Search results for "Method"
showing 10 items of 13253 documents
Features extraction on complex images
2005
The accessibility of inexpensive and powerful computers has allowed true digital holography to be used for industrial inspection using microscopy. This technique allows the capture of a complex image (i.e., one containing magnitude and phase), and the reconstruction of the phase and magnitude information. Digital holograms give a new dimension to texture analysis, since the topology information can be used as an additional way to extract features. This new technique can be used to extend previous work on the image segmentation of patterned wafers for defect detection. The paper presents a comparison between the features obtained using Gabor filtering on complex images under illumination and…
Correlation-Based and Contextual Merit-Based Ensemble Feature Selection
2001
Recent research has proved the benefits of using an ensemble of diverse and accurate base classifiers for classification problems. In this paper the focus is on producing diverse ensembles with the aid of three feature selection heuristics based on two approaches: correlation and contextual merit -based ones. We have developed an algorithm and experimented with it to evaluate and compare the three feature selection heuristics on ten data sets from UCI Repository. On average, simple correlation-based ensemble has the superiority in accuracy. The contextual merit -based heuristics seem to include too many features in the initial ensembles and iterations were most successful with it.
Distance-based relevance feedback using a hybrid interactive genetic algorithm for image retrieval
2011
Content-based image retrieval (CBIR) systems aim to provide a means to find pictures in large repositories without using any other information except the own content of the images, which is usually represented as a feature vector extracted from low-level descriptors. This paper describes a CBIR algorithm which combines relevance feedback, evolutionary computation concepts and distance-based learning in an attempt to reduce the existing gap between the high level semantic content of the images and the information provided by their low-level descriptors. In particular, a framework which is independent from the particular features used is presented. The effect of different crossover strategies…
A one class classifier for Signal identification: a biological case study
2008
The paper describes an application of a one-class KNN to identify different signal patterns embedded in a noise structured background. The problem become harder whenever only one pattern is well represented in the signal, in such cases one class classifier techniques are more indicated. The classification phase is applied after a preprocessing phase based on a Multi Layer Model (MLM) that provides a preliminary signal segmentation in an interval feature space. The one-class KNN has been tested on synthetic data that simulate microarray data for the identification of nucleosomes and linker regions across DNA. Results have shown a good recognition rate on synthetic data for nucleosome and lin…
Comprehensive Strategy for Proton Chemical Shift Prediction: Linear Prediction with Nonlinear Corrections
2014
A fast 3D/4D structure-sensitive procedure was developed and assessed for the chemical shift prediction of protons bonded to sp3carbons, which poses the maybe greatest challenge in the NMR spectral parameter prediction. The LPNC (Linear Prediction with Nonlinear Corrections) approach combines three well-established multivariate methods viz. the principal component regression (PCR), the random forest (RF) algorithm, and the k nearest neighbors (kNN) method. The role of RF is to find nonlinear corrections for the PCR predicted shifts, while kNN is used to take full advantage of similar chemical environments. Two basic molecular models were also compared and discussed: in the MC model the desc…
Innovative modelling techniques in computer vision
1996
Abstract The paper is concerned with two of main research activities currently carried on at the Computer Science and Artificial Intelligence lab of DIE. The first part deals with hybrid artificial vision models, intended to provide object recognition and classification capabilities to an autonomous intelligen system. In this framework, a system recovering 3-D shape information from grey-level images of a scene, building a geometric representation of the scene in terms of superquadrics at the geometric level, and reasoning about the scene at the symbolic level is described. In the second part, attention is focused on automatic indexing of image databases. JACOB, a prototypal system allowing…
New Areas of Application of Comparable Corpora
2019
This chapter describes several approaches of using comparable corpora beyond the area of MT for under-resourced languages, which is the primary focus of the ACCURAT project. Section 7.1, which is based on Rapp and Zock (Automatic dictionary expansion using non-parallel corpora. In: A. Fink, B. Lausen, W. Seidel, & A. Ultsch (Eds.) Advances in Data Analysis, Data Handling and Business Intelligence. Proceedings of the 32nd Annual Meeting of the GfKl, 2008. Springer, Heidelberg, 2010), addresses the task of creating resources for bilingual dictionaries using a seed lexicon; Sect. 7.2 (based on Rapp et al., Identifying word translations from comparable documents without a seed lexicon. Proceedi…
Elaborating the WARE Method for eParticipation Requirements
2010
Published version of a chapter in the book Information Systems Developement, 2010, 785-792. Also available from the publisher at: http://dx.doi.org/10.1007/b137171_82 eParticipation systems are often directly targeted at citizens. However, as a group of potential users, citizens form a heterogeneous and unpredictable group, which makes requirements elicitation a challenging issue. Based on recently developed ideas for wide audience requirement engineering (WARE), this chapter discusses and elaborates a method for eliciting citizen requirements for eParticipation. The method elaboration was conducted in connection with a project in southern Norway, where young people’s requirements for becom…
Randomized Hough Transform for Ellipse Detection with Result Clustering
2005
Our research is focused on the development of robust machine vision algorithms for pattern recognition. We want to provide robotic systems the ability to understand more on the external real world. In this paper, we describe a method for detecting ellipses in real world images using the randomized Hough transform with result clustering. A preprocessing phase is used in which real world images are transformed - noise reduction, greyscale transform, edge detection and final binarization - in order to be processed by the actual ellipse detector. The ellipse detector filters out false ellipses that may interfere with the final results. Due to the fact that usually more "virtual" ellipses are de…
A collaborative tool for designing and enacting design processes
2009
Today several approaches using Situational Method Engineering paradigm exist, each of them proposes methods and techniques for developing ad-hoc design processes. In this context heavy efforts were spent in the construction of appropriate tools that could help method engineers in producing a specific design process and in using it. We developed a tool called Metameth for supporting the design process definition and its enactment. Metameth is implemented as a multi-agent system, where each agent is capable of reasoning and adapting itself in order to support the designer in performing different kinds of design activities.