Automatic Identification of Relevant Colors in Non-Destructive Quality Evaluation of Fresh Salad Vegetables

Quality loss during storage is often associated to changes in relevant product colors and/or to the appearance of new pigments. Computer Vision System (CVS) for non-destructive quality evaluation often relies on human knowledge provided by operators to identify these relevant colors and their features. The approach described in this paper automatically identifies the most significant colors in unevenly colored products to evaluate their quality level. Its performance was compared with results obtained by exploiting human training. The new method improved quality evaluation and reduced the subjectivity and the inconsistency potentially induced by operators

The new Systematic Coronary Risk Evaluation (SCORE2 and SCORE2-OP) estimates the risk of arterial occlusive events in chronic myeloid leukemia patients treated with nilotinib or ponatinib

Patients with chronic myeloid leukemia (CML) treated with nilotinib or ponatinib may experience arterial occlusive events (AOEs). It is currently recommended to thoroughly assess cardiovascular risk factors before treating CML. We identified 455 consecutive CML adult patients, 335 treated with nilotinib and 120 with ponatinib; 380 patients without previous cardiovascular diseases or diabetes were stratified according to the Systematic Coronary Risk Evaluation (SCORE2) and SCORE2-Older Persons (SCORE2-OP). This updated algorithm from the European Society of Cardiology (ESC) estimates a 10-year risk of fatal and non-fatal cardiovascular diseases. It is based on sex, age, smoking habits, systolic blood pressure, non-high-density lipoprotein cholesterol, and European geographical region of cardiovascular risk. The SCORE2/SCORE2-OP algorithm translated more patients (50.2%) to the high-very high cardiovascular risk category than the previous SCORE (25.3%). Patients with a high to very high SCORE2/SCORE2-OP risk showed a significantly higher incidence rate of AOEs (69.2% vs. 46.5%, p < 0.001). The older SCORE was less specific in estimating AOEs in patients classified as low-intermediate risk (69.8 vs. 54.2%). In multivariate analysis, no associations were found between AOEs and gender, age, and type or dose of tyrosine kinase inhibitor. Only the SCORE2/SCORE2-OP risk was confirmed as a significant predictive factor (p = 0.028; hazard ratio = 2.2; 95% confidence interval = 1.1-4.5). Patients with AOEs required, in most cases, imaging diagnostic tests, additional drugs, and sometimes invasive procedures, increasing access to visits and hospital management. This real-life study suggested that the SCORE2 and SCORE2-OP charts could help identify cardiovascular fragility in CML patients providing them with more attention and a proper TKI selection

Low low-density lipoprotein (LDL), cholesterol and triglycerides plasma levels are associated with reduced risk of arterial occlusive events in chronic myeloid leukemia patients treated with ponatinib in the real-life. A Campus CML study

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Analysis of Early Events during the First Year of Tyrosine Kinase Inhibitor Therapy in Patients with Chronic Phase - Chronic Myeloid Leukemia: A "Campus CML" Study

Tyrosine kinase inhibitors (TKIs) revolutionized treatment of chronic myeloid leukemia (CML). However, the rst months of therapy are crucial, as optimal response is dened as the achievement of molecular milestones at 3, 6 and 12 months (mo.) and as many toxicities, also causing a TKI switch, are more frequent in the 1st yea

Semantic Segmentation of Packaged and Unpackaged Fresh-Cut Apples Using Deep Learning

Computer vision systems are often used in industrial quality control to offer fast, objective, non-destructive, and contactless evaluation of fruit. The senescence of fresh-cut apples is strongly related to the browning of the pulp rather than to the properties of the peel. This work addresses the identification and selection of pulp inside images of fresh-cut apples, both packaged and unpackaged; this is a critical step towards a computer vision system that is able to evaluate their quality and internal properties. A DeepLabV3+-based convolutional neural network model (CNN) has been developed for this semantic segmentation task. It has proved to be robust with respect to the similarity of colours between the peel and pulp. Its ability to separate the pulp from the peel and background has been verified on four varieties of apples: Granny Smith (greenish peel), Golden (yellowish peel), Fuji, and Pink Lady (reddish peel). The semantic segmentation achieved an accuracy greater than 99% on all these varieties. The developed approach was able to isolate regions significantly affected by the browning process on both packaged and unpackaged pieces: on these areas, the colour analysis will be studied to evaluate internal quality and senescence of packaged and unpackaged products

A.: Optimization of the POSIT algorithm for indoor autonomous navigation

Abstract This paper presents an optimization of POSIT (Pose from Orthographic and Scaling with Iterations), a model-based camera location algorithm, in the domain of indoor mobile robot navigation. The method finds the rotation matrix and the translation vector of the camera with respect to an object. The novelty of the proposed modified algorithm is that it does not need the perfect knowledge of camera parameters. A new definition of the scaling factor has been introduced in the scaled orthographic projection. Due to the peculiarity of the indoor bounded workspace a new formulation of the translation vector has been used. The new method has been successfully applied in a real environment considering a goal-directed navigation task for our real robot Khepera. The experimentation has shown better results in comparison with the original POSIT algorithm

αρογη ´- A System for the Virtual Aided Recomposition of Fragmented Frescos

Abstract. The paper describes a digital system for the virtual aided recomposition of fragmented frescos, an innovative approach that is being developed and proved on the St. Matthew’s fresco, painted by Cimabue in the Upper Church of St. Francis in Assisi which had an extension of 35 squared meters and broke into more than 140.000 pieces during the earthquake in 1997. The base level of the designed system transposes the traditional recomposition process in a digital way: the laboratory of fragments in Assisi, used for the traditional recomposition, becomes a client-server architecture that can be geographically distributed. Several operators can jointly work on the same fresco from different clients connected over Internet to a server, situated in our Institute in Bari, where a database has been designed and realized to contain the digital image of each single fragment. Several further features have been added to improve the efficiency and efficacy of the recomposition process. The database is indexed using a query-by-example approach applied to suitable sample images (fragments and/or details extracted from the reference image of the whole fresco). Colour and texture has been used as features to describe the pictorial content of fragments. Since the only available picture of the whole fresco has been acquired in unknown conditions and exhibits colours significantly different from those of fragments, it has been necessary to estimate and apply suitable and specific colour transformations to different regions of the reference image to make meaningful their comparison. The restorers of the Central Institute for Restoration, which cooperates in this project, are very interested in this strongly innovative approach. They are working on the St. Matthew’s fresco using the system and have pointed out several advantages that the system brings into their work as long as many new features that can enrich its possibilities. 1