Search results for "Computer Vision"

Gesture recognition using low-cost devices: Techniques, applications, perspectives

2016

Negli ultimi anni abbiamo assistito ad una grande diffusione dei cosiddetti “Kinect-like devices”, ovvero dispositivi basati su un insieme di sensori a basso costo, che consentono di ottenere un’immagine di profondità della scena ripresa. L’alta accessibilità di questi dispositivi, principalmente in termini di costi, ne ha facilitato la diffusione nell’ambito del riconoscimento dei gesti in numerose applicazioni, sia commerciali che di ricerca. In questo articolo saranno inizialmente illustrati i principi generali su cui si fondano le principali tecniche utilizzate per riconoscere i gesti, sfruttando i dati ottenibili dai dispositivi “Kinect-like”. Successivamente, saranno presentati alcuni…

Quantification of the microstructural evolution of polycrystalline fabrics using FAME: Application to in situ deformation of ice

2014

Abstract In geology, glaciology and material science new technological advances result in an ever increasing amount of data and datasets, in particular when in situ experiments are conducted. Rapid, rigorous and reliable statistical treatment is needed to allow researchers to access these large datasets for further analysis. Here, we present FAME (Fabric Analyser based Microstructure Evaluation), a suite of Matlab® scripts that utilize the Matlab® open-source toolboxes MTEX and PolyLX (optional) for rapid quantification of thin section data. The data has been collected using an automated Fabric Analyser at a spatial resolution of 5 μm/pixel. From the dataset, grain maps are reconstructed an…

Soft Computing and Image Analysis

2000

The paper describes a soft approach to solve image analysis problems. Theory of fuzzy-sets has been used to implement most of the algorithms described in the paper. Soft approaches can be useful to extend mathematical morphology operators on gray level images and to describe the shape of dotted objects. Examples on real data are also provided.

Analyzing Protein-Protein Spatial-Temporal Dependencies from Image Sequences Using Fuzzy Temporal Random Sets

2008

Total Internal Reflection Fluorescence Microscopy (TIRFM) allows us to image fluorescenttagged proteins near the plasma membrane of living cells with high spatial-temporal resolution. Using TIRFM imaging of GFP-tagged clathrin endocytic proteins, areas of fluorescence are observed as overlapping spots of different sizes and durations. Standard procedures to measure protein-protein colocalization of dual labeled samples threshold the original graylevel images to segment areas covered by different proteins. This binary logic is not appropriate as it leaves a free tuning parameter which can influence the conclusions. Moreover, these procedures rely on simple statistical analysis based on corre…

Cover Feature: New Approach to 1,4-Benzoxazin-3-ones by Electrochemical C−H Amination (Chem. Eur. J. 50/2017)

2017

Cover Feature: Direct Metal‐ and Reagent‐Free Sulfonylation of Phenols with Sodium Sulfinates by Electrosynthesis (Chem. Eur. J. 28/2019)

2019

Blood vessel segmentation and width estimation in ultra-wide field scanning laser ophthalmoscopy.

2014

Features of the retinal vasculature, such as vessel widths, are considered biomarkers for systemic disease. The aim of this work is to present a supervised approach to vessel segmentation in ultra-wide field of view scanning laser ophthalmoscope (UWFoV SLO) images and to evaluate its performance in terms of segmentation and vessel width estimation accuracy. The results of the proposed method are compared with ground truth measurements from human observers and with existing state-of-the-art techniques developed for fundus camera images that we optimized for UWFoV SLO images. Our algorithm is based on multi-scale matched filters, a neural network classifier and hysteresis thresholding. After …

Conjugate Gradient Method for Brain Magnetic Resonance Images Segmentation

2018

Part 8: Pattern Recognition and Image Processing; International audience; Image segmentation is the process of partitioning the image into regions of interest in order to provide a meaningful representation of information. Nowadays, segmentation has become a necessity in many practical medical imaging methods as locating tumors and diseases. Hidden Markov Random Field model is one of several techniques used in image segmentation. It provides an elegant way to model the segmentation process. This modeling leads to the minimization of an objective function. Conjugate Gradient algorithm (CG) is one of the best known optimization techniques. This paper proposes the use of the nonlinear Conjugat…

New Error Measures to Evaluate Features on Three-Dimensional Scenes

2011

In this paper new error measures to evaluate image features in three-dimensional scenes are proposed and reviewed. The proposed error measures are designed to take into account feature shapes, and ground truth data can be easily estimated. As other approaches, they are not error-free and a quantitative evaluation is given according to the number of wrong matches and mismatches in order to assess their validity

Outdoor Scenes Pixel-wise Semantic Segmentation using Polarimetry and Fully Convolutional Network

2019

International audience; In this paper, we propose a novel method for pixel-wise scene segmentation application using polarimetry. To address the difficulty of detecting highly reflective areas such as water and windows, we use the angle and degree of polarization of these areas, obtained by processing images from a polarimetric camera. A deep learning framework, based on encoder-decoder architecture, is used for the segmentation of regions of interest. Different methods of augmentation have been developed to obtain a sufficient amount of data, while preserving the physical properties of the polarimetric images. Moreover, we introduce a new dataset comprising both RGB and polarimetric images…