1,034 research outputs found
Vehicles Recognition Using Fuzzy Descriptors of Image Segments
In this paper a vision-based vehicles recognition method is presented.
Proposed method uses fuzzy description of image segments for automatic
recognition of vehicles recorded in image data. The description takes into
account selected geometrical properties and shape coefficients determined for
segments of reference image (vehicle model). The proposed method was
implemented using reasoning system with fuzzy rules. A vehicles recognition
algorithm was developed based on the fuzzy rules describing shape and
arrangement of the image segments that correspond to visible parts of a
vehicle. An extension of the algorithm with set of fuzzy rules defined for
different reference images (and various vehicle shapes) enables vehicles
classification in traffic scenes. The devised method is suitable for
application in video sensors for road traffic control and surveillance systems.Comment: The final publication is available at http://www.springerlink.co
FORGOTTEN AULACOTHORAX BOHEMAN, 1858, A SENIOR SYNONYM OF ORTHALTICA CROTCH, 1873 (COLEOPTERA: CHRYSOMELIDAE: GALERUCINAE: ALTICINI)
Discovery of the holotype of Aulacothorax exilis Boheman, 1858, originally described in Scydmaeninae (Staphylinidae), in the collections of Naturhistoriska Riksmuseet Stockholm in Sweden revealed that this species is actually a leaf beetle (Chrysomelidae) belonging to the genus until now known as Orthaltica Crotch, 1873. Because Aulacothorax has been available since 1858 and Orthaltica only since 1873, we here synonymize Orthaltica with Aulacothorax. Aulacothorax exilis is redescribed and illustrated
A Generalization of the Convex Kakeya Problem
Given a set of line segments in the plane, not necessarily finite, what is a
convex region of smallest area that contains a translate of each input segment?
This question can be seen as a generalization of Kakeya's problem of finding a
convex region of smallest area such that a needle can be rotated through 360
degrees within this region. We show that there is always an optimal region that
is a triangle, and we give an optimal \Theta(n log n)-time algorithm to compute
such a triangle for a given set of n segments. We also show that, if the goal
is to minimize the perimeter of the region instead of its area, then placing
the segments with their midpoint at the origin and taking their convex hull
results in an optimal solution. Finally, we show that for any compact convex
figure G, the smallest enclosing disk of G is a smallest-perimeter region
containing a translate of every rotated copy of G.Comment: 14 pages, 9 figure
Opaque Service Virtualisation: A Practical Tool for Emulating Endpoint Systems
Large enterprise software systems make many complex interactions with other
services in their environment. Developing and testing for production-like
conditions is therefore a very challenging task. Current approaches include
emulation of dependent services using either explicit modelling or
record-and-replay approaches. Models require deep knowledge of the target
services while record-and-replay is limited in accuracy. Both face
developmental and scaling issues. We present a new technique that improves the
accuracy of record-and-replay approaches, without requiring prior knowledge of
the service protocols. The approach uses Multiple Sequence Alignment to derive
message prototypes from recorded system interactions and a scheme to match
incoming request messages against prototypes to generate response messages. We
use a modified Needleman-Wunsch algorithm for distance calculation during
message matching. Our approach has shown greater than 99% accuracy for four
evaluated enterprise system messaging protocols. The approach has been
successfully integrated into the CA Service Virtualization commercial product
to complement its existing techniques.Comment: In Proceedings of the 38th International Conference on Software
Engineering Companion (pp. 202-211). arXiv admin note: text overlap with
arXiv:1510.0142
Using unstructured profile information for gender classification of Portuguese and English
This paper reports experiments on automatically detecting the gender of Twitter users, based on unstructured information found on their Twitter profile. A set of features previously proposed is evaluated on two datasets of English and Portuguese users, and their performance is assessed using several supervised and unsupervised approaches, including Naive Bayes variants, Logistic Regression, Support Vector Machines, Fuzzy c-Means clustering, and k-means. Results show that features perform well in both languages separately, but even best results were achieved when combining both languages. Supervised approaches reached 97.9 % accuracy, but Fuzzy c-Means also proved suitable for this task achieving 96.4 % accuracy.info:eu-repo/semantics/acceptedVersio
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Common mortality modeling and coherent forecasts. An empirical analysis of worldwide mortality data
A new common mortality modeling structure is presented for analyzing mortality dynamics for a pool of countries, under the framework of generalized linear models (GLM). The countries are first classified by fuzzy c-means cluster analysis in order to construct the common sparse age-period model structure for the mortality experience. Next, we propose a method to create the common sex difference age-period model structure and then use this to produce the residual age-periodmodel structure for each country and sex. The time related principal components are extrapolated using dynamic linear regression (DLR) models and coherent mortality forecasts are investigated. We make use of mortality data from the “Human Mortality Database”
Noise-robust method for image segmentation
Segmentation of noisy images is one of the most challenging problems in image analysis and any improvement of segmentation methods can highly influence the performance of many image processing applications. In automated image segmentation, the fuzzy c-means (FCM) clustering has been widely used because of its ability to model uncertainty within the data, applicability to multi-modal data and fairly robust behaviour. However, the standard FCM algorithm does not consider any information about the spatial linage context and is highly sensitive to noise and other imaging artefacts. Considering above mentioned problems, we developed a new FCM-based approach for the noise-robust fuzzy clustering and we present it in this paper. In this new iterative algorithm we incorporated both spatial and feature space information into the similarity measure and the membership function. We considered that spatial information depends on the relative location and features of the neighbouring pixels. The performance of the proposed algorithm is tested on synthetic image with different noise levels and real images. Experimental quantitative and qualitative segmentation results show that our method efficiently preserves the homogeneity of the regions and is more robust to noise than other FCM-based methods
FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification
This paper introduces a novel real-time Fuzzy Supervised Learning with Binary
Meta-Feature (FSL-BM) for big data classification task. The study of real-time
algorithms addresses several major concerns, which are namely: accuracy, memory
consumption, and ability to stretch assumptions and time complexity. Attaining
a fast computational model providing fuzzy logic and supervised learning is one
of the main challenges in the machine learning. In this research paper, we
present FSL-BM algorithm as an efficient solution of supervised learning with
fuzzy logic processing using binary meta-feature representation using Hamming
Distance and Hash function to relax assumptions. While many studies focused on
reducing time complexity and increasing accuracy during the last decade, the
novel contribution of this proposed solution comes through integration of
Hamming Distance, Hash function, binary meta-features, binary classification to
provide real time supervised method. Hash Tables (HT) component gives a fast
access to existing indices; and therefore, the generation of new indices in a
constant time complexity, which supersedes existing fuzzy supervised algorithms
with better or comparable results. To summarize, the main contribution of this
technique for real-time Fuzzy Supervised Learning is to represent hypothesis
through binary input as meta-feature space and creating the Fuzzy Supervised
Hash table to train and validate model.Comment: FICC201
Automated user modeling for personalized digital libraries
Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to
improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in
an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information
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