Search results for "pattern"
showing 10 items of 4203 documents
Non-Model Based Method for an Automation of 3D Acquisition and Post-Processing
2008
Most of the automation for 3D acquisition concerns objects with simple shape, like mechanical parts. For cultural heritage artefacts, the process is more complex, and it doesn't exist general solution nowadays. This paper presents a method to generate a complete 3D model of cultural heritage artefacts. In a first step, MVC is used to solve the view planning problem. Then, holes remaining in 3D model are detected, and their features are calculated to finish acquisition. Different post-processing are applied on each view to increase quality of the 3D model. This procedure has been tested with simulated scanner, before being implemented on a motion system with five degrees of freedom.
Complex networks : application for texture characterization and classification
2008
This article describes a new method and approch of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we presente how to have good performance in texture classification. Image is represented by a complex networks : one pixel as a node. Node degree and clustering coefficient, using with traditionnal and extended hierarchical measurements, are used to characterize ”organisation” of textures.
Fast fringe pattern phase demodulation using FIR Hilbert transformers
2016
This paper suggests the use of FIR Hilbert transformers to extract the phase of fringe patterns. This method is computationally faster than any known spatial method that produces wrapped phase maps. Also, the algorithm does not require any parameters to be adjusted which are dependent upon the specific fringe pattern that is being processed, or upon the particular setup of the optical fringe projection system that is being used. It is therefore particularly suitable for full algorithmic automation. The accuracy and validity of the suggested method has been tested using both computer-generated and real fringe patterns. This novel algorithm has been proposed for its advantages in terms of com…
Deep CNN for IIF Images Classification in Autoimmune Diagnostics
2019
The diagnosis and monitoring of autoimmune diseases are very important problem in medicine. The most used test for this purpose is the antinuclear antibody (ANA) test. An indirect immunofluorescence (IIF) test performed by Human Epithelial type 2 (HEp-2) cells as substrate antigen is the most common methods to determine ANA. In this paper we present an automatic HEp-2 specimen system based on a convolutional neural network method able to classify IIF images. The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a k-nearest neighbors classifier. The class…
Deep multimodal fusion for semantic image segmentation: A survey
2021
International audience; Recent advances in deep learning have shown excellent performance in various scene understanding tasks. However, in some complex environments or under challenging conditions, it is necessary to employ multiple modalities that provide complementary information on the same scene. A variety of studies have demonstrated that deep multimodal fusion for semantic image segmentation achieves significant performance improvement. These fusion approaches take the benefits of multiple information sources and generate an optimal joint prediction automatically. This paper describes the essential background concepts of deep multimodal fusion and the relevant applications in compute…
A new approach to partial discharge detection under DC voltage
2018
The continuing development of HVDC power transmission systems presents many problems related to evaluation of the reliability of power system assets [1]-[5]. In this context the identification of insulation defects plays a key role in preventing unexpected failures of electrical components. Partial discharge (PD) measurement is a useful approach to assessing the condition of HV power apparatus and cables. Such measurements are also widely employed for HVAC systems. The inception mechanisms of PD in AC systems are well-known, and measurements are usually performed following the IEC 60270 standard [6]. PD measurements under DC voltage present complexities related to the nature of the phenomen…
Multi-modality of polysomnography signals’ fusion for automatic sleep scoring
2019
Abstract Objective The study aims to develop an automatic sleep scoring method by fusing different polysomnography (PSG) signals and further to investigate PSG signals’ contribution to the scoring result. Methods Eight combinations of four modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) were considered to find the optimal fusion of PSG signals. A total of 232 features, covering statistical characters, frequency characters, time-frequency characters, fractal characters, entropy characters and nonlinear characters, were derived from these PSG signals. To select the optimal features for each signal fusion, …
The Role of Employees’ Work Patterns and Office Type Fit (and Misfit) in the Relationships Between Employee Well-Being and Performance
2018
Nearly half of workers agree that their workspace is unsuitable for their work tasks. Furthermore, it is assumed that happy workers often perform better than unhappy ones. Nevertheless, due to the effect of the emotional-cognitive processes, the misfit between employees’ office type and their work patterns (complexity and interactivity) may hamper this relationship between well-being and performance. This diary study on 83 office workers ( n = 603 time points) combines information about work patterns identified by using cluster analysis with Neufert’s office type classification. Results show that the work pattern–office type (mis)fit moderates the relationship between well-being and perfor…
Combining Supervised and Unsupervised Learning to Discover Emotional Classes
2017
Most previous work in emotion recognition has fixed the available classes in advance, and attempted to classify samples into one of these classes using a supervised learning approach. In this paper, we present preliminary work on combining supervised and unsupervised learning to discover potential latent classes which were not initially considered. To illustrate the potential of this hybrid approach, we have used a Self-Organizing Map (SOM) to organize a large number of Electroencephalogram (EEG) signals from subjects watching videos, according to their internal structure. Results suggest that a more useful labelling scheme could be produced by analysing the resulting topology in relation t…
Learning Improved Feature Rankings through Decremental Input Pruning for Support Vector Based Drug Activity Prediction
2010
The use of certain machine learning and pattern recognition tools for automated pharmacological drug design has been recently introduced. Different families of learning algorithms and Support Vector Machines in particular have been applied to the task of associating observed chemical properties and pharmacological activities to certain kinds of representations of the candidate compounds. The purpose of this work, is to select an appropriate feature ordering from a large set of molecular descriptors usually used in the domain of Drug Activity Characterization. To this end, a new input pruning method is introduced and assessed with respect to commonly used feature ranking algorithms.