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Pedestrian detection is one of the key technologies in automotive safety, robotic and intelligent video surveillance. Recently, deep convolutional neural networks have achieved significant effect in image classification and retrieval tasks. In this paper, we propose a novel deep convolutional neural networks model for pedestrian detection to simultaneously extract and classify pedestrian features...
Aiming at the shorting of the existing atrial fibrillation (AF) detection algorithms and improve the ability of intelligent recognition and extraction of AF signals. Recently, deep learning theory with massive data has been used on image, voice and other filed widely. In this paper, a method based on the stack sparse autoencoder neural network, a instance of deep learning strategy, was proposed for...
the objective to develop clinical decision support system (CDSS) tools is to help physicians making faster and more reliable clinical decisions. The first step in their development is choose a machine learning classifier as the system core. Previous works reported implementation of artificial neural networks, support vector machines, genetic algorithms, etc. as core classifiers for CDSS; however,...
This paper presents an attempt to solve the challenging problem of Devanagari numeral and character recognition. It uses structural and geometric features to represent the Devanagari numerals and characters. Each image is zoned in 9 blocks and 8 structural features are extracted from each block. Similarly 9 global geometric features are extracted. These 81 features are used for representing the image...
The entire process of face detection, identification and localization of faces should preferably be almost orientation or rotation invariant. The present paper aims to design one optimal Back Propagation (BP) Network model to perform these face identification tasks. The task is partially independent of orientation or rotation of the faces in the image. Also the identification rate of the faces is...
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