6,708 research outputs found
A statistical multiresolution approach for face recognition using structural hidden Markov models
This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy
Iris Recognition Using Scattering Transform and Textural Features
Iris recognition has drawn a lot of attention since the mid-twentieth
century. Among all biometric features, iris is known to possess a rich set of
features. Different features have been used to perform iris recognition in the
past. In this paper, two powerful sets of features are introduced to be used
for iris recognition: scattering transform-based features and textural
features. PCA is also applied on the extracted features to reduce the
dimensionality of the feature vector while preserving most of the information
of its initial value. Minimum distance classifier is used to perform template
matching for each new test sample. The proposed scheme is tested on a
well-known iris database, and showed promising results with the best accuracy
rate of 99.2%
Fingerprint Recognition Using Translation Invariant Scattering Network
Fingerprint recognition has drawn a lot of attention during last decades.
Different features and algorithms have been used for fingerprint recognition in
the past. In this paper, a powerful image representation called scattering
transform/network, is used for recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering representation is similar to sift
descriptors and the higher layers capture higher frequency content of the
signal. After extraction of scattering features, their dimensionality is
reduced by applying principal component analysis (PCA). At the end, multi-class
SVM is used to perform template matching for the recognition task. The proposed
scheme is tested on a well-known fingerprint database and has shown promising
results with the best accuracy rate of 98\%.Comment: IEEE Signal Processing in Medicine and Biology Symposium, 201
Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation
This paper proposes to generate and to use barcodes to annotate medical
images and/or their regions of interest such as organs, tumors and tissue
types. A multitude of efficient feature-based image retrieval methods already
exist that can assign a query image to a certain image class. Visual
annotations may help to increase the retrieval accuracy if combined with
existing feature-based classification paradigms. Whereas with annotations we
usually mean textual descriptions, in this paper barcode annotations are
proposed. In particular, Radon barcodes (RBC) are introduced. As well, local
binary patterns (LBP) and local Radon binary patterns (LRBP) are implemented as
barcodes. The IRMA x-ray dataset with 12,677 training images and 1,733 test
images is used to verify how barcodes could facilitate image retrieval.Comment: To be published in proceedings of The IEEE International Conference
on Image Processing (ICIP 2015), September 27-30, 2015, Quebec City, Canad
Multiscale Adaptive Representation of Signals: I. The Basic Framework
We introduce a framework for designing multi-scale, adaptive, shift-invariant
frames and bi-frames for representing signals. The new framework, called
AdaFrame, improves over dictionary learning-based techniques in terms of
computational efficiency at inference time. It improves classical multi-scale
basis such as wavelet frames in terms of coding efficiency. It provides an
attractive alternative to dictionary learning-based techniques for low level
signal processing tasks, such as compression and denoising, as well as high
level tasks, such as feature extraction for object recognition. Connections
with deep convolutional networks are also discussed. In particular, the
proposed framework reveals a drawback in the commonly used approach for
visualizing the activations of the intermediate layers in convolutional
networks, and suggests a natural alternative
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