Search results for "artificial intelligence"
showing 10 items of 6122 documents
<title>Restoration of a short-exposure image sequence degraded by atmospheric turbulence</title>
2000
This paper deals with the restoration of the shape of an object observed with a high-resolution infrared imaging device, through atmospheric turbulence. The propagation path is quite long (a few tenth kilometer) and the image is thus disturbed. A sequence of short-exposure images of the interesting object is recorded. We can see that the object shape fluctuates randomly during the sequence, but that its edges remain sharp, thanks to the very short exposure time. A bayesian analysis of the Fourier descriptors associated to the edges shows that the optimal shape is the one corresponding to the mean Fourier descriptors. We thus propose two ways to estimate this shape. The first one consists in…
A Mushroom Bodies inspired spiking network for classification and sequence learning
2015
Sequence learning is a complex capability shown by living beings, able to extract information from the environment. Looking into the insect world, there are several examples where the presentation time of specific stimuli is considered to select the proper behavioural response. On the basis of previously developed neural models for sequence learning, inspired by the Drosophila melanogaster, a new formalization of key brain structures involved in the process is here provided. The input classification is performed through resonant neurons, stimulated by the complex dynamics generated in a lattice of recurrent spiking neurons modelling the Mushroom Bodies neuropile in the insect brain. The net…
Classification of Sequences with Deep Artificial Neural Networks: Representation and Architectural Issues
2021
DNA sequences are the basic data type that is processed to perform a generic study of biological data analysis. One key component of the biological analysis is represented by sequence classification, a methodology that is widely used to analyze sequential data of different nature. However, its application to DNA sequences requires a proper representation of such sequences, which is still an open research problem. Machine Learning (ML) methodologies have given a fundamental contribution to the solution of the problem. Among them, recently, also Deep Neural Network (DNN) models have shown strongly encouraging results. In this chapter, we deal with specific classification problems related to t…
Cloud motion detection from infrared satellite images
2002
The estimation of cloud motion from a sequence of satellite images can be considered a challenging task due to the complexity of phenomena implied. Being a non-rigid motion and implying non-linear events, most motion models are not suitable and new algorithms have to be developed. We propose a novel technique, combining a Block Matching Algorithm (BMA) and a best candidate block search along with a vector median regularisation.
An Overview of the Application of Deep Learning in Short Read Sequence Classification
2020
AbstractAdvances in sequencing technology have led to an ever increasing amount of available short read sequencing data. This has, consequently, exacerbated the need for efficient and precise classification tools that can be used in the analysis of this data. As it stands, recent years have shown that massive leaps in performance can be achieved when it comes to approaches that are based in heuristics, and alongside these improvements there has been an ever increasing interest in applying deep learning techniques to revolutionize this classification task. We attempt to gather up these approaches and to evaluate their performance in a reproducible fashion to get a better perspective on the c…
Sequence Q-learning: A memory-based method towards solving POMDP
2015
Partially observable Markov decision process (POMDP) models a control problem, where states are only partially observable by an agent. The two main approaches to solve such tasks are these of value function and direct search in policy space. This paper introduces the Sequence Q-learning method which extends the well known Q-learning algorithm towards the ability to solve POMDPs through adding a special sequence management framework by advancing from action values to “sequence” values and including the “sequence continuity principle”.
Positionless aspect based sentiment analysis using attention mechanism.
2021
Abstract Aspect-based sentiment analysis (ABSA) aims at identifying fine-grained polarity of opinion associated with a given aspect word. Several existing articles demonstrated promising ABSA accuracy using positional embedding to show the relationship between an aspect word and its context. In most cases, the positional embedding depends on the distance between the aspect word and the remaining words in the context, known as the position index sequence. However, these techniques usually employ both complex preprocessing approaches with additional trainable positional embedding and complex architectures to obtain the state-of-the-art performance. In this paper, we simplify preprocessing by …
Display of travelling 3D scenes from single integral-imaging capture
2016
Integral imaging (InI) is a 3D auto-stereoscopic technique that captures and displays 3D images. We present a method for easily projecting the information recorded with this technique by transforming the integral image into a plenoptic image, as well as choosing, at will, the field of view (FOV) and the focused plane of the displayed plenoptic image. Furthermore, with this method we can generate a sequence of images that simulates a camera travelling through the scene from a single integral image. The application of this method permits to improve the quality of 3D display images and videos.
Computation and Display of 3D Movie From a Single Integral Photography
2016
Integral photography is an auto-stereoscopic technique that allows, among other interesting applications, the display of 3D images with full parallax and avoids the painful effects of the accommodation-convergence conflict. Currently, one of the main drawbacks of this technology is the need of a huge amount of data, which have to be stored and transmitted. This is due to the fact that behind every visual resolution unit, i.e. behind any microlens of an integral-photography monitor, between 100 and 300 pixels should appear. In this paper, we make use of an updated version of our algorithm, SPOC 2.0, to alleviate this situation. We propose the application of SPOC 2.0 for the calculation of co…
Extraction of ERP from EEG data
2007
In this article, a simple but novel technique for extracting a linear subspace related to event related potentials (ERPs) from ElectroEncephaloGraphy (EEG) data is introduced. The technique consists of a sequence of basic linear operations applied to multidimensional EEG data in a problem-specific manner. The derivation of the proposed technique is given and results with real data are described together with overall conclusions.