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In many scenarios, a persons behavior in office environment needs to be monitored and some predefined abnormal actions or activities should be detected and recognized. In this paper, we attempted towards the solution starting from a persons pose with poselets as the basic building blocks. The existed powerful pose representation, i.e., poselets, together with deep convolutional neural networks, are...
Nowadays the image recognition system is applied more and more widely in the security monitoring, the industrial intelligent monitoring, the unmanned vehicle, and even the space exploration. As an image recognition technique, the traditional convolution neural network has some defects such as long training time, easy over-fitting and high misclassification rate. After our analysis on the network structure...
Recognition of handwritten characters has been a popular task for the evaluation of classification algorithms for many years. Looking at the latest results on databases such as USPS or MNIST, one could think that character recognition is a solved problem. In this paper, we claim that this is not the case for two reasons : first because the classical databases for digit recognition are realistic but...
The objective of the present work is to provide an efficient technique for off-line recognition of handwritten numeral strings. It can be used in various applications, like postal code recognition or information extraction from fields of different forms. The proposed solution uses convolutional neural networks (CNNs) to implement two classifiers, one for digit recognition and one for numeral strings...
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