Search results for "Computer vision"
showing 10 items of 2353 documents
A genetic algorithm for combined topology and shape optimisations
2003
A method to find optimal topology and shape of structures is presented. With the first the optimal distribution of an assigned mass is found using an approach based on homogenisation theory, that seeks in which elements of a meshed domain it is present mass; with the second the discontinuous boundaries are smoothed. The problem of the optimal topology search has an ON/OFF nature and has suggested the employment of genetic algorithms. Thus in this paper a genetic algorithm has been developed, which uses as design variables, in the topology optimisation, the relative densities (with respect to effective material density) 0 or 1 of each element of the structure and, in the shape one, the coord…
Sample–tip coupling efficiencies of the photon-scanning tunneling microscope
1991
The photon-scanning tunneling microscope is the photon analog to the electron-scanning tunneling microscope. It uses the evanescent field due to the total internal reflection of a light beam in a prism, modulated by a sample attached to the prism. The exponential decay of the evanescent field is characterized by the penetration depth dp and depends on the angle of incidence θ, the wavelength, and the polarization of the incident beam. The 1/e decay lengths range from 150 to 265 nm as deduced from the expression of the electric-field intensity in the rarer medium for θ = π/2. If we place another optically transparent medium near the surface, frustrated total reflection occurs. It is shown th…
Recent Experimental Results with the PSTM: - Observation of a Step on a Quartz Surface. - Spatial Spectroscopy of Microwaveguides
1993
The Photon Scanning Tunneling Microscope (PSTM) is based on the frustration of the total internal reflected beam by the end of an optical fiber. Till today it has been used to obtain topographic information generally for smooth samples. In this paper we report two different kinds of experimental results. First, when the sample is in the form of a step, our measurements demonstrate how the images, obtained in the constant intensity mode, depend on the orientation of the incident beam of light with respect to the step. Next, we show that the first derivative of the collected intensity with respect to the probe-sample distance at each point of the sample yields to a new kind of image named her…
Ensemble Feature Selection Based on the Contextual Merit
2001
Recent research has proved the benefits of using ensembles of classifiers for classification problems. Ensembles constructed by machine learning methods manipulating the training set are used to create diverse sets of accurate classifiers. Different feature selection techniques based on applying different heuristics for generating base classifiers can be adjusted to specific domain characteristics. In this paper we consider and experiment with the contextual feature merit measure as a feature selection heuristic. We use the diversity of an ensemble as evaluation function in our new algorithm with a refinement cycle. We have evaluated our algorithm on seven data sets from UCI. The experiment…
Null Space Based Image Recognition Using Incremental Eigendecomposition
2011
An incremental approach to the discriminative common vector (DCV) method for image recognition is considered. Discriminative projections are tackled in the particular context in which new training data becomes available and learned subspaces may need continuous updating. Starting from incremental eigendecomposition of scatter matrices, an efficient updating rule based on projections and orthogonalization is given. The corresponding algorithm has been empirically assessed and compared to its batch counterpart. The same good properties and performance results of the original method are kept but with a dramatic decrease in the computation needed.
Learning Similarity Scores by Using a Family of Distance Functions in Multiple Feature Spaces
2017
There exist a large number of distance functions that allow one to measure similarity between feature vectors and thus can be used for ranking purposes. When multiple representations of the same object are available, distances in each representation space may be combined to produce a single similarity score. In this paper, we present a method to build such a similarity ranking out of a family of distance functions. Unlike other approaches that aim to select the best distance function for a particular context, we use several distances and combine them in a convenient way. To this end, we adopt a classical similarity learning approach and face the problem as a standard supervised machine lea…
Honeybees can recognise images of complex natural scenes for use as potential landmarks
2008
SUMMARY The ability to navigate long distances to find rewarding flowers and return home is a key factor in the survival of honeybees (Apis mellifera). To reliably perform this task, bees combine both odometric and landmark cues,which potentially creates a dilemma since environments rich in odometric cues might be poor in salient landmark cues, and vice versa. In the present study, honeybees were provided with differential conditioning to images of complex natural scenes, in order to determine if they could reliably learn to discriminate between very similar scenes, and to recognise a learnt scene from a novel distractor scene. Choices made by individual bees were modelled with signal detec…
Texture analysis with statistical methods for wheat ear extraction
2007
In agronomic domain, the simplification of crop counting, necessary for yield prediction and agronomic studies, is an important project for technical institutes such as Arvalis. Although the main objective of our global project is to conceive a mobile robot for natural image acquisition directly in a field, Arvalis has proposed us first to detect by image processing the number of wheat ears in images before to count them, which will allow to obtain the first component of the yield. In this paper we compare different texture image segmentation techniques based on feature extraction by first and higher order statistical methods which have been applied on our images. The extracted features are…
Normal and Abnormal Tissue Classification in Positron Emission Tomography Oncological Studies
2018
Positron Emission Tomography (PET) imaging is increasingly used in radiotherapy environment as well as for staging and assessing treatment response. The ability to classify PET tissues, as normal versus abnormal tissues, is crucial for medical analysis and interpretation. For this reason, a system for classifying PET area is implemented and validated. The proposed classification is carried out using k-nearest neighbor (KNN) method with the stratified K-Fold Cross-Validation strategy to enhance the classifier reliability. A dataset of eighty oncological patients are collected for system training and validation. For every patient, lesion (abnormal tissue) and background (normal tissue around …
Segmentation and virtual exploration of tracheobronchial trees
2003
Abstract The tracheobronchial tree as part of the lung is part of one of the most important organs of the human body. Inhaled air is distributed to the alveolus where oxygen and carbon dioxide exchange between air and blood takes place. In this paper, we introduce the virtual endoscopy system VIVENDI to perform virtual inspections of tracheobronchial trees based on their segmentation and of the complementing blood vessels. It is based on a hybrid segmentation pipeline which enables the segmentation of vascular and tracheobronchial structures down to the seventh generation of the bronchi.