Search results for "Image"

Detection of developmental dyslexia with machine learning using eye movement data

2021

Dyslexia is a common neurocognitive learning disorder that can seriously hinder individuals’ aspirations if not detected and treated early. Instead of costly diagnostic assessment made by experts, in the near future dyslexia might be identified with ease by automated analysis of eye movements during reading provided by embedded eye tracking technology. However, the diagnostic machine learning methods need to be optimized first. Previous studies with machine learning have been quite successful in identifying dyslexic readers, however, using contrasting groups with large performance differences between diagnosed and good readers. A practical challenge is to identify also individuals with bord…

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.

Extended constrained deformations: a new sculpturing tool

1999

Modelling tools tend to virtual sculpturing, in which a basic object is deformed by user supplied actions. The model we present aims to be generic: whatever the geometric description of the object, we can deform it to satisfy location constraints. Our model deforms the whole space, the image of a point is a blend of deformation functions with a projection matrix which allows the satisfaction of the constraints. The user can define the extent of the deformation (i.e. the part of the object to be deformed), the shape of the deformation function to create profiles and the displacement of the constraint points to be satisfied.

Computer graphics for quantum computation

2008

Chess recognition using 3D patterned illumination camera

2021

Computer Vision has been applied to augment traditional board games such as Chess for a number of reasons. While augmented reality enhances the gaming experience, the required additional hardware (e.g. head gear) is still not widely accepted in everyday leisure activities, and therefore, camera based methods have been developed to interface the computer with the real-life chess board. However, traditional 2D camera approaches suffer from ill-defined environmental conditions (lighting, viewing angle) and are therefore severely limited in their application. To answer this issue, we have incorporated a consumer-grade depth camera based on patterned illumination. We could show that in combinati…

Validating retinal fundus image analysis algorithms: issues and a proposal.

2013

This paper concerns the validation of automatic retinal image analysis (ARIA) algorithms. For reasons of space and consistency, we concentrate on the validation of algorithms processing color fundus camera images, currently the largest section of the ARIA literature. We sketch the context (imaging instruments and target tasks) of ARIA validation, summarizing the main image analysis and validation techniques. We then present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks, and capable of running …

Collaborative body sensor networks: Taxonomy and open challenges

2018

International audience; Single Body Sensor Networks (BSNs) have gained a lot of interest during the past few years. However, the need to monitor the activity of many individuals to assess the group status and take action accordingly has created a new research domain called Collaborative Body Sensor Network (CBSN). In such a new field, understanding CBSN's concept and challenges over the roots requires investigation to allow the development of suitable algorithms and protocols. Although there are many research studies in BSN, CBSN is still in its early phases and studies around it are very few. In this paper, we define and taxonomize CBSN, describe its architecture, and discuss its applicati…

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…

Information Abstraction from Crises Related Tweets Using Recurrent Neural Network

2016

Social media has become an important open communication medium during crises. The information shared about a crisis in social media is massive, complex, informal and heterogeneous, which makes extracting useful information a difficult task. This paper presents a first step towards an approach for information extraction from large Twitter data. In brief, we propose a Recurrent Neural Network based model for text generation able to produce a unique text capturing the general consensus of a large collection of twitter messages. The generated text is able to capture information about different crises from tens of thousand of tweets summarized only in a 2000 characters text.