1,704 research outputs found
Entropy Encoding, Hilbert Space and Karhunen-Loeve Transforms
By introducing Hilbert space and operators, we show how probabilities,
approximations and entropy encoding from signal and image processing allow
precise formulas and quantitative estimates. Our main results yield orthogonal
bases which optimize distinct measures of data encoding.Comment: 25 pages, 1 figur
Leave-one-out prediction error of systolic arterial pressure time series under paced breathing
In this paper we show that different physiological states and pathological
conditions may be characterized in terms of predictability of time series
signals from the underlying biological system. In particular we consider
systolic arterial pressure time series from healthy subjects and Chronic Heart
Failure patients, undergoing paced respiration. We model time series by the
regularized least squares approach and quantify predictability by the
leave-one-out error. We find that the entrainment mechanism connected to paced
breath, that renders the arterial blood pressure signal more regular, thus more
predictable, is less effective in patients, and this effect correlates with the
seriousness of the heart failure. The leave-one-out error separates controls
from patients and, when all orders of nonlinearity are taken into account,
alive patients from patients for which cardiac death occurred
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Detecting abnormality in optic nerve head images using a feature extraction analysis
Imaging and evaluation of the optic nerve head (ONH) plays an essential part in the detection and clinical management of glaucoma. The morphological characteristics of ONHs vary greatly from person to person and this variability means it is difficult to quantify them in a standardized way. We developed and evaluated a feature extraction approach using shiftinvariant wavelet packet and kernel principal component analysis to quantify the shape features in ONH images acquired by scanning laser ophthalmoscopy (Heidelberg Retina Tomograph [HRT]). The methods were developed and tested on 1996 eyes from three different clinical centers. A shape abnormality score (SAS) was developed from extracted features using a Gaussian process to identify glaucomatous abnormality. SAS can be used as a diagnostic index to quantify the overall likelihood of ONH abnormality. Maps showing areas of likely abnormality within the ONH were also derived. Diagnostic performance of the technique, as estimated by ROC analysis, was significantly better than the classification tools currently used in the HRT software – the technique offers the additional advantage of working with all images and is fully automated
Off-line handwritten signature recognition by wavelet entropy and neural network
Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN). Our investigation was conducted over several wavelet families and different entropy types. Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study. Several other methods used in the literature were considered for comparison. Two databases were used for algorithm testing. The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%
The Performance of LBP and NSVC Combination Applied to Face Classification
The growing demand in the field of security led to the development of interesting approaches in face classification. These works are interested since their beginning in extracting the invariant features of the face to build a single model easily identifiable by classification algorithms. Our goal in this article is to develop more efficient practical methods for face detection. We present a new fast and accurate approach based on local binary patterns (LBP) for the extraction of the features that is combined with the new classifier Neighboring Support Vector Classifier (NSVC) for classification. The experimental results on different natural images show that the proposed method can get very good results at a very short detection time. The best precision obtained by LBP-NSVC exceeds 99%
DESIGN OF COMPACT AND DISCRIMINATIVE DICTIONARIES
The objective of this research work is to design compact and discriminative dictionaries
for e�ective classi�cation. The motivation stems from the fact that dictionaries
inherently contain redundant dictionary atoms. This is because the aim of dictionary
learning is reconstruction, not classi�cation. In this thesis, we propose methods to obtain
minimum number discriminative dictionary atoms for e�ective classi�cation and
also reduced computational time.
First, we propose a classi�cation scheme where an example is assigned to a class
based on the weight assigned to both maximum projection and minimum reconstruction
error. Here, the input data is learned by K-SVD dictionary learning which alternates
between sparse coding and dictionary update. For sparse coding, orthogonal
matching pursuit (OMP) is used and for dictionary update, singular value decomposition
is used. This way of classi�cation though e�ective, still there is a scope to
improve dictionary learning by removing redundant atoms because our goal is not reconstruction.
In order to remove such redundant atoms, we propose two approaches
based on information theory to obtain compact discriminative dictionaries. In the
�rst approach, we remove redundant atoms from the dictionary while maintaining
discriminative information. Speci�cally, we propose a constraint optimization problem
which minimizes the mutual information between optimized dictionary and initial
dictionary while maximizing mutual information between class labels and optimized
dictionary. This helps to determine information loss between before and after the
dictionary optimization. To compute information loss, we use Jensen-Shannon diver-
gence with adaptive weights to compare class distributions of each dictionary atom.
The advantage of Jensen-Shannon divergence is its computational e�ciency rather
than calculating information loss from mutual information
Conventional Entropy Quantifier and Modified Entropy Quantifiers for Face Recognition
AbstractThis paper presents theoretically simple, yet computationally efficient approach for face recognition. There are many transforms and entropy measures used in face recognition technology. Recognition rate is poor with binary and edge based recognition techniques. We employ the entropy concept to binary and edge images. We use Conventional Entropy Quantifier (CEQ) which counts only the transitions, and Modified Entropy Quantifier (MEQ) which considers the positions with transitions for measuring the entropy. The proposed entropy features possess good texture discriminative property. The experiments are conducted on benchmark databases using SVM and K-NN classifiers. Experimental results show the effectiveness of our system
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