Computer Vision, Pattern Recognition and Machine Learning in Italy
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 30 April 2025 | Viewed by 37442
Special Issue Editors
Interests: computer vision; machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Most modern technological innovations are also made possible with the most recent advances in pattern recognition, machine learning, and computer vision.
The main aim of this Special Issue is to collect works from the fervent Italian research community.
Works should aim to report the main theoretical improvements in the aforementioned research areas and their impact on different application contexts, such as video surveillance and biometry, sports analysis, inspection, assistive and manufacturing technologies, smart agriculture, eHealth, environment monitoring, intelligent transportation and construction, retail, and so on.
Dr. Marco Leo
Dr. Sara Colantonio
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- computer vision
- pattern recognition
- machine learning
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A systematic review of effective factors on high-throughput plant phenotyping based on ML /DL algorithms
Authors: Firozeh Solimani; Vito Renò
Affiliation: --
Abstract: Recently, a new science has emerged, called plant phenotype, which deals with the study of the complex characteristics of plants with the aim of evaluating their condition. Undoubtedly, this goal can only be achieved by combining biological knowledge with computer science and engineering skills, especially when it comes to the huge amounts of data generated by high-throughput phenotyping (HTP) platforms. In this scenario, machine learning and deep learning algorithms, which have been successfully integrated with non-invasive imaging techniques, play a key role in the automation, standardization, and quantitative analysis of big data. In this context, we aim to conduct a systematic study on high-throughput plant phenotyping to identify the factors that are effective in evaluating the condition of plants ( aerial part of the plant and root system). In this study, we followed the PRISMA protocol and investigated the topic by proposing 4 influential factors (platforms, sensors, algorithms, and new techniques). The study covers the period from 1 January 2019 to the end of 2022. We used the Scopus database to find 1000 articles dealing with the subject of high throughput plant phenotyping, which we were able to filter using inclusion and exclusion criteria. Following a thorough review, we selected 34 articles dealing with issues of our goal. The results of our data show that about 65% of recent studies by researchers are aimed at analyzing phenotyping data, indicating the importance of managing a huge amount of data through phenotyping platforms. Meanwhile, deep learning has taken a larger share of research with 59%, which can indicate the better accuracy and speed of this algorithm. Future research should focus on improving deep-learning models for managing big data generated by platforms and also reduce the cost of plant phenotyping for farmers by developing customized or user-friendly models.
Title: MCRC - A Novel Dataset for Multi-Camera and Robot-World Hand-Eye Calibration
Authors: Davide Allegro, Matteo Terreran, Stefano Ghidoni.
Affiliation: --
Abstract: Multi-camera systems are an effective solution for dealing with large areas or complex scenarios with many occlusions. In such a setup, an accurate camera network calibration is crucial in order to localize scene elements with respect to a single reference frame shared by all the viewpoints of the network. Multi-camera calibration is a critical requirement also in several robotics scenarios, particularly those involving a robotic workcell equipped with a manipulator surrounded by multiple sensors. Within this scenario the robot-world hand-eye calibration is an additional crucial element to determine the exact position of each camera with respect to the robot, in order to provide information about the surrounding workspace directly to the manipulator. Despite the importance of the calibration process in the two scenarios outlined above, namely i) a camera network, and ii) a camera network with a robot, there is a lack of standard datasets available in the literature to evaluate and compare calibration methods. Moreover they are usually treated separately and tested on dedicated setups. In this paper we propose a generic standard dataset acquired in a robotic workcell where both methods can be evaluated according to two benchmarks: camera network calibration and robot-world hand-eye calibration. MCRC is a Multi-Camera Robot system Calibration dataset, consisting of over 10000 synthetic and real images of ChAruCo and checkerboard patterns, each one rigidly attached to the robot end-effector which was moved in front of four cameras surrounding the manipulator from different viewpoints during the image acquisition. The real dataset includes several multi-view image sets captured by three different types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455 and Lidar L515, to evaluate their advantages and disadvantages for calibration. Furthermore, in order to accurately analyze the effect of camera-robot distance on calibration, we acquired a comprehensive synthetic dataset, with related ground truth, with three different camera network setups corresponding to three levels of calibration difficulty depending on the cell size. An additional contribution of this work is to provide a comprehensive evaluation of state-of-the-art calibration methods using our dataset, highlighting their strengths and weaknesses, in order to outline the two aforementioned benchmarks.