes by providing step-by-step guidance, like assembling a computer....
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ISBN:
(数字)9798350385601
ISBN:
(纸本)9798350385618
The aim of this paper is to create a software that enables users to setup devices using augmented reality. Such software could serve educational purposes by providing step-by-step guidance, like assembling a computer. Recognizing a device requires an imageprocessing algorithm capable of real-time recognition of a given equipment. Determining the unique parameters of the devices is also necessary for the algorithm to recognize the device model. Subsequently, various states for these models need to be defined, indicating the steps required to move from one state to another. During these steps, it's possible to specify what instructions the user should receive. These instructions could be textual or some form of shape that highlights a specific part of the device or guides from one point to another. To display these shapes, storing instructions is necessary, typically in a database or a markup language. Additionally, for the software to project the shapes accurately, it needs relative coordinates in relation to a given origin point, which could be a specific point on the recognized device.
This study devises an automatic synthetic aperture radar (SAR) image enhancement method for ship detection and inspection for installation in a near-real-time automatic high-speed processing system. The proposed metho...
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This study devises an automatic synthetic aperture radar (SAR) image enhancement method for ship detection and inspection for installation in a near-real-time automatic high-speed processing system. The proposed method was examined in small ship inspection and detection over the Ieodo Ocean area off Korea using RADARSAR-2 HV-polarization data. The proposed method involves four steps. First, the SAR input data is converted into a highly compressed gray scale image, which enables both computer screen display and high-speed processing due to its light volume. Second, the overall contrast is adjusted by power-law scaling to strengthen the target discrimination, which is attenuated because of the inefficiency of one-sided intensity distribution. This additionally provides excellent target visibility. Third, the intensity of the area in which targets and clutter coexist is rescaled from 0 to 255 using min-max linear stretching. This suppresses background clutter and makes targets more easily distinguishable from the clutter. Lastly, the remaining clutter is successfully eliminated using a median filter. As a result, an output image is obtained that is close to binary data and enables ship detection using only simple global thresholding. Our ship detection results were compared with that of ships identified using an automatic identification system and those visible in high-precision images by visual inspection. We verified that our method offers a high detection rate for small ships and does not involve complexity in distribution assumption, filtering or thresholding. The potential of our method is confirmed as an automatic SAP. enhancement method for near-real-time ship detection and inspection.
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