385 research outputs found
Development of artificial neural network-based object detection algorithms for low-cost hardware devices
Finally, the fourth work was published in the “WCCI” conference in 2020 and consisted of an individuals' position estimation algorithm based on a novel neural network model for environments with forbidden regions, named “Forbidden Regions Growing Neural Gas”.The human brain is the most complex, powerful and versatile learning machine ever known. Consequently, many scientists of various disciplines are fascinated by its structures and information processing methods. Due to the quality and quantity of the information extracted from the sense of sight, image is one of the main information channels used by humans. However, the massive amount of video footage generated nowadays makes it difficult to process those data fast enough manually. Thus, computer vision systems represent a fundamental tool in the extraction of information from digital images, as well as a major challenge for scientists and engineers.
This thesis' primary objective is automatic foreground object detection and classification through digital image analysis, using artificial neural network-based techniques, specifically designed and optimised to be deployed in low-cost hardware devices. This objective will be complemented by developing individuals' movement estimation methods by using unsupervised learning and artificial neural network-based models.
The cited objectives have been addressed through a research work illustrated in four publications supporting this thesis. The first one was published in the “ICAE” journal in 2018 and consists of a neural network-based movement detection system for Pan-Tilt-Zoom (PTZ) cameras deployed in a Raspberry Pi board. The second one was published in the “WCCI” conference in 2018 and consists of a deep learning-based automatic video surveillance system for PTZ cameras deployed in low-cost hardware. The third one was published in the “ICAE” journal in 2020 and consists of an anomalous foreground object detection and classification system for panoramic cameras, based on deep learning and supported by low-cost hardware
Autonomous Wireless Radar Sensor Mote for Target Material Classification
An autonomous wireless sensor network consisting of different types of sensor modalities is a topic of intense research due to its versatility and portability.These types of autonomous sensor networks commonly include passive sensor nodes such as infrared,acoustic,seismic and magnetic.However,fusion of another active sensor such as Doppler radar in the integrated sensor network may offer powerful capabilities for many different sensing and classification tasks.In this work,we demonstrate the design and implementation of an autonomous wireless sensor network integrating a Doppler sensor into wireless sensor node with commercial off the shelf components.We also investigate the effect of different types of target materials on return radar signal as one of the applications of the newly designed radar-mote network.Usually type of materials can affect the amount of energy reflected back to the source of an electromagnetic wave.We obtain mathematical and simulation models for the reflectivity of different homogeneous non-conducting materials and study the effect of such reflectivity on different types of targets.We validate our simulation results on effect of reflectivity on different types of targets using real toy experiment data collected through our autonomous radar-mote sensor network
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Classification and Geometry of General Perceptual Manifolds
Perceptual manifolds arise when a neural population responds to an ensemble
of sensory signals associated with different physical features (e.g.,
orientation, pose, scale, location, and intensity) of the same perceptual
object. Object recognition and discrimination requires classifying the
manifolds in a manner that is insensitive to variability within a manifold. How
neuronal systems give rise to invariant object classification and recognition
is a fundamental problem in brain theory as well as in machine learning. Here
we study the ability of a readout network to classify objects from their
perceptual manifold representations. We develop a statistical mechanical theory
for the linear classification of manifolds with arbitrary geometry revealing a
remarkable relation to the mathematics of conic decomposition. Novel
geometrical measures of manifold radius and manifold dimension are introduced
which can explain the classification capacity for manifolds of various
geometries. The general theory is demonstrated on a number of representative
manifolds, including L2 ellipsoids prototypical of strictly convex manifolds,
L1 balls representing polytopes consisting of finite sample points, and
orientation manifolds which arise from neurons tuned to respond to a continuous
angle variable, such as object orientation. The effects of label sparsity on
the classification capacity of manifolds are elucidated, revealing a scaling
relation between label sparsity and manifold radius. Theoretical predictions
are corroborated by numerical simulations using recently developed algorithms
to compute maximum margin solutions for manifold dichotomies. Our theory and
its extensions provide a powerful and rich framework for applying statistical
mechanics of linear classification to data arising from neuronal responses to
object stimuli, as well as to artificial deep networks trained for object
recognition tasks.Comment: 24 pages, 12 figures, Supplementary Material
Coping with Data Scarcity in Deep Learning and Applications for Social Good
The recent years are experiencing an extremely fast evolution of the Computer Vision and
Machine Learning fields: several application domains benefit from the newly developed
technologies and industries are investing a growing amount of money in Artificial Intelligence.
Convolutional Neural Networks and Deep Learning substantially contributed to the rise and
the diffusion of AI-based solutions, creating the potential for many disruptive new businesses.
The effectiveness of Deep Learning models is grounded by the availability of a huge
amount of training data. Unfortunately, data collection and labeling is an extremely expensive
task in terms of both time and costs; moreover, it frequently requires the collaboration of
domain experts.
In the first part of the thesis, I will investigate some methods for reducing the cost
of data acquisition for Deep Learning applications in the relatively constrained industrial
scenarios related to visual inspection. I will primarily assess the effectiveness of Deep Neural
Networks in comparison with several classical Machine Learning algorithms requiring a
smaller amount of data to be trained. Hereafter, I will introduce a hardware-based data
augmentation approach, which leads to a considerable performance boost taking advantage of
a novel illumination setup designed for this purpose. Finally, I will investigate the situation in
which acquiring a sufficient number of training samples is not possible, in particular the most
extreme situation: zero-shot learning (ZSL), which is the problem of multi-class classification
when no training data is available for some of the classes. Visual features designed for image
classification and trained offline have been shown to be useful for ZSL to generalize towards
classes not seen during training. Nevertheless, I will show that recognition performances
on unseen classes can be sharply improved by learning ad hoc semantic embedding (the
pre-defined list of present and absent attributes that represent a class) and visual features, to
increase the correlation between the two geometrical spaces and ease the metric learning
process for ZSL.
In the second part of the thesis, I will present some successful applications of state-of-the-
art Computer Vision, Data Analysis and Artificial Intelligence methods. I will illustrate
some solutions developed during the 2020 Coronavirus Pandemic for controlling the disease
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evolution and for reducing virus spreading. I will describe the first publicly available
dataset for the analysis of face-touching behavior that we annotated and distributed, and
I will illustrate an extensive evaluation of several computer vision methods applied to the
produced dataset. Moreover, I will describe the privacy-preserving solution we developed
for estimating the \u201cSocial Distance\u201d and its violations, given a single uncalibrated image
in unconstrained scenarios. I will conclude the thesis with a Computer Vision solution
developed in collaboration with the Egyptian Museum of Turin for digitally unwrapping
mummies analyzing their CT scan, to support the archaeologists during mummy analysis
and avoiding the devastating and irreversible process of physically unwrapping the bandages
for removing amulets and jewels from the body
Data Mining
Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment
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