Banknote recognition systems have many applications in the modern world of automatic monetary transaction machines. They are traditionally based on simple classifiers applied over manually selected areas. A new solution in this field, borrowed by content-based image retrieval (CBIR), which is based on dense scale-invariant feature transform features in a bag-of-words framework followed by a support vector machine (SVM) classifier, is explored. The proposed computer vision system for banknote recognition, on one hand, enables recognition at high accuracy and speed, and, on the other hand, provides basis for further applications, e.g., counterfeit detection and fitness test. This approach makes the system robust to various defects, which may occur during image acquisition or during circulation life of banknote. We implemented and tested on an embedded platform three state-of-the-art classification techniques [SVM, artificial neural network (ANN), and hidden Markov model (HMM)]. The comparative results are reported for accuracy with different sizes of the training datasets and with various types of datasets. In this framework, the SVM classifier outperforms ANN and HMM on the basis of speed and accuracy on our embedded platform.
In this paper we propose to integrate the recently introduces ORB descriptors in the currently favored approach
for image classification, that is the Bag of Words model. In particular the problem to be solved is to provide
a clustering method able to deal with the binary string nature of the ORB descriptors. We suggest to use a
k-means like approach, called k-majority, substituting Euclidean distance with Hamming distance and majority
selected vector as the new cluster center. Results combining this new approach with other features are provided
over the ImageCLEF 2011 dataset.
In this paper we focus on the problem of automatic classification of melanocytic lesions, aiming at identifying the
presence of reticular patterns. The recognition of reticular lesions is an important step in the description of the
pigmented network, in order to obtain meaningful diagnostic information. Parameters like color, size or symmetry
could benefit from the knowledge of having a reticular or non-reticular lesion. The detection of network patterns is
performed with a three-steps procedure. The first step is the localization of line points, by means of the line points
detection algorithm, firstly described by Steger. The second step is the linking of such points into a line considering
the direction of the line at its endpoints and the number of line points connected to these. Finally a third step
discards the meshes which couldn't be closed at the end of the linking procedure and the ones characterized by
anomalous values of area or circularity. The number of the valid meshes left and their area with respect to the whole
area of the lesion are the inputs of a discriminant function which classifies the lesions into reticular and non-reticular.
This approach was tested on two balanced (both sets are formed by 50 reticular and 50 non-reticular
images) training and testing sets. We obtained above 86% correct classification of the reticular and non-reticular
lesions on real skin images, with a specificity value never lower than 92%.
Automatic segmentation of skin lesions in clinical images is a very
challenging task; it is necessary for visual analysis of the edges,
shape and colors of the lesions to support the melanoma diagnosis,
but, at the same time, it is cumbersome since lesions (both naevi
and melanomas) do not have regular shape, uniform color, or univocal
structure. Most of the approaches adopt unsupervised color
clustering. This works compares the most spread color clustering
algorithms, namely median cut, k-means, fuzzy-c means and mean shift
applied to a method for automatic border extraction, providing an
evaluation of the upper bound in accuracy that can be reached with
these approaches. Different tests have been performed to examine the
influence of the choice of the parameter settings with respect to
the performances of the algorithms. Then a new supervised learning
phase is proposed to select the best number of clusters and to
segment the lesion automatically. Examples have been carried out in
a large database of medical images, manually segmented by
dermatologists. From these experiments mean shift was resulted the
best technique, in term of sensitivity and specificity. Finally, a
qualitative evaluation of the goodness of segmentation has been
validated by the human experts too, confirming the results of the
quantitative comparison.
In this paper we focus on the problem of automatically registering dermatological images, because even if different
products are available, most of them share the problem of a limited field of view on the skin. A possible solution is then
the composition of multiple takes of the same lesion with digital software, such as that for panorama images creation.
In this work, to perform an automatic selection of matching points the Harris Corner Detector is used, and to cope with
outlier couples we employed the RANSAC method. Projective mapping is then used to match the two images. Given a
set of correspondence points, Singular Value Decomposition was used to compute the transform parameters.
At this point the two images need to be blended together. One initial assumption is often implicitly made: the aim is to
merge two rectangular images. But when merging occurs between more than two images iteratively, this assumption will
fail. To cope with differently shaped images, we employed the Distance Transform and provided a weighted merging of
images.
Different tests were conducted with dermatological images, both with standard rectangular frame and with not typical
shapes, as for example a ring due to the objective and lens selection. The successive composition of different circular
images with other blending functions, such as the Hat function, doesn't correctly get rid of the border and residuals of the
circular mask are still visible. By applying Distance Transform blending, the result produced is insensitive of the outer
shape of the image.
Conference Committee Involvement (4)
Multimedia Content Access: Algorithms and Systems IV
21 January 2010 | San Jose, California, United States
Multimedia Content Access: Algorithms and Systems III
21 January 2009 | San Jose, California, United States
Multimedia Content Access: Algorithms and Systems II
30 January 2008 | San Jose, California, United States
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.