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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 8465

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Special Issue Editors

Prof. Dr. Zaixing He

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The State Key Laboratory of Fluid Power & Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: robotic vision; deep learning; visual intelligence of manufacturing equipment

Prof. Dr. Mingqiang Wei

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Guest Editor

School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 213300, China
Interests: computer graphics; computer vision; deep Learning

Prof. Dr. Qiong Wang

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Guest Editor

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Interests: medical artificial intelligence; virtual reality and human interaction; surgical navigation and robot
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Meng Wu

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Guest Editor

School of Mathematics and Statistics, Nanjing University of Science & Technology, Nanjing 210094, China
Interests: isogeometric analysis; computationally aided geometry design

Special Issue Information

Dear Colleagues,

Recently, machine learning has become prevalent in all research directions (e.g., smart inspection, virtual/augmented reality, autonomous driving, and robotic). Researchers are seeking machine learning substitutes for all traditional methods, especially in the fields of computer graphics and image processing. However, machine learning applications should not simply train the same models in different cases; they should be smartly designed to cope with the differing nature of different cases. This situation is still far from being satisfactory in order to leverage the full potential of mathematical models, deep learning, and geometry-domain knowledge for the generation, processing, and understanding of different data modalities. In computer graphics and image processing, there are specific difficulties, for example, complex data structures such as triangular mesh, and off-grid operations such as scattered data interpolation preventing the ordinary neural network operations, e.g., convolution and pooling, from being directly used. In addition, large model sizes also are a big obstacle of real-time applications such as the rendering of large-scale scenes with high qualities. Novel applications of machine learning ideas to solve core problems in computer graphics and image processing are particularly welcome in this Special Issue.

Prospective authors are invited to submit original manuscripts on topics including, but not limited to:

Machine learning for off-grid image processing;
Machine learning for iso-geometric analysis;
Geometry-based machine learning models;
Machine learning on triangular meshes or tetrahedra meshes;
Machine learning on real-time graphics or image processing;
Machine learning for biomedical applications;
Graph convolutional network for graphics or image processing;
Deep learning for surface reconstruction and optimization;
Representation learning for 3D irregular data;
Multi-modal data fusion and processing;
Low-level vision tasks;
High-level vision tasks;
Transformer for graphics and image processing;
Reinforcement learning, continual learning, domain adaptation;
Realistic 2D/3D data generation and synthesis;
Computer animation, video games;
Image analysis and understanding;
Geometric and solid modeling and processing;
Deep geometry processing;
Deep learning for smart inspection and manufacturing.

Prof Dr. Zaixing He
Prof. Dr. Mingqiang Wei
Prof. Dr. Qiong Wang
Prof. Dr. Meng Wu
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

machine learning
computer graphics
geometry optimization
computer vision
3D vision
isogeometric analysis

Published Papers (7 papers)

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Research

22 pages, 2998 KiB

Open AccessArticle

Enhancing Arabic Sign Language Interpretation: Leveraging Convolutional Neural Networks and Transfer Learning

by Saad Al Ahmadi, Farah Muhammad and Haya Al Dawsari

Mathematics 2024, 12(6), 823; https://doi.org/10.3390/math12060823 - 11 Mar 2024

Viewed by 662

Abstract

In a world essentializing communication for human connection, the deaf community encounters distinct barriers. Sign language, their main communication method is rich in hand gestures but not widely understood outside their community, necessitating interpreters. The existing solutions for sign language recognition depend on extensive datasets for model training, risking overfitting with complex models. The scarcity of details on dataset sizes and model specifics in studies complicates the scalability and verification of these technologies. Furthermore, the omission of precise accuracy metrics in some research leaves the effectiveness of gesture recognition by these models in question. The key phases of this study are Data collection, Data preprocessing, Feature extraction using CNN and finally transfer learning-based classification. The purpose of utilizing CNN and transfer learning is to tap into pre-trained neural networks for optimizing performance on new, related tasks by reusing learned patterns, thus accelerating development and improving accuracy. Data preprocessing further involves resizing of images, normalization, standardization, color space conversion, augmentation and noise reduction. This phase is capable enough to prune the image dataset by improving the efficiency of the classifier. In the subsequent phase, feature extraction has been performed that includes the convolution layer, feature mapping, pooling layer and dropout layer to obtain refined features from the images. These refined features are used for classification using ResNet. Three different datasets are utilized for the assessment of proposed model. The ASL-DS-I Dataset includes a total of 5832 images of hand gestures whereas, ASL-DS-II contains 54,049 images and ASL-DS-III dataset includes 7857 images adopted from specified web links. The obtained results have been evaluated by using standard metrics including ROC curve, Precision, Recall and F-measure. Meticulous experimental analysis and comparison with three standard baseline methods demonstrated that the proposed model gives an impressive recognition accuracy of 96.25%, 95.85% and 97.02% on ASL-DS-I, ASL-DS-II and ASL-DS-III, respectively. Full article

(This article belongs to the Special Issue New Trends of Machine Learning Applications in Computer Graphics and Image Processing)

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16 pages, 6728 KiB

Open AccessArticle

Composite Backbone Small Object Detection Based on Context and Multi-Scale Information with Attention Mechanism

by Xinhan Jing, Xuesong Liu and Baolin Liu

Mathematics 2024, 12(5), 622; https://doi.org/10.3390/math12050622 - 20 Feb 2024

Viewed by 519

Abstract

Object detection has gained widespread application across various domains; nevertheless, small object detection still presents numerous challenges due to the inherent limitations of small objects, such as their limited resolution and susceptibility to interference from neighboring elements. To improve detection accuracy of small objects, this study presents a novel method that integrates context information, attention mechanism, and multi-scale information. First, to realize feature augmentation, a composite backbone network is employed which can jointly extract object features. On this basis, to efficiently incorporate context information and focus on key features, the composite dilated convolution and attention module (CDAM) is designed, consisting of a composite dilated convolution module (CDM) and convolutional block attention module (CBAM). Then, a feature elimination module (FEM) is introduced to reduce the feature proportion of medium and large objects on feature layers; the impact of neighboring objects on small object detection can thereby be mitigated. Experiments conducted on MS COCO validate the superior performance of the method compared with baseline detectors, while it yields an average enhancement of 0.8% in overall detection accuracy, with a notable enhancement of 2.7% in small object detection. Full article

(This article belongs to the Special Issue New Trends of Machine Learning Applications in Computer Graphics and Image Processing)

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11 pages, 1380 KiB

Open AccessArticle

Point-Rich: Enriching Sparse Light Detection and Ranging Point Clouds for Accurate Three-Dimensional Object Detection

by Yanchao Zhang, Yinuo Zheng, Dingkun Zhu, Qiaoyun Wu, Hansheng Zeng , Lipeng Gu and Xiangping Bryce Zhai

Mathematics 2023, 11(23), 4809; https://doi.org/10.3390/math11234809 - 28 Nov 2023

Viewed by 652

Abstract

LiDAR point clouds often suffer from sparsity and uneven distributions in outdoor scenes, leading to the poor performance of cutting-edge 3D object detectors. In this paper, we propose Point-Rich, which is designed to improve the performance of 3D object detection. Point-Rich consists of two key modules: HighDensity and HighLight. The HighDensity module addresses the issue of density imbalance by enhancing the point cloud density. The HighLight module leverages image semantic features to enrich the point clouds. Importantly, Point-Rich imposes no restrictions on the 3D object detection architecture and remains unaffected by feature or depth blur. The experimental results show that compared with the Pointpillars on the KITTI dataset, the mAP of Point-Rich under the bird’s eyes view improves by 5.53% on average. Full article

(This article belongs to the Special Issue New Trends of Machine Learning Applications in Computer Graphics and Image Processing)

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16 pages, 896 KiB

Open AccessArticle

Parameter Optimization in a Leaky Integrator Echo State Network with an Improved Gravitational Search Algorithm

by Shuxian Lun, Zhenqian Zhang, Ming Li and Xiaodong Lu

Mathematics 2023, 11(6), 1514; https://doi.org/10.3390/math11061514 - 21 Mar 2023

Cited by 2 | Viewed by 1012

Abstract

In the prediction of a nonlinear time series based on a leaky integrator echo state network (leaky-ESN), building a reservoir related to the specific problem is a key step. For problems such as poor performance of randomly generated reservoirs, it is tough to determine the parameter values of the reservoirs. The work in this paper uses the gravitational search algorithm (GSA) to optimize the global parameters of a leaky-ESN, such as the leaking rate, the spectral radius, and the input scaling factor. The basic GSA has some problems, such as slow convergence and poor balance between exploration and exploitation, and it cannot solve some complex optimization problems well. To solve these problems, an improved gravitational search algorithm (IGSA) is proposed in this paper. First, the best agent and elite agents were archived and utilized to accelerate the exploration phase and improve the convergence rate in the exploitation phase. Second, to improve the effect of the poor fitness agents on the optimization result, a differential mutation strategy was proposed, which generated new individuals to replace original agents with worse fitness, increasing the diversity of the population and improving the global optimization ability of the algorithm. Finally, two simulation experiments showed that the leaky-ESN optimized by the IGSA had better prediction accuracy. Full article

(This article belongs to the Special Issue New Trends of Machine Learning Applications in Computer Graphics and Image Processing)

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15 pages, 2775 KiB

Open AccessArticle

The Choice of Machine Learning Algorithms Impacts the Association between Brain-Predicted Age Difference and Cognitive Function

by Won Hee Lee

Mathematics 2023, 11(5), 1229; https://doi.org/10.3390/math11051229 - 02 Mar 2023

Viewed by 1664

Abstract

Machine learning has been increasingly applied to neuroimaging data to compute personalized estimates of the biological age of an individual’s brain (brain age). The difference between an individual’s brain-predicted age and their chronological age (brainPAD) is used as a biomarker of brain aging and disease, but the potential contribution of different machine learning algorithms used for brain age prediction to the association between brainPAD and cognitive function has not been investigated yet. Here, we applied seven commonly used algorithms to the same multimodal brain imaging data (structural and diffusion MRI) from 601 healthy participants aged 18–88 years in the Cambridge Centre for Ageing and Neuroscience to assess variations in brain-predicted age. The inter-algorithm similarity in brain-predicted age and brain regional regression weights was examined using the Pearson’s correlation analyses and hierarchical clustering. We then assessed to what extent machine learning algorithms impact the association between brainPAD and seven cognitive variables. The regression models achieved mean absolute errors of 5.46–7.72 years and Pearson’s correlation coefficients of 0.86–0.92 between predicted brain age and chronological age. Furthermore, we identified a substantial difference in linking brainPAD to cognitive measures, indicating that the choice of algorithm could be an important source of variability that confounds the relationship between brainPAD and cognition. Full article

(This article belongs to the Special Issue New Trends of Machine Learning Applications in Computer Graphics and Image Processing)

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14 pages, 1854 KiB

Open AccessArticle

Correlation Filter of Multiple Candidates Match for Anti-Obscure Tracking in Unmanned Aerial Vehicle Scenario

by Zhen Chen, Hongyuan Zheng, Xiangping (Bryce) Zhai, Kangliang Zhang and Hua Xia

Mathematics 2023, 11(1), 163; https://doi.org/10.3390/math11010163 - 28 Dec 2022

Viewed by 750

Abstract

Due to the complexity of Unmanned Aerial Vehicle (UAV) target tracking scenarios, tracking drift caused by target occlusion is common and has no suitable solution. In this paper, an occlusion-resistant target tracking algorithm based on the correlated filter tracking model is proposed. First, instead of the traditional target tracking model that uses single template matching to locate the target, we locate the target by finding the optimal match based on multiple candidates templates matching. Then, in order to increase the accuracy of matching, we use the self-attentive mechanism for feature enhancement. We experiment our proposed algorithm on datasets OTB100 and UAV123, respectively, and the results show that the tracking accuracy of our algorithm outperforms the traditional correlated filtered target tracking model. In addition, we have also tested the anti-occlusion performance of our proposed algorithm on some video sequences in which the target is occluded. The results show that our proposed algorithm has a certain resistance to occlusion, especially in the UAV tracking scenario. Full article

(This article belongs to the Special Issue New Trends of Machine Learning Applications in Computer Graphics and Image Processing)

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19 pages, 22409 KiB

Open AccessArticle

Absent Color Indexing: Histogram-Based Identification Using Major and Minor Colors

by Ying Tian, Ming Fang and Shun’ichi Kaneko

Mathematics 2022, 10(13), 2196; https://doi.org/10.3390/math10132196 - 23 Jun 2022

Cited by 3 | Viewed by 1924

Abstract

The color histogram is a statistical behavior for robust pattern search or matching; however, difficulties have arisen in using it to discriminate among similar objects. Our method, called absent color indexing (ABC), describes how to use absent or minor colors as a feature in order to solve problems while robustly recognizing images, even those with similar color features. The proposed approach separates a source color histogram into apparent (AP) and absent (AB) color histograms in order to provide a fair way of focusing on the major and minor contributions together. A threshold for this separation is automatically obtained from the mean color histogram by considering the statistical significance of the absent colors. After these have been separated, an inversion operation is performed to reinforce the weight of AB. In order to balance the contributions of the two histograms, four similarity measures are utilized as candidates for combination with ABC. We tested the performance of ABC in terms of the F-measure using different similarity measures, and the results show that it is able to achieve values greater than 0.95. Experiments on Mondrian random patterns verify the ability of ABC to distinguish similar objects by margin. The results of extensive experiments on real-world images and open databases are presented here in order to demonstrate that the performance of our relatively simple algorithm remained robust even in difficult cases. Full article

(This article belongs to the Special Issue New Trends of Machine Learning Applications in Computer Graphics and Image Processing)

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