Search results for "Landmark"
showing 10 items of 44 documents
A Neural Solution for a Mobile Robot Navigation into Unknown Indoor Environments Using Visual Landmarks
1998
In this paper we present a neural solution for a mobile robot navigation into unknown indoor environments by using landmarks. Robot navigation task is implemented by two groups of processes based on MLP neural networks classifiers: a Low Level Vision System performs obstacle avoidance and corridor following, while an High Level Vision System extracts landmarks contents and performs goal directed navigation. A path-planner manages the two navigation systems and interacts with the robot hardware. The proposed solution is very strong and flexible and can be used to drive a mobile robot in real indoor environments. In the paper experimental results are also reported.
Insight into the noble crayfish morphological diversity: a geometric morphometric approach.
2022
The noble crayfish (Astacus astacus), a keystone species of high ecological, economic, and cultural importance in Europe, is threatened due to a long-term population decline caused by anthropogenic pressure on its habitats, the presence of non-indigenous invasive crayfish species and climate change. Since the effective protection of the remaining populations requires conservation measures based on the comprehensive knowledge of the species, including good understanding of its genetic and morphological variability, our aim was to study morphological features of the noble crayfish in Croatia using geometric morphometrics for the first time. We applied two-dimensional geometric morphometrics t…
Il segno nella città: il Jewish Museum a Berlino
2009
L’affermarsi della visione satellitare ha cambiato il modo di progettare/percepire le architetture, le città, il paesaggio urbano; è in questo senso che si può valutare appieno la forza del segno del museo ebraico nella città di Berlino, un museo, un contenitore che è già contenuto.
Quantitative comparison of motion history image variants for video-based depression assessment
2017
Abstract Depression is the most prevalent mood disorder and a leading cause of disability worldwide. Automated video-based analyses may afford objective measures to support clinical judgments. In the present paper, categorical depression assessment is addressed by proposing a novel variant of the Motion History Image (MHI) which considers Gabor-inhibited filtered data instead of the original image. Classification results obtained with this method on the AVEC’14 dataset are compared to those derived using (a) an earlier MHI variant, the Landmark Motion History Image (LMHI), and (b) the original MHI. The different motion representations were tested in several combinations of appearance-based …
A study on the reproducibility of cephalometric landmarks when undertaking a three-dimensional (3D) cephalometric analysis
2012
Objectives: Cone Beam Computerized Tomography (CBCT) allows the possibility of modifying some of the diagnostic tools used in orthodontics, such as cephalometry. The first step must be to study the characteristics of these devices in terms of accuracy and reliability of the most commonly used landmarks. The aims were 1- To assess intra and inter-observer reliability in the location of anatomical landmarks belonging to hard tissues of the skull in images taken with a CBCT device, 2- To determine which of those landmarks are more vs. less reliable and 3- To introduce planes of reference so as to create cephalometric analyses appropriated to the 3D reality. Study design: Fifteen patients who h…
Scaling Up a Metric Learning Algorithm for Image Recognition and Representation
2008
Maximally Collapsing Metric Learning is a recently proposed algorithm to estimate a metric matrix from labelled data. The purpose of this work is to extend this approach by considering a set of landmark points which can in principle reduce the cost per iteration in one order of magnitude. The proposal is in fact a generalized version of the original algorithm that can be applied to larger amounts of higher dimensional data. Exhaustive experimentation shows that very similar behavior at a lower cost is obtained for a wide range of the number of landmark points used.
Exudates as Landmarks Identified through FCM Clustering in Retinal Images
2020
The aim of this work was to develop a method for the automatic identification of exudates, using an unsupervised clustering approach. The ability to classify each pixel as belonging to an eventual exudate, as a warning of disease, allows for the tracking of a patient&rsquo
Supporting tourism with public interactive displays
2019
Interactive displays are common in public and semi-public areas, such as museums, shopping malls, train stations, and even on streets. Especially with the emergence of new ways of interaction, interactive displays could be introduced to popular tourist attractions to support tourism. The aim of this tutorial is to prepare its participants for designing such interactive public displays. The tutorial will first introduce participants to state of the art in interactive public displays. This will be followed by a city tour where a number of local attractions are visited. Finally, the participants will brainstorm and create concepts for novel interactive public displays that aim to improve the e…
Deep Learning Techniques for Depression Assessment
2018
Depression is a typical mood disorder, which affects a significant number of individuals worldwide at an increasing rate. Objective measures for early detection of signs related to depression could be beneficial for clinicians with regards to a decision support system. In this paper, assessment of depression is done by applying three deep learning techniques of Convolutional Neural Network (CNN). These techniques are transfer learning using AlexNet, fine-tuning using AlexNet and building an end to end CNN. The inputs of the CNNs are a combination of Motion History Image, Landmark Motion History Image and Gabor Motion History Image, and have been generated on a depression dataset. Accuracy o…
Statistical atlas based exudate segmentation
2013
Diabetic macular edema (DME) is characterized by hard exudates. In this article, we propose a novel statistical atlas based method for segmentation of such exudates. Any test fundus image is first warped on the atlas co-ordinate and then a distance map is obtained with the mean atlas image. This leaves behind the candidate lesions. Post-processing schemes are introduced for final segmentation of the exudate. Experiments with the publicly available HEI-MED data-set shows good performance of the method. A lesion localization fraction of 82.5% at 35% of non-lesion localization fraction on the FROC curve is obtained. The method is also compared to few most recent reference methods.