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Vincenzo Lomonaco
In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks [1] [2] [3]. The rise of deep learning is also revolutionizing the entire field of Machine Learning and […]
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Hasmik Osipyan, Martin Krulis, Stephane Marchand-Maillet
The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction which is defined as a process of mapping data from high-dimensional space into low-dimensional. One of the most popular methods for handling this problem is multidimensional scaling. Due to the technological advances, the dimensionality of the input data as […]
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Yi Hou, Hong Zhang, Shilin Zhou
Deep convolutional neural networks (CNN) have recently been shown in many computer vision and pattern recognition applications to outperform by a significant margin state-of-the-art solutions that use traditional hand-crafted features. However, this impressive performance is yet to be fully exploited in robotics. In this paper, we focus one specific problem that can benefit from the […]
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David Markvica
The longest common subsequence (LCS) problem is one of the classic problems in string processing. It is commonly used in file comparison, pattern recognition, and computational biology as a measure of sequence similarity. Given a set of strings, the LCS is the longest string that is a subsequence of every string in the set. For […]
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Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, Trevor Darrell
Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and […]
Youssef S. G. Nashed
Nature based computational models are usually inherently parallel. The collaborative intelligence in those models emerges from the simultaneous instruction processing by simple independent units (neurons, ants, swarm members, etc…). This dissertation investigates the benefits of such parallel models in terms of efficiency and accuracy. First, the viability of a parallel implementation of bio-inspired metaheuristics for […]
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Ken Chatfield, Karen Simonyan, Andrew Zisserman
We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval – where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. We make three contributions: (i) we present an evaluation of state-of-the-art […]
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Ping Zhang, Yongqi Sun, Hao Shen, Rui Zhang
PCA-SIFT is an algorithm to extract invariant features from images, it has been widely applied to many application fields including image processing, computer vision and pattern recognition. However, the execution of PCA-SIFT is time-consuming. A parallel algorithm of PCA-SIFT based on Compute Unified Device Architecture (CUDA) is proposed in this paper, in which each step […]
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Charles J. Gala
Facial recognition is an active research area that provides a real-time application of pattern recognition techniques. Input can be provided to recognition algorithms using both static images and video data. However, there are significant challenges to working with live streaming data as the recognition method needs to keep up with the frame rate of the […]
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Md. Enamul Haque, Abdullah Al Kaisan, Mahmudur R Saniat, Aminur Rahman
In this paper, we implemented both sequential and parallel version of fractal image compression algorithms using CUDA (Compute Unified Device Architecture) programming model for parallelizing the program in Graphics Processing Unit for medical images, as they are highly similar within the image itself. There are several improvement in the implementation of the algorithm as well. […]
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S. Kostopoulos, D. Glotsos, K. Sidiropoulos, P. Asvestas, D. Cavouras, I. Kalatzis
The aim of the present study was to implement a pattern recognition system for the discrimination of healthy from malignant prostate tumors from proteomic Mass Spectroscopy (MS) samples and to identify m/z intervals of potential biomarkers associated with prostate cancer. One hundred and six MS-spectra were studied in total. Sixty three spectra corresponded to healthy […]
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S. Kostopoulos, K. Sidiropoulos, D. Glotsos, N. Dimitropoulos, I. Kalatzis, P. Asvestas and D. Cavouras
The aim of this study was to design a pattern recognition system for assisting the diagnosis of breast lesions, using image information from Ultrasound (US) and Digital Mammography (DM) imaging modalities. State-of-art computer technology was employed based on commercial Graphics Processing Unit (GPU) cards and parallel programming. An experienced radiologist outlined breast lesions on both […]
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