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Training of Convolutional Neural Networks (CNNs) on long video sequences is computationally expensive due to the substantial memory requirements and the massive number of parameters that deep architectures demand. Early fusion of video frames is thus a standard technique, in which several consecutive frames are first agglomerated into a compact representation, and then fed into the CNN as an input...
Principal component analysis (PCA) is one of the most crucial dimensionality reduction methods and widely used in satellite image analysis, face recognition, social network feature extraction and other application scenarios. But it is fragile because of its quadratic error criterion when faced with outliers. There are many robust PCAs to solve this problem, however, when extended to the high-dimensional...
We present a histogram-based real-time solution to detecting directly irradiated regions in digital fluoroscopic images. Our method leverages the power of model matching, machine learning and domain knowledge to characterize and segment images using histograms. The input image is automatically identified as containing partial, all, or null direct radiation. The regions with direct radiation are segmented...
Gait analysis of human plays a significant role in maintaining the well-being of our mobility and healthcare, and it can be used for various e-healthcare systems for fast medical prognosis and diagnosis. In this paper we have developed a novel body sensor network based recognition system to identify the specific gait pattern of Parkinson's disease (PD). Firstly, a BSN with 16 nodes is used to acquire...
We develop an algorithm RSFA to perform nonlinear blind source separation with temporal constraints. The algorithm is based on slow feature analysis using random Fourier features for shift invariant kernels, followed by a selection procedure to obtain the sought-after signals. This method not only obtains remarkable results in a short computing time, but also excellently handles situations where there...
Craters are important geographical features caused by the impacts of meteoroids. Craters have been widely studied because they contain crucial information about the age and geologic formations of planets. This paper discusses an automated crater-detection framework using knowledge discovery and data mining (KDD) process including sampling, feature selection and creation, and supervised learning methods...
Most existing algorithms for ordinal regression usually seek an orientation for which the projected samples are well separated, and seriate intervals on that orientation to represent the ranks. However, these algorithms only make use of one dimension in the sample space, which would definitely lose some useful information in its complementary subspace. As a remedy, we propose an algorithm framework...
We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for a recognition tasks based on geometrical learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase...
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