Searched for: subject%3A%22Convolutional%255C+Neural%255C+networks%22
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Blom, W.B. (author)
The digital environment has an ever increasing amount smart programs. Programs that also get smarter every day. They help us filtering spam e-mail and they adjust to show us personalized advertisements. These smart programs observe people and serve (other) people. A robot can be seen as a program with a body. Make the program smart enough and it...
master thesis 2016
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Zhou, Yuan (author)
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master thesis 2017
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Choiri, Hendra Hadhil (author)
Analysing attractiveness of places in a region is beneficial to support urban planning and policy making. However, the attractiveness of a place is a subjective and high-level concept which is difficult to quantify. The existing methods rely on traditional surveys which may require high cost and have low scalability. This thesis attempts to...
master thesis 2017
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Goes, Sten (author)
The millions of filter weights in Convolutional Neural Networks (CNNs), all have a well-defined and analytical expression for the partial derivative to the loss function. Therefor these weights can be learned from data with a technique called gradient descent optimization. While the filter weights have a well-defined derivative, the filter size...
master thesis 2017
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Snuverink, Iris (author)
In hyperspectral (HS) imaging, for every pixel a spectrum of wavelengths is captured. These spectra represent material properties, i.e. the spectral signatures. So, classification of HS imagery is based on material properties. This thesis describes a framework to perform pixelwise classification of HS images of a fixed scene subject to varying...
master thesis 2017
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Liu, W. (author), Liu, Zhigang (author), Nunez, Alfredo (author), Wang, Liyou (author), Liu, Kai (author), Lyu, Yang (author), Wang, H. (author)
The goal of this paper is to evaluate from a multi-objective perspective the performance on the detection of catenary support components when using state-of-the-art deep convolutional neural networks (DCNNs). The detection of components is the first step towards a complete automatized monitoring system that will provide actual information about...
conference paper 2018
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Gama, F. (author), Leus, G.J.T. (author), Marques, Antonio G. (author), Ribeiro, Alejandro (author)
Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks. Since two of the key operations that CNNs implement are convolution and pooling, this type of networks is implicitly designed to act on data described by regular...
conference paper 2018
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Gama, F. (author), Marques, Antonio G. (author), Ribeiro, Alejandro (author), Leus, G.J.T. (author)
Superior performance and ease of implementation have fostered the adoption of Convolutional Neural Networks (CNN s) for a wide array of inference and reconstruction tasks. CNNs implement three basic blocks: convolution, pooling and pointwise nonlinearity. Since the two first operations are well-defined only on regular-structured data such as...
conference paper 2018
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Chen, Junwen (author), Liu, Zhigang (author), Wang, H. (author), Nunez, 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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Scharenborg, O.E. (author), Merkx, Danny (author)
Fine-Tracker is a speech-based model of human speech recognition. While previous work has shown that Fine-Tracker is successful at modelling aspects of human spoken-word recognition, its speech recognition performance is not comparable to that of human performance, possibly due to suboptimal intermediate articulatory feature (AF) representations...
conference paper 2018
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Psyllidis, A. (author), Choiri, Hendra Hadhil (author)
An understanding of how people perceive attractive or unattractive places in cities is vitally important to urban planning and policy making. Given the subjective nature of human perception and the ambiguous character of attractiveness as an attribute of urban places, it is challenging to quantify and reliably assess the extent to which a place...
abstract 2018
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Khademi, S. (author), Shi, X. (author), Mager, Tino (author), Siebes, R.M. (author), Hein, C.M. (author), De Boer, Victor (author), van Gemert, J.C. (author)
We address the interpretability of convolutional neural networks (CNNs) for predicting a geo-location from an image. In a pilot experiment we classify images of Pittsburgh vs Tokyo and visualize the learned CNN filters. We found that varying the CNN architecture leads to variating in the visualized filters. This calls for further...
conference paper 2018
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Sorgedrager, Riemer (author)
This study focuses on automated malaria diagnosis in low quality blood smear images, captured by a low-cost smartphone based microscope system. The aim is to localize and classify the healthy and infected erythrocytes (red blood cells) in order to evaluate the parasitaemia in an infected blood smear. Due to the lower quality of the smartphone...
master thesis 2018
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Arif Nurhidayat, Arif (author)
In this thesis, a method to monitor the health condition of railway crossings based on vibration data recorded by the accelerometers installed on the crossing is proposed. Due to various types of trains and other exogenous factors, responses obtained from accelerometers vary, even when the crossing has the same state condition. As a consequence,...
master thesis 2018
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Sahla, Nordin (author)
The last decade has marked a rapid and significant growth of the global market of warehouse automation. The biggest challenge lies in the identification and handling of foreign objects. The aim of this research is to investigate whether a usable relation exist between object features such as size or shape, and barcode location, that can be used...
master thesis 2018
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Tetteroo, Jonathan (author)
As machine learning algorithms play an ever increasing role in today's technology, more demands are placed on computational hardware to run these algorithms efficiently. In recent years, Convolutional Neural Networks (CNNs) have become an important part of machine learning applications in areas such as object recognition and detection. In this...
master thesis 2018
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Samiotis, Ioannis Petros (author)
Side-Channel Attacks, are a prominent type of attacks, used to break cryptographic implementations on a computing system. They are based on information "leaked" by the hardware of a computing system, rather than the encryption algorithm itself. Recent studies showed that Side-Channel Attacks can be performed using Deep Learning models. In this...
master thesis 2018
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Zwanepol, Jacco (author)
In computer vision pose estimation of objects in everyday scenes is a basic need for a clearunderstanding of the surrounding environment, fields of interests include; augmented reality,surveillance, navigation, manipulation, and robotics in general. Pose estimation is a wellstudied topic, however fast and robust solutions are still hard to...
master thesis 2018
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Bormans, R. P.A. (author), Lindenbergh, R.C. (author), Karimi Nejadasl, F. (author)
One of the biggest challenges for an autonomous vehicle (and hence the WEpod) is to see the world as humans would see it. This understanding is the base for a successful and reliable future of autonomous vehicles. Real-world data and semantic segmentation generally are used to achieve full understanding of its surroundings. However, deploying...
journal article 2018
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Hommos, Omar (author)
Action recognition continues to receive significant attention from the research community, with new neural network architectures being developed continuously. Optical flow is by far the most popular input motion representation to these architectures, leaving a lot of undiscovered potential for other types of motion representations. Eulerian...
master thesis 2018
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