pinecone/core-2020-05-10-deduplication huggingface dataset:
Multi-resolution image analysis for vehicle detection

Proceeding of: Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005Computer Vision can provide a great deal of assistance to Intelligent Vehicles. In this paper an Advanced Driver Assistance Systems for Vehicle Detection is presented. A geometric model of the vehicle is defined where its energy function includes information of the shape and symmetry of the vehicle and the shadow it produces. A genetic algorithm finds the optimum parameter values. As the algorithm receives information from a road detection module some geometric restrictions can be applied. A multi-resolution approach is used to speed up the algorithm and work in realtime. Examples of real images are shown to validate the algorithm.Publicad
Evaluation of distances between color image segmentations

Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), 2005, Estoril (Portugal)We illustrate the problem of comparing images by means of their color segmentations. A group of seven distances are proposed within the frame of the Integrated Region Matching distance and the employ of Multivariate Gaussian Distributions (MGD) for the color description of image regions. The performance of these distances is examined in tasks such as image retrieval and object recognition using the two segmentation algorithms in [1] and [2]. The best overall results are obtained for both tasks using the graph–partition approach along with the Fréchet distance, outperforming other distances in comparing MGDs.Peer Reviewe
A comparative study of highlights detection and elimination by color morphology and polar color models

In this paper, we present a comparative study of detection and elimination of highlights in real color images of any type of material. We use different polar color spaces for the automatic highlight detection (HLS, HSV and L1-norme). To eliminate the highlights detected, we use a new connected vectorial filter of color mathematical morphology which it operates exclusively on bright zones, reducing the high cost of processing of the connected filter and avoiding over-simplification. The new method proposed here achieves good results and it not requires costly multiple-view systems or stereo images
Parallel Perceptrons, Activation Margins and Imbalanced Training Set Pruning

The final publication is available at Springer via http://dx.doi.org/10.1007/11492542_6Proceedings of Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Part IIA natural way to deal with training samples in imbalanced class problems is to prune them removing redundant patterns, easy to classify and probably over represented, and label noisy patterns that belonging to one class are labelled as members of another. This allows classifier construction to focus on borderline patterns, likely to be the most informative ones. To appropriately define the above subsets, in this work we will use as base classifiers the so–called parallel perceptrons, a novel approach to committee machine training that allows, among other things, to naturally define margins for hidden unit activations. We shall use these margins to define the above pattern types and to iteratively perform subsample selections in an initial training set that enhance classification accuracy and allow for a balanced classifier performance even when class sizes are greatly different.With partial support of Spain’s CICyT, TIC 01–572, TIN2004–0767
Balanced boosting with parallel perceptrons

The final publication is available at Springer via http://dx.doi.org/10.1007/11494669_26Proceedings of 8th International Work-Conference on Artificial Neural Networks, IWANN 2005, Vilanova i la Geltrú, Barcelona, Spain, June 8-10, 2005.Boosting constructs a weighted classifier out of possibly weak learners by successively concentrating on those patterns harder to classify. While giving excellent results in many problems, its performance can deteriorate in the presence of patterns with incorrect labels. In this work we shall use parallel perceptrons (PP), a novel approach to the classical committee machines, to detect whether a pattern’s label may not be correct and also whether it is redundant in the sense of being well represented in the training sample by many other similar patterns. Among other things, PP allow to naturally define margins for hidden unit activations, that we shall use to define the above pattern types. This pattern type classification allows a more nuanced approach to boosting. In particular, the procedure we shall propose, balanced boosting, uses it to modify boosting distribution updates. As we shall illustrate numerically, balanced boosting gives very good results on relatively hard classification problems, particularly in some that present a marked imbalance between class sizes.With partial support of Spain’s CICyT, TIC 01–572
Using a Mahalanobis-like distance to train Radial Basis Neural Networks

Proceeding of: International Work-Conference on Artificial Neural Networks (IWANN 2005)Radial Basis Neural Networks (RBNN) can approximate any regular function and have a faster training phase than other similar neural networks. However, the activation of each neuron depends on the euclidean distance between a pattern and the neuron center. Therefore, the activation function is symmetrical and all attributes are considered equally relevant. This could be solved by altering the metric used in the activation function (i.e. using non-symmetrical metrics). The Mahalanobis distance is such a metric, that takes into account the variability of the attributes and their correlations. However, this distance is computed directly from the variance-covariance matrix and does not consider the accuracy of the learning algorithm. In this paper, we propose to use a generalized euclidean metric, following the Mahalanobis structure, but evolved by a Genetic Algorithm (GA). This GA searches for the distance matrix that minimizes the error produced by a fixed RBNN. Our approach has been tested on two domains and positive results have been observed in both cases
Inter-spike-intervals Analysis of Poisson Like Hardware Synthetic AER Generation

Address-Event-Representation (AER) is a communication protocol
for transferring images between chips, originally developed for bio-inspired
image processing systems. Such systems may consist of a complicated
hierarchical structure with many chips that transmit images among them in real
time, while performing some processing (for example, convolutions). In
developing AER based systems it is very convenient to have available some
kind of means of generating AER streams from on-computer stored images. In
this paper we present a hardware method for generating AER streams in real
time from a sequence of images stored in a computer’s memory. The
Kolmogorov-Smirnov test has been applied to quantify that this method follows
a Poisson distribution of the spikes. A USB-AER board and a PCI-AER board,
developed by our RTCAR group, have been used.European Commission IST-2001-34124Ministerio de Ciencia y Tecnología TIC-2003-08164-C03-0
Using PSOMs to learn inverse kinematics through virtual decomposition of the robot

International Workshop on Artificial Neural Networks (IWANN), 2005, Barcelona (España)We propose a technique to speed up the learning of the inverse kinematics of a robot manipulator by decomposing it into two or more virtual robot arms. Unlike previous decomposition approaches, this one does not place any requirement on the robot architecture and, thus, it is completely general. Parametrized Self-Organizing Maps (PSOM) are particularly adequate for this type of learning, and permit comparing results obtained directly and through the decomposition. Experimentation shows that time reductions of up to two orders of magnitude are easily attained.This work was supported by the project 'Planificador de trayectorias para sistemas robotizados de arquitectura arbitraria' (J-00930).Peer Reviewe
Achieving a general, formal, and decidable approach to the OR-join in workflow using reset nets

Workflow languages offer constructs for coordinating tasks. Among these constructs are various types of splits and joins. One type of join, which shows up in various incarnations, is the OR-join. Different approaches assign a different (often only intuitive) semantics to this type of join, though they do share the common theme that synchronisation is only to be performed for active threads. Depending on context assumptions this behaviour may be relatively easy to deal with, though in general its semantics is complicated, both from a definition point of view (in terms of formally capturing a desired intuitive semantics) and from a computational point of view (how does one determine whether an OR-join is enabled?). In this paper the concept of OR-join is examined in detail in the context of the workflow language YAWL, a powerful workflow language designed to support a collection of workflow patterns and inspired by Petri nets. The OR-join’s definition is adapted from an earlier proposal and an algorithmic approach towards determining OR-join enablement is examined. This approach exploits a link that is proposed between YAWL and Reset nets, a variant of Petri nets with a special type of arc that can remove all tokens from a place
CompAr: Ensuring Safe Around Advice Composition

International audienceAdvanced techniques in separation of concerns such as Aspect-Oriented Programming, help to develop more maintainable and more efficient applications by providing means for modularizing crosscutting concerns. However, conflicts may appear when several concerns need to be composed for the same application, especially when dealing with around advice. We call this problem the Aspect Composition Issue (ACI). Based on our experience in programming aspects, this paper presents a language called CompAr, which allows the programmer to abstractly define an execution domain, the advice codes, and their execution constraints. The CompAr compiler then evaluates the definitions in order to check if the execution constraints are fulfilled. Using a concrete AOP case study, we show how to use the CompAr language in order to detect and avoid ACIs
Guaranteeing Resource Bounds for Component Software

Component software is software that has been assembled from various pieces of standardized, reusable computer programs, so-called components. Executing component software creates instances of these components. For several reasons, for example, limited resources and/or application requirements, it can be important to have control over the number of such instances.

In our previous work, we have given an abstract component language and a type system which ensures that the number of simultaneously active instances of any component never exceeds a sharp bound expressed in the type. The language featured instantiation and reuse of components, as well as sequential composition, choice and scope.

This work extends the previous one to include a parallel composition. Moreover, we improve on the operational semantics by using a small-step reduction relation. As a result, we can prove the soundness property of our static type system using the technique of Wright and Felleisen
Partial order Semantics of Sequence Diagrams for Mobility.

This is the author's draft of a paper published by LNCS and reproduced with kind permission of Springer Science and Business Media. The paper is available at http://www.springerlink.com/content/9ktp13ne6u4ch8nx/?p=d3876ef7b7c747cab3b9cf4f4d0fef45&pi=7There exist different formalism for mobile system specification, but until very recently, there was no satisfactory graphical notation for modelling of such systems. In a previous paper, we have introduced the so-called Sequence Diagrams for Mobility (SDM), a graphical notations based on UML Sequence Diagram. This notation has been used in several case studies and proved very useful. In this paper we introduce a formal, partial order based semantics for SDM. We define the notion of run and show how to figure out the system topology from the information contained in a run. We formalize the zoom-out abstraction mechanism introduced in a previous paper and show that its application does not depend on the particular order it is applied. We formalise also the notion of lifeline introduced informally in the previous paper. We integrate our semantics with UML2.0 and show that they fit well together. We explain our approach using series of examples
Automatic assignment of Wikipedia encyclopedic entries to WordNet synsets

The final publication is available at Springer via http://dx.doi.org/10.1007/11495772_59Proceedings of Third International Atlantic Web Intelligence Conference, AWIC 2005, Lodz, Poland, June 6-9, 2005.We describe an approach taken for automatically associating entries from an on-line encyclopedia with concepts in an ontology or a lexical semantic network. It has been tested with the Simple English Wikipedia and WordNet, although it can be used with other resources. The accuracy in disambiguating the sense of the encyclopedia entries reaches 91.11% (83.89% for polysemous words). It will be applied to enriching ontologies with encyclopedic knowledge.This work has been sponsored by CICYT, project number TIC2002-0194
Dragon: A Fast Word Based Stream Cipher

This paper presents Dragon, a new stream cipher constructed using a single word based non-linear feedback shift register and a non-linear filter function with memory. Dragon uses a variable length key and initialisation vector of 128 or 256 bits, and produces 64 bits of keystream per iteration. At the heart of Dragon are two highly optimised 8 × 32 s-boxes. Dragon uses simple operations on 32-bit words to provide a high degree of efficiency in a wide variety of environments, making it highly competitive when compared with other word based stream ciphers. The components of Dragon are designed to resist all known attacks
A Flexible and Modular Framework for Implementing Infrastructures for Global Computing

We present a Java software framework for building infrastructures to support the development of applications for systems where mobility and network awareness are key issues. The framework is particularly useful to develop run-time support for languages oriented towards global computing. It enables platform designers to customize communication protocols and network architectures and guarantees transparency of name management and code mobility in distributed environments. The key features are illustrated by means of a couple of simple case studies
A framework for understanding the factors influencing pair programming success

Pair programming is one of the more controversial aspects of several Agile system development methods, in particular eXtreme Programming (XP). Various studies have assessed factors that either drive the success or suggest advantages (and disadvantages) of pair programming.\ud
In this exploratory study the literature on pair programming is examined and factors distilled. These factors are then compared and contrasted with those discovered in our recent Delphi study of pair programming.\ud
Gallis et al. (2003) have proposed an initial framework aimed at providing a comprehensive identification of the major factors impacting team programming situations including pair programming. However, this\ud
study demonstrates that the framework should be extended to include an additional category of factors that relate to organizational matters. These factors will be further refined, and used to develop and empirically evaluate a conceptual model of pair programming (success)
Maximal digital straight segments and convergence of discrete geometric estimators

International audienceDiscrete geometric estimators approach geometric quantities on digitized shapes without any knowledge of the continuous shape. A classical yet difficult problem is to show that an estimator asymptotically converges toward the true geometric quantity as the resolution increases. We study here the convergence of local estimators based on Digital Straight Segment (DSS) recognition. It is closely linked to the asymptotic growth of maximal DSS, for which we show bounds both about their number and sizes. These results not only give better insights about digitized curves but indicate that curvature estimators based on local DSS recognition are not likely to converge. We indeed invalidate an hypothesis which was essential in the only known convergence theorem of a discrete curvature estimator. The proof involves results from arithmetic properties of digital lines, digital convexity, combinatorics, continued fractions and random polytopes
A Memory Architecture and Contextual Reasoning Framework for Cognitive Vision

One of the key requirements for a cognitive vision system to support reasoning is the possession of an effective mechanism to exploit context both for scene interpretation and for action planning. Context can be used effectively provided the system is endowed with a conducive memory architecture that supports contextual reasoning at all levels of processing, as well as a contextual reasoning framework. In this paper we describe a unified apparatus for reasoning using context, cast in a Bayesian reasoning framework. We also describe a modular memory architecture developed as part of the VAMPIRE* vision system which allows the system to store raw video data at the lowest level and its semantic annotation of monotonically increasing abstraction at the higher levels. By way of illustration, we use as an application for the memory system the automatic annotation of a tennis match
Levelset and B-spline deformable model techniques for image segmentation: a pragmatic comparative study

International audienceDeformable contours are now widely used in image segmentation, using different models, criteria and numerical schemes. Some theoretical comparisons between some deformable model methods have already been published. Yet, very few experimental comparative studies on real data have been reported. In this paper,we compare a levelset with a B-spline based deformable model approach in order to understand the mechanisms involved in these widely used methods and to compare both evolution and results on various kinds of image segmentation problems. In general, both methods yield similar results. However, specific differences appear when considering particular problems
Generation, Renderingand Animation of Polygon Tree Models

<p>This thesis contains a set of contributions that focuses on challenges in visualization of polygon models of trees, in particular models of tree stems and branches. These challenges cover a wide range of the visualization research field, including generation of polygon models of trees, hardware accelerated visualization and animation of trees from parametric systems, and surface modelling and tessellation for tree stems.</p><p>To generate polygon models of trees, an existing approach for automatically generating polygon models of branching vascular transportation systems, typically blood vessels, is extended to handle tree stems. The approach allowed for automation of the generation process, while maintaining smoothness at branching areas. The approach is used to generate polygon models of trees.</p><p>For hardware accelerated visualization and animation of trees from parametric systems, methods for visualization of trees described by parametric descriptions are adapted and extended to run on dedicated graphic processing hardware. To allow for animation of trees visualized in such a manner, an animation process that is hardware accelerated is developed using knowledge gained from work on hardware acceleration approaches in the field of image processing.</p><p>For surface modelling and tessellation for visualization of trees, concepts, ideas and early work are presented. The basic concept is that by working with a coarse polygon model of a tree during animation (or by generating a coarse polygon model of a tree after animation is done); one can tessellate this coarse model in hardware in real-time to a much higher degree then earlier approaches. This will then enable one to visualize trees stems of a higher Level of Detail (LOD) and at higher speeds than were previously achievable.</p><p>Although the contributions in this thesis cover a wide range of challenges from the visualization research field, the main focus has been on generation, animation and visualization of tree stems and branches for use in real-time applications. Especially the concept of hardware acceleration has become more of a focus, as graphic processing hardware has become more and more powerful both in speed and especially in functionality. The contributions will hopefully help in future development of even better methods for visualizing trees and plants.</p>PhD i informasjons- og kommunikasjonsteknologiPhD in Information and Communications Technolog
Conic Sections and Meet Intersections in Geometric Algebra

This paper first gives a brief overview over some interesting descriptions of
conic sections, showing formulations in the three geometric algebras of
Euclidean spaces, projective spaces, and the conformal model of Euclidean
space. Second the conformal model descriptions of a subset of conic sections
are listed in parametrizations specific for the use in the main part of the
paper. In the third main part the meets of lines and circles, and of spheres
and planes are calculated for all cases of real and virtual intersections. In
the discussion special attention is on the hyperbolic carriers of the virtual
intersections.Comment: 15 pages, 8 figure
Boosting parallel perceptrons for label noise reduction in classification problems

The final publication is available at Springer via http://dx.doi.org/10.1007/11499305_60Proceedings of First International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2005, Las Palmas, Canary Islands, Spain, June 15-18, 2005Boosting combines an ensemble of weak learners to construct a new weighted classifier that is often more accurate than any of its components. The construction of such learners, whose training sets depend on the performance of the previous members of the ensemble, is carried out by successively focusing on those patterns harder to classify. This fact deteriorates boosting’s results when dealing with malicious noise as, for instance, mislabeled training examples. In order to detect and avoid those noisy examples during the learning process, we propose the use of Parallel Perceptrons. Among other things, these novel machines allow to naturally define margins for hidden unit activations. We shall use these margins to detect which patterns may have an incorrect label and also which are safe, in the sense of being well represented in the training sample by many other similar patterns. As candidates for being noisy examples we shall reduce the weights of the former ones, and as a support for the overall detection procedure we shall augment the weights of the latter ones.With partial support of Spain’s CICyT, TIC 01–572, TIN 2004–0767