Search results for "Age class"
showing 10 items of 133 documents
Classification par méthodes d’apprentissage supervisé et faiblement superviséd’images multimodales pour l’aide au diagnostic du lentigo malin en derm…
2021
Carried out in collaboration with the Saint-Étienne University Hospital, this work provides additional information to help the skin diagnosis by providing new decision methods on Lentigo Maligna and Lentigo Maligna Melanoma pathologies. To this end, the modalities regularly used in clinical conditions are made available to this work and are orchestrated within a multimodal process. Among image modalities, may be mentioned the clinical photography, the dermatoscopy, and the confocal reflectance microscopy. Initially, the first steps of this manuscript focus on reflectance confocal microscopy as the work in computer diagnostic assistance is relatively underdeveloped, in particular on the dete…
What Conclusions does Rapid Image Classification by Eye Movements Provide for Machine Vision?
2008
Human ability to rapidly classify images of natural objects has been a matter of study for more than a decade. Recently eye movements have been exploited as a behavioural response, which has lead to alternative hypotheses of natural image processing. In this research, twelve volunteers made a movement towards a briefly displayed digital image if it was an animal, and a movement away otherwise. In both cases, the average response time was more than 400 milliseconds.
Region-based segmentation on depth images from a 3D reference surface for tree species recognition.
2013
International audience; The aim of the work presented in this paper is to develop a method for the automatic identification of tree species using Terrestrial Light Detection and Ranging (T-LiDAR) data. The approach that we propose analyses depth images built from 3D point clouds corresponding to a 30 cm segment of the tree trunk in order to extract characteristic shape features used for classifying the different tree species using the Random Forest classifier. We will present the method used to transform the 3D point cloud to a depth image and the region based segmentation method used to segment the depth images before shape features are computed on the segmented images. Our approach has be…
Multimodale Analyse von Interaktion im fremdsprachlichen Klassenzimmer
2016
The article offers a contribution to the interaction research in the foreign language teaching and learning. It starts with an overview of various research approaches to the foreign language teaching and learning, from the 60s to the present days. A multimodal analysis of an excerpt of videorecorded classroom interaction is then provided. The analysis is focused on several aspects of classroom participation and teaching sequences. Some implications of the present research for teachers' training are finally provided.
(In)congruence between presupposing teacher turns and the student’s epistemic stance : case analysis of teacher turns that presuppose the student to …
2017
Episteemisyyden eli tietoon pääsyn tutkiminen luokkahuonevuorovaikutuksessa on kasvattanut suosiotaan keskustelunanalyyttisessa tutkimuksessa viime vuosina. Kasvavan kiinnostuksen taustalla on ajatus siitä, että oppiminen tapahtuu vuorovaikutuksessa toisten kanssa, kun taas vuorovaikutusta ajaa eteenpäin osallistujien välinen tiedollinen epäsymmetria. Tämän tutkimuksen tarkoitus on lisätä tietoa episteemisyydestä luokkahuonevuorovaikutuksessa. Tarkastelin opettajan aloitevuoroja, joihin oli sisäänrakennettuna oletus oppilaan pääsystä tietoon eli oletus siitä, että oppilas tietää vastauksen kysymykseen. Lisäksi tarkastelin oppilaiden vastauksia opettajan kysymyksiin, sekä niitä seuraavia ope…
A deep learning segmentation approach to calories and weight estimation of food images
2019
Master's thesis Information- and communication technology IKT590 - University of Agder 2019 Today’s generation is very aware of what they are eating and the amountof calories in their food. Eating too many calories can lead to increasedweight, which has become a big health issue. A study from 2016 states thatmore than 1,9 billion adults are overweight where almost one third of theseare obese. Statistics from Norway show that 1 of 4 men and 1 of 5 womenare obese.Artificial Intelligence in general and deep learning in particular can be usedto help understand the content of eaten food. In this master thesis, wepropose a network to estimate the weight of food from a single image. Thisis done in…
Zero-shot Semantic Segmentation using Relation Network
2021
Zero-shot learning (ZSL) is widely studied in recent years to solve the problem of lacking annotations. Currently, most studies on ZSL are for image classification and object detection. But, zero-shot semantic segmentation, pixel level classification, is still at its early stage. Therefore, this work proposes to extend a zero-shot image classification model, Relation Network (RN), to semantic segmentation tasks. We modified the structure of RN based on other state-of-the-arts semantic segmentation models (i.e. U-Net and DeepLab) and utilizes word embeddings from Caltech-UCSD Birds 200-2011 attributes and natural language processing models (i.e. word2vec and fastText). Because meta-learning …
Time Unification on Local Binary Patterns Three Orthogonal Planes for Facial Expression Recognition
2019
International audience; Machine learning has known a tremendous growth within the last years, and lately, thanks to that, some computer vision algorithms started to access what is difficult or even impossible to perceive by the human eye. While deep learning based computer vision algorithms have made themselves more and more present in the recent years, more classical feature extraction methods, such as the ones based on Local Binary Patterns (LBP), still present a non negligible interest, especially when dealing with small datasets. Furthermore, this operator has proven to be quite useful for facial emotions and human gestures recognition in general. Micro-Expression (ME) classification is…
The Truth is Out There : Focusing on Smaller to Guess Bigger in Image Classification
2023
In Artificial Intelligence (AI) in general and in Machine Learning (ML) in particular, which are important and integral components of modern Industry 4.0, we often deal with uncertainty, e.g., lack of complete information about the objects we are classifying, recognizing, diagnosing, etc. Traditionally, uncertainty is considered to be a problem especially in the responsible use of AI and ML tools in the smart manufacturing domain. However, in this study, we aim not to fight with but rather to benefit from the uncertainty to improve the classification performance in supervised ML. Our objective is a kind of uncertainty-driven technique to improve the performance of Convolutional Neural Netwo…
Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance
2022
Accurate sleep stage classification is vital to assess sleep quality and diagnose sleep disorders. Numerous deep learning based models have been designed for accomplishing this labor automatically. However, the class imbalance problem existing in polysomnography (PSG) datasets has been barely investigated in previous studies, which is one of the most challenging obstacles for the real-world sleep staging application. To address this issue, this paper proposes novel methods with signal-driven and image-driven ways of noise addition to balance the imbalanced relationship in the training dataset samples. We evaluate the effectiveness of the proposed methods which are integrated into a convolut…