Pattern Recognition

Improved road crack detection based on one-class Parzen density estimation and entropy reduction

by José Jasnau Caeiro

Automatic Rural Road Extraction from Sets of Connected Edge Points

by José Jasnau Caeiro

Analysis and Prediction of Air Quality Data with the Gamma Classifier

by Itzamá López Yáñez

A New Classifier Based on Associative Memories

by Itzamá López Yáñez

Perfect Recall on the Lernmatrix

by Itzamá López Yáñez

Classifying Patterns in Bioinformatics Databases by Using Alpha-Beta Associative Memories

by Itzamá López Yáñez

Feature Extraction from Vein Images using Spatial Information and Chain Codes

by Anika Pflug

The pattern formed by subcutaneous blood vessels is unique attribute of each individual and can therefore be used as a... more The pattern formed by subcutaneous blood vessels is unique attribute of each individual and can therefore be used as a biometric characteristic. Exploiting the specific near infrared light absorption properties of blood, the capture procedure for this biometric characteristic is convenient and allows contact-less sensors. However, image skeletons extracted from vein images are often unstable, because the raw vein images suffer from low contrast. We propose a new chain code based feature en- coding method, using spatial and orientation properties of vein patterns, which is capable of dealing with noisy and unstable image skeletons. Chain code comparison and a selection of preprocessing methods have been evaluated in a series of different experiments in single and multi-reference scenarios on two different vein image databases. The experiments showed that chain code comparison outperforms minutiae-based approaches and similarity based mix matching.

A writer-independent off-line signature verification system based on signature morphology

by Lopamudra Kundu

The Web-based Face-to-Face Economy

by Fabian Bethge

Event-driven Data Mining Techniques for Automotive Fault Diagnosis

by Chaitanya Sankavaram

Authors: C. Sankavaram, A. Kodali, K. Pattipati, S. Singh, and P. Bandyopadhyay

21st International Workshop on Principles of Diagnosis, Portland, OR, October 2010

Th e Strategies of Using the Generalizing Patterns of the Primary School 5th Grade Students

by Halil Eksi

Dilek TANIŞLI, Aynur ÖZDAŞ
Educational Sciences: Th eory & Practice
9 (3) • Summer 2009 • 1485-1497

Th e main purpose of this study is to determine the strategies of using the generalizing
patterns of the primary... more Th e main purpose of this study is to determine the strategies of using the generalizing
patterns of the primary fifth grade students. Th e practice of this research is conducted on
twelve students, which have high, middle and low success levels. Task-based interviews
and students journals are used as the tools for data collection. For the analysis of the data,
a classification method including “data reduction”, “data display” and “drawing conclusion
and verification” are used. At the end of the research, it is seen that the visual and numerical
approaches are adopted in the generalization of patterns and the visual approach
is made easy for generalization, as well. In generally, the present strategies in the generalizing
of patterns are also taken into account of near or far generalizing. Th e recursive strategies
are used in the near generalizing. However, the explicit strategies are determined
in using far generalizing.

A feature extraction method for marble tile classification

by Ugur Murat Leloglu

Content-based Filtering in On-line Social Networks

by Marco Vanetti

Marco Vanetti, Elisabetta Binaghi, Barbara Carminati, Moreno Carullo and Elena Ferrari.

Published in "Privacy and Security Issues in Data Mining and Machine Learning - Proceedings of International ECML/PKDD Workshop, PSDML 2010". Pages 127-140, Barcelona, Spain, September 24, 2010.

This paper proposes a system enforcing content-based message filtering for On-line Social Networks (OSNs). The system... more This paper proposes a system enforcing content-based message filtering for On-line Social Networks (OSNs). The system allows OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, that allows a user to customize the filtering criteria to be applied to their walls, and a Machine Learning based soft classifier automatically labelling messages in support of content-based filtering.

A System to Filter Unwanted Messages from OSN User Walls

by Marco Vanetti

Marco Vanetti, Elisabetta Binaghi, Elena Ferrari, Barbara Carminati and Moreno Carullo.

To be published in "IEEE Transactions on Knowledge and Data Engineering (TKDE)".

One fundamental issue in today On-line Social Networks (OSNs) is to give users the ability to control the messages... more One fundamental issue in today On-line Social Networks (OSNs) is to give users the ability to control the messages posted on their own private space to avoid that unwanted content is displayed. Up to now OSNs provide little support to this requirement. To fill the gap, in this paper, we propose a system allowing OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, that allows users to customize the filtering criteria to be applied to their walls, and a Machine Learning based soft classifier automatically labeling messages in support of content-based filtering.

Object Segmentation using Multiple Neural Networks for Commercial Offers Visual Search

by Marco Vanetti

Ignazio Gallo, Angelo Nodari and Marco Vanetti.

Published in "Proocedings of EANN 2011 - Engineering Applications of Neural Networks". Corfu, Greece, 2011.

We describe a web application that takes advantage of new computer vision techniques to allow the user to make... more We describe a web application that takes advantage of new computer vision techniques to allow the user to make searches based on visual similarity of color and texture related to the object of interest. We use a supervised neural network strategy to segment different classes of objects. A strength of this solution is the high speed in generalization of the trained neural networks, in order to obtain an object segmentation in real time. Information about the segmented object, such as color and texture, are extracted and indexed as text descriptions. Our case study is the online commercial offers domain where each offer is composed by text and images. Many successful experiments were done on real datasets in the fashion field.

Learning Object Segmentation Using A Multi Network Segment Classification Approach

by Marco Vanetti

Simone Albertini, Ignazio Gallo, Marco Vanetti and Angelo Nodari.

Published in "Proceedings of VISAPP 2012 - International Conference on Computer Vision Theory and Applications". Rome, Italy, 2012.

In this study we propose a new strategy to perform an object segmentation using a multi neural network approach. We... more In this study we propose a new strategy to perform an object segmentation using a multi neural network approach. We started extending our previously presented object detection method applying a new segment based classification strategy. The result obtained is a segmentation map post processed by a phase that exploits the GrabCut algorithm to obtain a fairly precise and sharp edges of the object of interest in a full automatic way. We tested the new strategy on a clothing commercial dataset obtaining a substantial improvement on the quality of the segmentation results compared with our previous method. The segment classification approach we propose achieves the same improvement on a subset of the Pascal VOC 2011 dataset which is a recent standard segmentation dataset, obtaining a result which is inline with the state of the art.

A novel interactive visualization framework for gene expression analysis

by Omneya Khaled

Fingerprint Recognition System: Design & Analysis

by Dibyendu Nath

Fingerprint Recognition is one of the research hotspots in Biometrics. It refers to the automated method of verifying... more Fingerprint Recognition is one of the research hotspots in Biometrics. It refers to the automated method of verifying a match between two human fingerprints. It is essentially a challenging pattern recognition problem where two competing error rates: the False Accept Rate (FAR) and the False Reject Rate (FRR) need to be minimized. Advancement of computing capabilities led to the development of Automated Fingerprint Authentication Systems (AFIS) and this led to extensive research especially in the last two decades. In this paper, we attempt to give a comprehensive scoping of the fingerprint recognition problem and address its major design and implementation issues as well as give an insight into its future prospects.

Recognizing novel three-dimensional objects by summing signals from parts and views

by David Foster

Visually recognizing objects at different orientations and distances has been assumed to depend either on extracting... more Visually recognizing objects at different orientations and distances has been assumed to depend either on extracting from the retinal image a viewpoint-invariant, typically three-dimensional (3D) structure, such as object parts, or on mentally transforming two-dimensional (2D) views. To test how these processes might interact with each other, an experiment was performed in which observers discriminated images of novel, computer-generated, 3D objects, differing by rotations in 3D space and in the number of parts (in principle, a viewpoint-invariant, ‘non-accidental’ property) or in the curvature, length or angle of join of their parts (in principle, each a viewpoint-dependent, metric property), such that the discriminatory cue varied along a common physical scale. Although differences in the number of parts were more readily discriminated than differences in metric properties, they showed almost exactly the same orientation dependence. Overall, visual performance proved remarkably lawful: for both long (2 s) and short (100 ms) display durations, it could be summarized by a simple, compact equation with one term representing generalized viewpoint-invariant parts-based processing of 3D object structure, including metric structure, and another term representing structure-invariant processing of 2D views. Object discriminability was determined by summing signals from these two independent processes.

Selective internal operations in the recognition of locally and globally point-inverted patterns

by David Foster

Performance in discriminating rotated 'same' patterns from 'different' patterns may decrease with rotation angle up to... more Performance in discriminating rotated 'same' patterns from 'different' patterns may decrease with rotation angle up to about 90° and then increase with angle up to 180°. This anomalously improved performance under 180° pattern rotation or point-inversion can be explained by assuming that patterns are internally represented in terms of local features and their spatial-order relations ('left of', 'above', etc.), and that, in pattern comparison, an efficient internal sense-reversal operation occurs (transforming 'left of' to 'right of', etc.). Previous experiments suggested that local features and spatial relations could not be efficiently separated in some pattern-comparison tasks. This hypothesis was tested by measuring 'same-different' discrimination performance under four transformations: point-inversion i 1 of the whole pattern, point-inversion lF of local features alone, point-inversion lp of local-feature positions alone, and identity transformation Id. The results suggested that internal sense-reversal operations could be applied selectively and efficiently, provided that local features were well separated. Under this condition performances for lF and l were about the same whereas performance for lp was significantly worse, the latter performance resulting possibly from an attempt to apply internal global and local sense-reversal operations serially.