Topical Collection "Computer Vision and Machine Learning: Theory, Methods and Applications"
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Associate Professor, Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A 43124 Parma, Italy
video surveillance; mobile vision; visual sensor networks; machine vision; multimedia and video processing; performance analysis of multimedia computer architectures
Special Issues, Collections and Topics in MDPI journals
Electrical Engineering, Fu Jen Catholic University, New Taipei 24205, Taiwan
intelligent video surveillance; face recognition; deep learning for object detection; robotic vision; embedded computer vision; sleep healthcare; neuromorphic computing
Special Issues, Collections and Topics in MDPI journals
Topical Collection Information
Computer vision (CV) and machine learning (ML) now represent two of the most addressed topics in artificial intelligence and computer science. The field of computer vision has witnessed an incredible shift in the last decade with the advent of deep learning, by means of which new applications have emerged and new milestones have been made reachable. Deep learning represents the most noticeable point of connection between CV and ML, but there is still much to be discovered in all these fields.
This topical collection will gather papers proposing advances in theory and models in CV and ML, paving the way to new applications.
Hence, we invite the academic community and relevant industrial partners to submit papers to this collection, on relevant fields and topics including (but not limited to) the following:
- New algorithms and methods of classical computer vision.
- Architecture and applications of convolutional neural networks.
- Transformers applied to computer vision.
- Geometric deep learning and graph convolution networks.
- Generative adversarial networks (GAN).
- Generative models beyond GANs.
- Fairness, privacy, and explainability in deep learning.
- Continual, online, developmental, and federated learning.
- Zero- and few-shot learning.
- Brand new applications of computer vision and machine learning.
Prof. Dr. Andrea Prati
Prof. Dr. Yuan-Kai Wang
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 collection 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.
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- deep learning
- generative models
- computer vision
- pattern recognition
- explainable AI
Published Papers (3 papers)
A Benchmark for the Evaluation of Corner Detectors
Viewed by 489
Corners are an important kind of image feature and play a crucial role in solving various tasks. Over the past few decades, a great number of corner detectors have been proposed. However, there is no benchmark dataset with labeled ground-truth corners and unified
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Corners are an important kind of image feature and play a crucial role in solving various tasks. Over the past few decades, a great number of corner detectors have been proposed. However, there is no benchmark dataset with labeled ground-truth corners and unified metrics to evaluate their corner detection performance. In this paper, we build three benchmark datasets for corner detection. The first two consist of those binary and gray-value images that have been commonly used in previous corner detection studies. The third one contains a set of urban images, called the Urban-Corner dataset. For each test image in these three datasets, the ground-truth corners are manually labeled as objectively as possible with the assistance of a line segment detector. Then, a set of benchmark evaluation metrics is suggested, including five conventional ones: the precision, the recall, the arithmetic mean of precision and recall (APR), the F score, the localization error (Le), and a new one proposed in this work called the repeatability referenced to ground truth
(RGT). Finally, a comprehensive evaluation of current state-of-the-art corner detectors is conducted.
Automated Design of Salient Object Detection Algorithms with Brain Programming
Cited by 1
| Viewed by 789
Despite recent improvements in computer vision, artificial visual systems’ design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still open due to the difficulty of understanding the brain’s inner workings. Progress
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Despite recent improvements in computer vision, artificial visual systems’ design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still open due to the difficulty of understanding the brain’s inner workings. Progress in this research area follows the traditional path of hand-made designs using neuroscience knowledge or, more recently, deep learning, a particular branch of machine learning. Recently, a different approach based on genetic programming appeared to enhance handcrafted techniques following two different strategies. The first method follows the idea of combining previous hand-made methods through genetic programming and fuzzy logic. The second approach improves the inner computational structures of basic hand-made models through artificial evolution. This research proposes expanding the artificial dorsal stream using a recent proposal based on symbolic learning to solve salient object detection problems following the second technique. This approach applies the fusion of visual saliency and image segmentation algorithms as a template. The proposed methodology discovers several critical structures in the template through artificial evolution. We present results on a benchmark designed by experts with outstanding results in an extensive comparison with the state of the art, including classical methods and deep learning approaches to highlight the importance of symbolic learning in visual saliency.
Scene Text Detection Using Attention with Depthwise Separable Convolutions
Cited by 5
| Viewed by 704
In spite of significant research efforts, the existing scene text detection methods fall short of the challenges and requirements posed in real-life applications. In natural scenes, text segments exhibit a wide range of shape complexities, scale, and font property variations, and they appear
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In spite of significant research efforts, the existing scene text detection methods fall short of the challenges and requirements posed in real-life applications. In natural scenes, text segments exhibit a wide range of shape complexities, scale, and font property variations, and they appear mostly incidental. Furthermore, the computational requirement of the detector is an important factor for real-time operation. To address the aforementioned issues, the paper presents a novel scene text detector using a deep convolutional network which efficiently detects arbitrary oriented and complex-shaped text segments from natural scenes and predicts quadrilateral bounding boxes around text segments. The proposed network is designed in a U-shape architecture with the careful incorporation of skip connections to capture complex text attributes at multiple scales. For addressing the computational requirement of the input processing, the proposed scene text detector uses the MobileNet model as the backbone that is designed on depthwise separable convolutions. The network design is integrated with text attention blocks to enhance the learning ability of our detector, where the attention blocks are based on efficient channel attention. The network is trained in a multi-objective formulation supported by a novel text-aware non-maximal procedure to generate final text bounding box predictions. On extensive evaluations on ICDAR2013
, and COCOText
datasets, the paper reports detection F-scores of 0.910, 0.879, 0.830, and 0.617, respectively.