Searched for: subject:"Feature%5C+extraction"
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Liu, Changjiang (author), Li, Y. (author), Ao, Dongyang (author), Tian, Haiyan (author)
Radar sensors offer several advantages over optical sensors in the gesture recognition for remote control of electronic devices. In this paper, we investigate the feasibility of human gesture recognition using the spectra of radar measurement parameters. With the combination of radar theory and classification methods, we found that the...
journal article 2019
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Kang, Z. (author), Yang, Juntao (author), Zhong, Ruofei (author), Wu, Yongxing (author), Shi, Zhenwei (author), Lindenbergh, R.C. (author)
The digital mapping of road environment is an important task for road infrastructure inventory and urban planning. Automatic extraction and classification of pole-like objects can remarkably reduce mapping cost and enhance work efficiency. Therefore, this paper proposes a voxel-based method that automatically extracts and classifies three...
journal article 2018
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Liu, C (author), Cheng, Gang (author), Chen, Xihui (author), Pang, Y. (author)
Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was...
journal article 2018
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Chen, Junwen (author), Liu, Zhigang (author), Wang, H. (author), Nunez Vicencio, Alfredo (author), Han, Zhiwei (author)
The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for...
journal article 2018
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Zheng, M. (author), Lemmens, M.J.P.M. (author), van Oosterom, P.J.M. (author)
This paper presents our work on automated classification of Mobile Laser Scanning (MLS) point clouds of urban scenes with features derived from cylinders around points of consideration. The core of our method consists of spanning up a cylinder around points and deriving features, such as reflectance, height difference, from the points present...
conference paper 2018
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Lemmens, M.J.P.M. (author)
A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm,...
journal article 2018
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Yadati, N.K. (author), Larson, M.A. (author), Liem, C.C.S. (author), Hanjalic, A. (author)
In this paper, we focus on event detection over the timeline of a music track. Such technology is motivated by the need for innovative applications such as searching, non-linearaccess and recommendation. Event detection over the timeline requires time-code level labels in order to train machine learning dels. We use timed comments from...
journal article 2018
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Nurunnabi, Abdul (author), Sadahiro, Yukio (author), Lindenbergh, R.C. (author)
This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only...
journal article 2017
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Lawonn, K.R.H. (author), Trostmann, Erik (author), Preim, Bernhard (author), Hildebrandt, K.A. (author)
We present novel techniques for visualizing, illustrating, analyzing, and generating carvings in surfaces. In particular, we consider the carvings in the plaster of the cloister of the Magdeburg cathedral, which dates to the 13th century. Due to aging and weathering, the carvings have flattened. Historians and restorers are highly interested in...
journal article 2017
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Ruelens, F (author), Claessens, BJ (author), Vandael, S (author), De Schutter, B.H.K. (author), Babuska, R. (author), Belmans, R (author)
Driven by recent advances in batch Reinforcement Learning (RL), this paper contributes to the application of batch RL to demand response. In contrast to conventional model-based approaches, batch RL techniques do not require a system identification step, making them more suitable for a large-scale implementation. This paper extends fitted Q...
journal article 2017
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Palma, David (author), Montessoro, Pier Luca (author), Giordano, G. (author), Blanchini, Franco (author)
Most of the existing techniques for palmprint recognition rely on metrics, typically based on static functions, which evaluate the distance between a pair of features. In this paper, we propose a new technique for palmprint verification based on a dynamical system approach for principal palm lines matching. The proposed dynamic algorithm is...
journal article 2017
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Zheng, M. (author), Lemmens, M.J.P.M. (author), van Oosterom, P.J.M. (author)
The demand for 3D maps of cities and road networks is steadily growing and mobile laser scanning (MLS) systems are often the preferred geo-data acquisition method for capturing such scenes. Because MLS systems are mounted on cars or vans they can acquire billions of points of road scenes within a few hours of survey. Manual processing of...
conference paper 2017
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van Loren, A. (author), van Leuken, T.G.R.M. (author)
Robust, power- and area-efficient spike classifier, capable of accurate identification of the neural spikes even for low SNR, is a prerequisite for the real-time, implantable, closed-loop brain-machine interface. In this paper, we propose an easily-scalable, 128-channel, programmable, neural spike classifier based on nonlinear energy operator...
conference paper 2016
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Munk, J. (author), Kober, J. (author), Babuska, R. (author)
Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). One advantage of DNNs is that they can cope with large input dimensions. Instead of relying on feature engineering to lower the input dimension, DNNs can extract the features from raw observations. The drawback of this end-to-end learning is that it...
conference paper 2016
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Faghih Roohi, S. (author), Hajizadeh, S. (author), Nunez Vicencio, Alfredo (author), Babuska, R. (author), De Schutter, B.H.K. (author)
In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and detect rail surface defects. Therefore, automated...
conference paper 2016
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Van den Berg, B.A. (author), Reinders, M.J. (author), Roubos, J.A. (author), De Ridder, D. (author)
Background Amino acid sequences and features extracted from such sequences have been used to predict many protein properties, such as subcellular localization or solubility, using classifier algorithms. Although software tools are available for both feature extraction and classifier construction, their application is not straightforward,...
journal article 2014
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Sirmacek, B. (author), Lindenbergh, R.C. (author), Menenti, M. (author)
Fusion of 3D airborne laser (LIDAR) data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from...
conference paper 2013
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Eleftherakis, D. (author), Amiri-Simkooei, A. (author), Snellen, M. (author), Simons, D.G. (author)
Riverbed and seafloor sediment classification using acoustic remote sensing techniques is of high interest due to their high coverage capabilities at limited cost. This contribution presents the results of riverbed sediment classification using multi-beam echo-sounder data based on an empirical method. Two data sets are considered, both taken at...
journal article 2012
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Shahbahrami, A. (author), Pham, T.A. (author), Bertels, K.L.M. (author)
Texture features extraction algorithms are key functions in various image processing applications such as medical images, remote sensing, and content-based image retrieval. The most common way to extract texture features is the use of Gray Level Co-occurrence Matrices (GLCMs). The GLCM contains the second-order statistical information of spatial...
journal article 2011
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Van Genderen, P. (author), Kovalenko, V. (author)
Detection in most surveillance radars is based on the condition of point targets against a more or less homogeneous background. Currently, the resolution of many new types of radar is increasing, at least in the range dimension. Therefore many objects can no longer be considered as points. Also as a consequence, the background is becoming more...
journal article 2009
Searched for: subject:"Feature%5C+extraction"
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