Search results for "computer vision"
showing 10 items of 2353 documents
La logica visiva dei data
2020
I deal with visuals, and I would like to show how, starting out from the visual aspect as a key element of Western society, we may attempt to understand how digitality (both in terms of technology and thought) has brought about revolutionary change in our society, altering cultural, social, and economic paradigms... I would also like to show how the archive, and all that may be understood as such, has become the cultural logic of our time, in the form of the database.
Soft Topographic Map for Clustering and Classification of Bacteria
2007
In this work a new method for clustering and building a topographic representation of a bacteria taxonomy is presented. The method is based on the analysis of stable parts of the genome, the so-called “housekeeping genes”. The proposed method generates topographic maps of the bacteria taxonomy, where relations among different type strains can be visually inspected and verified. Two well known DNA alignement algorithms are applied to the genomic sequences. Topographic maps are optimized to represent the similarity among the sequences according to their evolutionary distances. The experimental analysis is carried out on 147 type strains of the Gammaprotebacteria class by means of the 16S rRNA…
Automatic differentiation of melanoma from dysplastic nevi.
2015
International audience; Malignant melanoma causes the majority of deaths related to skin cancer. Nevertheless, it is the most treatable one, depending on its early diagnosis. The early prognosis is a challenging task for both clinicians and dermatologist, due to the characteristic similarities of melanoma with other skin lesions such as dysplastic nevi. In the past decades, several computerized lesion analysis algorithms have been proposed by the research community for detection of melanoma. These algorithms mostly focus on differentiating melanoma from benign lesions and few have considered the case of melanoma against dysplastic nevi. In this paper, we consider the most challenging task a…
A Lightweight Software Architecture for Robot Navigation and Visual Logging through Environmental Landmarks Recognition
2006
A robot architecture with real-time performance in navigation tasks is presented. The system architecture is multi-threaded with shared memory and fast message passing through static signalling. In this paper, we focused on the reactive layer components and its straightforward implementation. The proposed architecture is described with reference to an experimental setup, in which the robot task is visual logging of environmental landmarks detected on the basis of sensor readings. Our experimental results show how the robot is able to identify, make snapshots and log a set of landmarks by matching 2D geometric patterns.
Pd/Au/SiC Nanostructured Diodes for Nanoelectronics: Room Temperature Electrical Properties
2010
Pd/Au/SiC nanostructured Schottky diodes were fabricated embedding Au nanoparticles (NPs) at the metalsemiconductor interface of macroscopic Pd/SiC contacts. The Au NPs mean size was varied controlling the temperature and time of opportune annealing processes. The electrical characteristics of the nanostructured diodes were studied as a function of the NPs mean size. In particular, using the standard theory of thermoionic emission, we obtained the effective Schottky barrier height (SBH) and the effective ideality factor observing their dependence on the annealing time and temperature being the signature of their dependence on the mean NP size. Furthermore, plotting the effective SBH as a fu…
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval
2016
Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However, they face difficulties in coping with large training data sets. With the increasing amount of optical remote sensing data made available for analysis and the possibility of using a large amount of simulated data from radiative transfer models (RTMs) to train kernel MLRAs, efficient data reduction techniques will need to be implemented. Active learning (AL) methods enable to select the most informative samples in a data set. This letter introduces six AL methods for achieving optimized biophysical variable estimat…
Toward a Collective Agenda on AI for Earth Science Data Analysis
2021
In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer. Thanks to both the massive availability of observational data, improved simulations, and algorithmic advances, these disciplines have found common objectives and challenges to advance the modeling and understanding of the Earth system. Despite such great opportunities, we also observed a worrying tendency to remain in disciplinary comfort zones applying recent advances from artificial intelligence on well resolved remote sensing problems. Here we take a position on research directions where we think the interface between these fields will have the most impact and be…
Multi-temporal and Multi-source Remote Sensing Image Classification by Nonlinear Relative Normalization
2016
Remote sensing image classification exploiting multiple sensors is a very challenging problem: data from different modalities are affected by spectral distortions and mis-alignments of all kinds, and this hampers re-using models built for one image to be used successfully in other scenes. In order to adapt and transfer models across image acquisitions, one must be able to cope with datasets that are not co-registered, acquired under different illumination and atmospheric conditions, by different sensors, and with scarce ground references. Traditionally, methods based on histogram matching have been used. However, they fail when densities have very different shapes or when there is no corres…
Improving the performance of acousto-optic tunable filters in imaging applications
2010
Acousto-optic tunable filters (AOTFs) can be used as spectral filters for the implementation of multispectral imaging systems. However, obtaining quality images is challenging. In this work, we propose several improvements that enable the use of these systems in quantitative spectroscopic imaging applications. The improvements are based on three pillars: 1. a finer spectral bandpass shaping by dynamically optimizing the radio frequency (rf) driving signal, 2. an extensive calibration process, and 3. careful image preprocessing that uses calibration data to correct some well known AOTF issues in imaging applications. A novel multispectral imaging instrument is built using commercial off-the-…
Adaptive motion estimation and video vector quantization based on spatiotemporal non-linearities of human perception
1997
The two main tasks of a video coding system are motion estimation and vector quantization of the signal. In this work a new splitting criterion to control the adaptive decomposition for the non-uniform optical flow estimation is exposed. Also, a novel bit allocation procedure is proposed for the quantization of the DCT transform of the video signal. These new approaches are founded on a perception model that reproduce the relative importance given by the human visual system to any location in the spatial frequency, temporal frequency and amplitude domain of the DCT transform. The experiments show that the proposed procedures behave better than their equivalent (fixed-block-size motion estim…