On the efficacy of handcrafted and deep features for seed image classification

Computer vision techniques have become important in agriculture and plant sciences due to their wide variety of applications. In particular, the analysis of seeds can provide meaningful information on their evolution, the history of agriculture, the domestication of plants, and knowledge of diets in ancient times. This work aims to propose an exhaustive comparison of several different types of features in the context of multiclass seed classification, leveraging two public plant seeds data sets to classify their families or species. In detail, we studied possible optimisations of five traditional machine learning classifiers trained with seven different categories of handcrafted features. We also fine-tuned several well-known convolutional neural networks (CNNs) and the recently proposed SeedNet to determine whether and to what extent using their deep features may be advantageous over handcrafted features. The experimental results demonstrated that CNN features are appropriate to the task and representative of the multiclass scenario. In particular, SeedNet achieved a mean F-measure of 96%, at least. Nevertheless, several cases showed satisfactory performance from the handcrafted features to be considered a valid alternative. In detail, we found that the Ensemble strategy combined with all the handcrafted features can achieve 90.93% of mean F-measure, at least, with a considerably lower amount of times. We consider the obtained results an excellent preliminary step towards realising an automatic seeds recognition and classification framework

Using Artificial Intelligence for COVID-19 Detection in Blood Exams: A Comparative Analysis

COVID-19 is an infectious disease that was declared a pandemic by the World Health Organization (WHO) in early March 2020. Since its early development, it has challenged health systems around the world. Although more than 12 billion vaccines have been administered, at the time of writing, it has more than 623 million confirmed cases and more than 6 million deaths reported to the WHO. These numbers continue to grow, soliciting further research efforts to reduce the impacts of such a pandemic. In particular, artificial intelligence techniques have shown great potential in supporting the early diagnosis, detection, and monitoring of COVID-19 infections from disparate data sources. In this work, we aim to make a contribution to this field by analyzing a high-dimensional dataset containing blood sample data from over forty thousand individuals recognized as infected or not with COVID-19. Encompassing a wide range of methods, including traditional machine learning algorithms, dimensionality reduction techniques, and deep learning strategies, our analysis investigates the performance of different classification models, showing that accurate detection of blood infections can be obtained. In particular, an F-score of 84% was achieved by the artificial neural network model we designed for this task, with a rate of 87% correct predictions on the positive class. Furthermore, our study shows that the dimensionality of the original data, i.e. the number of features involved, can be significantly reduced to gain efficiency without compromising the final prediction performance. These results pave the way for further research in this field, confirming that artificial intelligence techniques may play an important role in supporting medical decision-making

Generation of synthetic wide-band electromagnetic time series

The estimation of the earth transfer functions in MT prospecting method poses the greatest difficulty. As in the seismic prospecting method this task requires the development of advanced processing techniques. In order to assess the performance of each technique, controlled synthetic data and different noise types, which simulate the observed signals, are required. This paper presents a procedure to generate a wide-band noise-free electromagnetic field to be used both for magnetotelluric and audio-magnetotelluric studies. Furthermore, an effort was made to extend the simulation procedures to generally stratified and simple inhomogeneous earth structures. The discrete-time magnetic field values are generated through the inverse Fourier transform of a continuous amplitude spectrum and a sampling procedure. The electric field time series are obtained by the convolution of the magnetic field time series, calculated in the interested frequency band, with a non-causal impedance impulse response. Polarized fields, which are important when inhomogeneous media are considered, are also generated

Generation of synthetic wide-band electromagnetic time series

The estimation of the earth transfer functions in MT prospecting method poses the greatest difficulty. As in the seismic prospecting method this task requires the development of advanced processing techniques. In order to assess the performance of each technique, controlled synthetic data and different noise types, which simulate the observed signals, are required. This paper presents a procedure to generate a wide-band noise-free electromagnetic field to be used both for magnetotelluric and audio-magnetotelluric studies. Furthermore, an effort was made to extend the simulation procedures to generally stratified and simple inhomogeneous earth structures. The discrete-time magnetic field values are generated through the inverse Fourier transform of a continuous amplitude spectrum and a sampling procedure. The electric field time series are obtained by the convolution of the magnetic field time series, calculated in the interested frequency band, with a non-causal impedance impulse response. Polarized fields, which are important when inhomogeneous media are considered, are also generated

A Shallow Learning Investigation for COVID-19 Classification

COVID-19, an infectious coronavirus disease, triggered a pandemic that resulted in countless deaths. Since its inception, clinical institutions have used computed tomography as a supplemental screening method to reverse transcription-polymerase chain reaction. Deep learning approaches have shown promising results in addressing the problem; however, less computationally expensive techniques, such as those based on handcrafted descriptors and shallow classifiers, may be equally capable of detecting COVID-19 based on medical images of patients. This work proposes an initial investigation of several handcrafted descriptors well known in the computer vision literature already been exploited for similar tasks. The goal is to discriminate tomographic images belonging to three classes, COVID-19, pneumonia, and normal conditions, and present in a large public dataset. The results show that kNN and ensembles trained with texture descriptors achieve outstanding accuracy in this task, reaching accuracy and F-measure of 93.05% and 89.63%, respectively. Although it did not exceed state of the art, it achieved satisfactory performance with only 36 features, enabling the potential to achieve remarkable improvements from a computational complexity perspective

On The Potential of Image Moments for Medical Diagnosis

Medical imaging is widely used for diagnosis and postoperative or post-therapy monitoring. The ever-increasing number of images produced has encouraged the introduction of automated methods to assist doctors or pathologists. In recent years, especially after the advent of convolutional neural networks, many researchers have focused on this approach, considering it to be the only method for diagnosis since it can perform a direct classification of images. However, many diagnostic systems still rely on handcrafted features to improve interpretability and limit resource consumption. In this work, we focused our efforts on orthogonal moments, first by providing an overview and taxonomy of their macrocategories and then by analysing their classification performance on very different medical tasks represented by four public benchmark data sets. The results confirmed that convolutional neural networks achieved excellent performance on all tasks. Despite being composed of much fewer features than those extracted by the networks, orthogonal moments proved to be competitive with them, showing comparable and, in some cases, better performance. In addition, Cartesian and harmonic categories provided a very low standard deviation, proving their robustness in medical diagnostic tasks. We strongly believe that the integration of the studied orthogonal moments can lead to more robust and reliable diagnostic systems, considering the performance obtained and the low variation of the results. Finally, since they have been shown to be effective on both magnetic resonance and computed tomography images, they can be easily extended to other imaging techniques

Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence

Ripening is a very important process that contributes to cheese quality, as its characteristics are determined by the biochemical changes that occur during this period. Therefore, monitoring ripening time is a fundamental task to market a quality product in a timely manner. However, it is difficult to accurately determine the degree of cheese ripeness. Although some scientific methods have also been proposed in the literature, the conventional methods adopted in dairy industries are typically based on visual and weight control. This study proposes a novel approach aimed at automatically monitoring the cheese ripening based on the analysis of cheese images acquired by a photo camera. Both computer vision and machine learning techniques have been used to deal with this task. The study is based on a dataset of 195 images (specifically collected from an Italian dairy industry), which represent Pecorino cheese forms at four degrees of ripeness. All stages but the one labeled as 'day 18', which has 45 images, consist of 50 images. These images have been handled with image processing techniques and then classified according to the degree of ripening, i.e., 18, 22, 24, and 30 days. A 5-fold cross-validation strategy was used to empirically evaluate the performance of the models. During this phase, each training fold was augmented online. This strategy allowed to use 624 images for training, leaving 39 original images per fold for testing. Experimental results have demonstrated the validity of the approach, showing good performance for most of the trained models

SOLUZIONE DINAMICA DELLA RETE ASSOGEO E INQUADRAMENTO EUREF-RDN

L’importante sviluppo avuto recentemente dai servizi GPS di posizionamento differenziale in tempo reale (RTK e VRS), ha permesso di diminuire in modo considerevole i tempi e quindi i costi per un rilievo topografico, questo senza incidere in modo significativo sulla precisione delle misure eseguite [Pesci et al., 2008]. L’infrastruttura che permette la realizzazione di questi servizi è costituita da una rete GPS di stazioni permanenti (SP) distribuite più o meno regolarmente su di una griglia la cui maglia, cioè la distanza tra le stazioni, è di qualche decina di chilometri. Assogeo S.p.a. a partire dal 2006 ha sviluppato nell’Italia centro-settentrionale una rete di stazioni permanenti (Fig. 1a) in grado di supportare i diversi servizi di posizionamento in tempo reale.Attualmente questa rete è costituita da 32 stazioni equipaggiate con ricevitore ed antenne geodetiche a doppia frequenza (Tab. 1) in continua espansione. Un singolo operatore, anche se equipaggiato di un solo ricevitore GPS, ha la possibilità di connettersi al centro operativo Assogeo ed eseguire il proprio rilievo topografico, in tempo reale. Il sistema di riferimento in cui vengono forniti i risultati del rilievo è quello su cui vengono calcolate le posizioni delle diverse SP, per questo motivo è importante che tale sistema di riferimento sia compatibile con quello utilizzato in ambito cartografico e topografico definito dall’Istituto Geografico Militare Italiano (IGMI). Inoltre, è necessario che la stima della posizione delle diverse SP sia la più precisa possibile e che quindi venga calcolata utilizzando tutte le informazioni acquisite da tali stazioni. A questo proposito IGMI ha istituito la Rete Dinamica Nazionale [RDN, Baroni et al. 2009] formata da 99 SP già operanti sul territorio e omogeneamente distribuite, offrendo un riferimento per allineare le diverse reti GPS per il servizio di posizionamento in tempo reale sorte sul territorio italiano. La rete RDN è a sua volta allineata ad un sistema di riferimento convenzionale ufficializzato in Europa, cioè il sistema ETRF2000 [Bruyninx et al., 2009], e riferito all’epoca 2005.0, secondo le più recenti direttive EUREF [Bruyninx, 2004, Kenyeres and Bruyninx, 2004]. Per uniformare le soluzioni allo stesso sistema di riferimento utilizzato dall’IGMI, è necessario quindi che anche le posizioni delle SP della rete Assogeo siano calcolate in ETRF2000 all’epoca 2005.0 e poiriportate al 2008.0. In questo lavoro vengono proposte e confrontate due strategie di calcolo per ottenere, in un modo semplice e intuitivo, il sistema di riferimento desiderato. Si tratta di procedure differenti che analizzano il medesimo insieme di osservazioni utilizzando lo stesso software, ma utilizzando strategie diverse, questo per verificare la reale precisione con cui può essere stimata la posizione di una stazione GPS permanente

Shallow to intermediate resistivity features of the Colfiorito Fault System inferred by DC and MT survey

Over the last decade electromagnetic (EM) measurements have provided new constraints on the upper-crustal structure of the major fault zones in the world, both when they act as conduit and as a barrier, due to strong sensitivity of resistivity to fluids circulation and mineralization. On the track of a high impact magnetotelluric (MT) study performed across the San Andreas Fault, high resolution EM data were collected in the Colfiorito epicentral area along profiles crossing some main fault lineaments. Being the study focussed both on shallow that on intermediate resistivity distribution in the brittle upper-crust, a MT profile was integrated by several electrical resistivity tomographies (ERT). The latter were successful in locating faults even where the structures are buried by a wide covering of Quaternary deposits and in the recognition of different electrical signatures of the faults. MT resistivity model crossing Mt. Prefoglio normal fault clearly imaged the typical thrust structures of the area and a high conductive zone spatially related to the fault. Seismicity seems to be located outside such conductive area, whose behaviour suggests a fluidised and altered zone incapable of supporting significant stress internally