Cleft lip is one of the most common birth defects. Several operations are performed to form a natural lip. A problem with these operations is that the criteria for the facial symmetry are unclear. Based on this backgr...
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Radar is a key component of the suite of perception sensors used for safe and reliable navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity imaging, detection of agents in occlus...
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Radar is a key component of the suite of perception sensors used for safe and reliable navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity imaging, detection of agents in occlusion and over long ranges, and robust performance in adverse weather conditions. However, the usage of radar data presents some challenges: it is characterized by low resolution, sparsity, clutter, high uncertainty, and lack of gooddatasets. These challenges have limited radar deep learning research. As a result, current radar models are often influenced by lidar and vision models, which are focused on optical features that are relatively weak in radar data, thus resulting in under-utilization of radar's capabilities anddiminishing its contribution to autonomous perception. This review seeks to encourage further deep learning research on autonomous radar data by 1) identifying key research themes, and 2) offering a comprehensive overview of current opportunities and challenges in the field. Topics covered include early and late fusion, occupancy flow estimation, uncertainty modeling, and multipath detection. The paper also discusses radar fundamentals anddata representation, presents a curated list of recent radar datasets, and reviews state-of-the-art lidar and vision models relevant for radar research.
In this paper, we present a novel method, called four-dimensional convolutional recurrent neural network, which integrating frequency, spatial and temporal information of multichannel EEG signals explicitly to improve...
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In this paper, we present a novel method, called four-dimensional convolutional recurrent neural network, which integrating frequency, spatial and temporal information of multichannel EEG signals explicitly to improve EEG-based emotion recognition accuracy. First, to maintain these three kinds of information of EEG, we transform the differential entropy features from different channels into 4d structures to train the deep model. Then, we introduce CRNN model, which is combined by convolutional neural network (CNN) and recurrent neural network with long short term memory (LSTM) cell. CNN is used to learn frequency and spatial information from each temporal slice of 4d inputs, and LSTM is used to extract temporal dependence from CNN outputs. The output of the last node of LSTM performs classification. Our model achieves state-of-the-art performance both on SEEd anddEAP datasets under intra-subject splitting. The experimental results demonstrate the effectiveness of integrating frequency, spatial and temporal information of EEG for emotion recognition.
The rapidly improving temporal resolution of X-ray computed tomography (CT) imaging methods makes it ever easier to do in-situ, time-resolved (4d) experiments. This work describes a method of segmenting 4d X-ray CT da...
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The rapidly improving temporal resolution of X-ray computed tomography (CT) imaging methods makes it ever easier to do in-situ, time-resolved (4d) experiments. This work describes a method of segmenting 4d X-ray CT data that works well for extracting information on the interfacial properties, such as interfacial curvature and velocity. As an example of this method, a segmentation is performed on data from an isothermal coarsening experiment of an Al-Cu solid/liquid mixture.
Patients suffering from cerebral ischemia or subarachnoid hemorrhage, undergo a 4d (3d+time) CT Perfusion (CTP) scan to assess the cerebral perfusion and a CT Angiography (CTA) scan to assess the vasculature. The aim ...
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ISBN:
(纸本)9780819480248
Patients suffering from cerebral ischemia or subarachnoid hemorrhage, undergo a 4d (3d+time) CT Perfusion (CTP) scan to assess the cerebral perfusion and a CT Angiography (CTA) scan to assess the vasculature. The aim of our research is to extract the vascular information from the CTP scan. This requires thin-slice CTP scans that suffer from a substantial amount of noise. Therefore noise reduction is an important prerequisite for further analysis. So far, the few noise filtering methods for 4d datasets proposed in literature deal with the temporal dimension as a 4th dimension similar to the 3 spatial dimensions, mixing temporal and spatial intensity information. We propose a bilateral noise reduction method based on time-intensity profile similarity (TIPS), which reduces noise while preserving temporal intensity information. TIPS was compared to 4d bilateral filtering on 10 patient CTP scans and, even though TIPS bilateral filtering is much faster, it results in better vessel visibility and higher image quality ranking (observer study) than 4d bilateral filtering.
The recent development of electro-optical instrumentation allowed constructing 4d (3d + time) structure-light scanners which may be used to measure the surface of human body in motion. The main advantage of structure-...
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ISBN:
(纸本)9780819470232
The recent development of electro-optical instrumentation allowed constructing 4d (3d + time) structure-light scanners which may be used to measure the surface of human body in motion. The main advantage of structure-light scanners is the possibility of capturing data from the whole measured body surface, while traditional marker-based systems acquire data only form markers attached to skin of the examined patient. The paper describes new parameters describing the local shape of measured surface. The distribution maps of these parameters allow discrimination of various surface types and in effect localization and tracing of under-skin anatomical structures in time. The presented parameters give similar results to well-known curvatures but are easier and quicker to calculate. Moreover the calculation process of the new parameters is more numerically stable itself. The developed path of processing and analysis of 4d measurement data has been presented. It contains the following stages: data acquisition, volumetric model creation, calculations of shape parameters, selecting areas of interest, locating and tracing of anatomical landmarks. Exemplary results of application of developed parameters and methods to real measurement and computer generateddata are also presented.
In this paper we discuss the problem of graphically representing functions of three variables and explain the implementation of a method in which each picture shows the 3d solid shape described by one data level. The ...
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In this paper we discuss the problem of graphically representing functions of three variables and explain the implementation of a method in which each picture shows the 3d solid shape described by one data level. The method enables us to plot individual data levels from any REAL array ARR(NX, NY, NZ) of equally spaced values over an NX by NY by NZ regular grid. A perspective representation of each 3d shape can be drawn from any chosen viewing position with hidden-line removal; this is illustrated by some pictures drawn using a Fortran 77 program.
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