Search results for "Image processing"
showing 10 items of 3285 documents
Improved locally adaptive least-squares detection of differences in images
2007
We introduce a method for change detection under nonuniform changes of intensity using an improved least-squares method. A locally adaptive normalizing window is correlated with the two images, and a morphological postprocessing is then applied to isolate objects that have been added or removed from the scene. We use a modification of the least-squares solution to get rid of clutter caused by intensity changes that do not satisfy the model assumed for the least-squares solution.
Real-time characterization of aspect flaws on warped surface by artificial vision
1997
Artificial vision is an efficient means of assuring the quality of a certain class of products. The vision system must respect the industrial constraints, in particular, the production rate. The geometrical features of flaws are pertinent information used for the acceptance of the controlled product. This article presents a real-time algorithm for the geometrical characterization of defects located on warped objects. The algorithms described enable the characterization of defects by their size and their 2-D shape. Both parameters are calculated in real time by simple reference to a look-up table. The 2-D shape is obtained by a geometrical transform and an interpolation. The efficiency of th…
Supporting Autonomy in Agent Oriented Methodologies
2016
Designing a software solution for a complex systems is always a demanding task, it becomes much more complex if we consider to design a multi agent system where agents have to exhibit autonomy; which abstractions and which concepts to take into consideration when using a design methodology we would like to support autonomy? In this paper, we answer this question by studying and analyzing literature on the concept of agents in order to establish the basic set of concepts an agent oriented methodology has to deal with.
Extended scale-invariant pattern recognition with white-light illumination.
2000
A previous method of obtaining scale-invariance detection with white-light illumination has been improved on. We were able to detect different scaled versions of the target up to a magnification factor equal to 2. We simultaneously detected several versions in the same scene, because each scale factor is codified in a different wavelength. Experimental results demonstrate the proposed technique and show the utility of the method.
A Windowing strategy for Distributed Data Mining optimized through GPUs
2017
Abstract This paper introduces an optimized Windowing based strategy for inducing decision trees in Distributed Data Mining scenarios. Windowing consists in selecting a sample of the available training examples (the window) to induce a decision tree with an usual algorithm, e.g., J48; finding instances not covered by this tree (counter examples) in the remaining training examples, adding them to the window to induce a new tree; and repeating until a termination criterion is met. In this way, the number of training examples required to induce the tree is reduced considerably, while maintaining the expected accuracy levels; which is paid in terms of time performance. Our proposed enhancements…
A comparative study of best spectral bands selection systems for face recognition
2014
Multispectral images (MI) have shown promising capabilities to solve problems resulting from high illumination variation in face recognition. However, the use of MI, with the huge number of captured spectral bands for each subject, is impractical unless a system for best spectral bands selection (BSBS) is used. In this work, first we give an up to date overview of the existing BSBS techniques proposed for face recognition. We aim to highlight the imporatnce of this component of MI based systems. The reviewed techniques are then experimented using the multispectral face database IRIS - M3 to compare their performances. To the best of our knowledge this is the first study that reviews and com…
Acoustic Detection of Moving Vehicles
2018
This chapter outlines a robust algorithm to detect the arrival of a vehicle of arbitrary type when other noises are present. It is done via analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. To achieve it with minimum number of false alarms, a construction of a training database of acoustic signatures of signals emitted by vehicles using the distribution of the energies among blocks of wavelet packet coefficients (waveband spectra, see Sect. 4.6) is combined with a procedure of random search for a near-optimal footprint (RSNOFP). The number of false alarms in the detection is minimized even under severe conditions such as: signals emi…
Maximum Lifetime of the Wireless Sensor Network and the Gossip Problem
2018
In the gossip problem each node of the graph G possesses a unique piece of information - the gossip message. A sequence of one-way or two-way communications between pair of nodes is made to spread the messages so that any node of the graph knows all the gossips. The question is, what is the minimum number of calls between pairs of nodes needed to exchange all gossip messages? The solution to the two-way communication gossip problem is that \(2N-4\) calls (\(N\ge 4\)) suffice if and only if the graph contains a four cycle subgraph. For one-way communication problem the classical results states that in a strongly connected graph \(2N-2\) calls (\(N\ge 4\)) suffice. In this paper we consider t…
Adaptive Threshold, Wavelet and Hilbert Transform for QRS Detection in Electrocardiogram Signals
2017
This paper combines Hilbert and Wavelet transforms and an adaptive threshold technique to detect the QRS complex of electrocardiogram signals. The method is performed in a window framework. First, the Wavelet transform is applied to the ECG signal to remove noise. Next, the Hilbert transform is applied to detect dominant peak points in the signal. Finally, the adaptive threshold technique is applied to detect R-peaks, Q, and S points. The performance of the algorithm is evaluated against the MIT-BIH arrhythmia database, and the numerical results indicated significant detection accuracy.
Manifold Learning with High Dimensional Model Representations
2020
Manifold learning methods are very efficient methods for hyperspectral image (HSI) analysis but, unless specifically designed, they cannot provide an explicit embedding map readily applicable to out-of-sample data. A common assumption to deal with the problem is that the transformation between the high input dimensional space and the (typically low) latent space is linear. This is a particularly strong assumption, especially when dealing with hyperspectral images due to the well-known nonlinear nature of the data. To address this problem, a manifold learning method based on High Dimensional Model Representation (HDMR) is proposed, which enables to present a nonlinear embedding function to p…