Search results for "Deep Learning"
showing 10 items of 337 documents
Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
2021
Road networks are critical infrastructures within any region and it is imperative to maintain their conditions for safe and effective movement of goods and services. Road Management, therefore, plays a key role to ensure consistent efficient operation. However, significant resources are required to perform necessary maintenance activities to achieve and maintain high levels of service. Pavement maintenance can typically be very expensive and decisions are needed concerning planning and prioritizing interventions. Data are key towards enabling adequate maintenance planning but in many instances, there is limited available information especially in small or under-resourced urban road authorit…
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…
WATCHING PEOPLE: ALGORITHMS TO STUDY HUMAN MOTION AND ACTIVITIES
2020
Nowadays human motion analysis is one of the most active research topics in Computer Vision and it is receiving an increasing attention from both the industrial and scientific communities. The growing interest in human motion analysis is motivated by the increasing number of promising applications, ranging from surveillance, human–computer interaction, virtual reality to healthcare, sports, computer games and video conferencing, just to name a few. The aim of this thesis is to give an overview of the various tasks involved in visual motion analysis of the human body and to present the issues and possible solutions related to it. In this thesis, visual motion analysis is categorized into thr…
Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks
2021
Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weath…
A method for anomaly detection in hyperspectral images, using deep convolutional autoencoders
2017
Menetelmä poikkeavuuksien havaitsemiseen hyperspektrikuvista käyttäen syviä konvolutiivisia autoenkoodereita. Poikkeavuuksien havaitseminen kuvista, erityisesti hyperspektraalisista kuvista, on hankalaa. Kun ongelmaan yhdistetään ennalta tuntematon data ja poikkeavuudet, muodostuu ongelma vielä laajemmaksi. Spektraalisten poikkeavuuksien havaitsemiseen on kehitetty useita eri menetelmiä, mutta spatiaalisten poikkeavuuksien havaitseminen on huomattavasti hankalempaa. Tässä työssä esitellään uudenkaltainen menetelmä sekä spatiaalisten että spektraalisten poikkeavuuksien samanaikaiseen havaitsemiseen. Menetelmä on suunniteltu erityisesti spektraaliselle datalle, mutta soveltuu myös perinteisil…
On Assessing Vulnerabilities of the 5G Networks to Adversarial Examples
2022
The use of artificial intelligence and machine learning is recognized as the key enabler for 5G mobile networks which would allow service providers to tackle the network complexity and ensure security, reliability and allocation of the necessary resources to their customers in a dynamic, robust and trustworthy way. Dependability of the future generation networks on accurate and timely performance of its artificial intelligence components means that disturbance in the functionality of these components may have negative impact on the entire network. As a result, there is an increasing concern about the vulnerability of intelligent machine learning driven frameworks to adversarial effects. In …
Agile Deep Learning UAVs Operating in Smart Spaces : Collective Intelligence Versus “Mission-Impossible”
2018
The environments, in which we all live, are known to be complex and unpredictable. The complete discovery of these environments aiming to take full control over them is a “mission-impossible”, however, still in our common agenda. People intend to make their living spaces smarter utilizing innovations from the Internet of Things and Artificial Intelligence. Unmanned aerial vehicles (UAVs) as very dynamic, autonomous and intelligent things capable to discover and control large areas are becoming important “inhabitants” within existing and future smart cities. Our concern in this paper is to challenge the potential of UAVs in situations, which are evolving fast in a way unseen before, e.g., em…
Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs
2019
Abstract The T cell repertoire is composed of T cell receptors (TCR) selected by their cognate MHC-peptides and naive TCR that do not bind known peptides. While the task of distinguishing a peptide-binding TCR from a naive TCR unlikely to bind any peptide can be performed using sequence motifs, distinguishing between TCRs binding different peptides requires more advanced methods. Such a prediction is the key for using TCR repertoires as disease-specific biomarkers. We here used large scale TCR-peptide dictionaries with state-of-the-art natural language processing (NLP) methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific classifier to predict which TCR binds to which…