Search results for "Computer Vision and Pattern Recognition"

A Hardware and Secure Pseudorandom Generator for Constrained Devices

2018

Hardware security for an Internet of Things or cyber physical system drives the need for ubiquitous cryptography to different sensing infrastructures in these fields. In particular, generating strong cryptographic keys on such resource-constrained device depends on a lightweight and cryptographically secure random number generator. In this research work, we have introduced a new hardware chaos-based pseudorandom number generator, which is mainly based on the deletion of an Hamilton cycle within the $N$ -cube (or on the vectorial negation), plus one single permutation. We have rigorously proven the chaotic behavior and cryptographically secure property of the whole proposal: the mid-term eff…

Reliable polygonal approximations of imaged real objects through dominant point detection

1998

Abstract The problem of dominant point detection is posed, taking into account what usually happens in practice. The algorithms found in the literature often prove their performance with laboratory contours, but the shapes in real images present noise, quantization, and high inter and intra-shape variability. These effects are analyzed and solutions to them are proposed. We will also focus on the conditions for an efficient (few points) and precise (low error) dominant point extraction that preserves the original shape. A measurement of the committed error (optimization error, E 0 ) that takes into account both aspects is defined for studying this feature.

An offline/real-time artifact rejection strategy to improve the classification of multi-channel evoked potentials

2008

The primary goal of this paper is to improve the classification of multi-channel evoked potentials (EPs) by introducing a temporal domain artifact detection strategy and using this strategy to (a) evaluate how the performance of classifiers is affected by artifacts and (b) show how the performance can be improved by detecting and rejecting artifacts in offline and real-time classification experiments. Using a pattern recognition approach, an artifact is defined in this study as any signal that may lead to inaccurate classifier parameter estimation and inaccurate testing. The temporal domain artifact detection tests include: a within-channel standard deviation (STD) test that can detect sign…

User grouping and power allocation in NOMA systems: a novel semi-supervised reinforcement learning-based solution

2022

Author's accepted manuscript In this paper, we present a pioneering solution to the problem of user grouping and power allocation in non-orthogonal multiple access (NOMA) systems. The problem is highly pertinent because NOMA is a well-recognized technique for future mobile radio systems. The salient and difcult issues associated with NOMA systems involve the task of grouping users together into the prespecifed time slots, which are augmented with the question of determining how much power should be allocated to the respective users. This problem is, in and of itself, NP-hard. Our solution is the frst reported reinforcement learning (RL)-based solution, which attempts to resolve parts of thi…

Towards more relevance-oriented data mining research

2008

Data mining (DM) research has successfully developed advanced DM techniques and algorithms over the last few decades, and many organisations have great expectations to take more benefit of their data warehouses in decision making. Currently, the strong focus of most DM-researchers is still only on technology-oriented topics. Commonly the DM research has several stakeholders, the major of which can be divided into internal and external ones each having their own point of view, and which are at least partly conflicting. The most important internal groups of stakeholders are the DM research community and academics in other disciplines. The most important external stakeholder groups are manager…

Complexity reduction in efficient prototype-based classification

2006

Corrigendum to three papers that deal with “Anti”-Bayesian Pattern Recognition [Pattern Recognition]

2014

In the papers 1 (Thomas and Oommen, 2013), 2 (Oommen and Thomas, 2014) and 3 (Thomas and Oommen, 2013), and their associated conference versions cited in those papers, we had introduced a new method of so-called "Anti"-Bayesian Pattern Recognition (PR) which achieved the classification using only a few (sometimes as few as two) points distant from the mean. While the PR strategy, in and of itself, is accurate, the claim that it was based on the Order Statistics (OS) of the distributions of the features is not. The PR and classification results are rather founded on the symmetric quantiles and not on the symmetric OSs. This brief paper corrects the flawed claim presented in those papers. Hig…

Hybrid architecture for shape reconstruction and object recognition

1998

The proposed architecture is aimed to recover 3-D- shape information from gray-level images of a scene; to build a geometric representation of the scene in terms of geometric primitives; and to reason about the scene. The novelty of the architecture is in fact the integration of different approaches: symbolic reasoning techniques typical of knowledge representation in artificial intelligence, algorithmic capabilities typical of artificial vision schemes, and analogue techniques typical of artificial neural networks. Experimental results obtained by means of an implemented version of the proposed architecture acting on real scene images are reported to illustrate the system capabilities.

Connectionist models of face processing: A survey

1994

Abstract Connectionist models of face recognition, identification, and categorization have appeared recently in several disciplines, including psychology, computer science, and engineering. We present a review of these models with the goal of complementing a recent survey by Samal and Iyengar [Pattern Recognition25, 65–77 (1992)] of nonconnectionist approaches to the problem of the automatic face recognition. We concentrate on models that use linear autoassociative networks, nonlinear autoassociative (or compression) and/or heteroassociative backpropagation networks. One advantage of these models over some nonconnectionist approaches is that analyzable features emerge naturally from image-b…

Regularized RBF Networks for Hyperspectral Data Classification

2004

In this paper, we analyze several regularized types of Radial Basis Function (RBF) Networks for crop classification using hyperspectral images. We compare the regularized RBF neural network with Support Vector Machines (SVM) using the RBF kernel, and AdaBoost Regularized (ABR) algorithm using RBF bases, in terms of accuracy and robustness. Several scenarios of increasing input space dimensionality are tested for six images containing six crop classes. Also, regularization, sparseness, and knowledge extraction are paid attention.