mathematics-logo

Journal Browser

Journal Browser

Innovative Intelligent Systems in Computer Vision: Methodologies, Algorithms, and Applications

A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1612

Editor


E-Mail Website
Guest Editor
Computer Information Science, Higher Colleges of Technology (HCT), Abu Dhabi, United Arab Emirates
Interests: artificial intelligence; computer vision; deep learning; pattern recognition; machine learning; visual understanding; edge AI; AI in engineering applications; explainable AI; embedded intelligent systems

Special Issue Information

Dear Colleagues,

This Special Issue seeks original research and review articles focused on innovative intelligent systems in the field of computer vision, with emphasis on mathematical methodologies, learning algorithms, and their real-world applications. Fueled by rapid developments in deep learning and AI, computer vision has emerged as a dominant discipline across science and engineering.
We welcome contributions that explore:

  • Foundational models and novel algorithmic frameworks
  • Mathematical analysis and performance evaluation
  • Practical deployments in fields such as autonomous systems, healthcare, and industrial automation

Topics of interest include (but are not limited to): object detection, image segmentation, pattern recognition, scene understanding, video analytics, multimodal learning, and visual computing at the edge.
We also encourage review articles that provide a comprehensive synthesis of current trends or spotlight emerging opportunities in this vibrant domain.

We look forward to your high-quality submissions.

Dr. Ali Al Bataineh
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • computer vision
  • intelligent systems
  • deep learning
  • machine learning
  • image processing
  • pattern recognition
  • object detection
  • visual understanding

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

17 pages, 2160 KB  
Article
Mahalanobis-Constrained Hard-Clustering Algorithm
by Vedran Novoselac
Mathematics 2026, 14(15), 2701; https://doi.org/10.3390/math14152701 - 28 Jul 2026
Viewed by 370
Abstract
The paper proposes a modification of the standard k-means algorithm that removes low-probability tail points of Gaussian components using the p-quantile of the chi-squared distribution as a trimming threshold based on the squared Mahalanobis distance. The procedure is evaluated on synthetic [...] Read more.
The paper proposes a modification of the standard k-means algorithm that removes low-probability tail points of Gaussian components using the p-quantile of the chi-squared distribution as a trimming threshold based on the squared Mahalanobis distance. The procedure is evaluated on synthetic noisy datasets and compared with well-known state-of-the-art clustering methods. Applications in circular image pattern recognition demonstrate effective detection of spherical patterns in highly noisy environments. Pattern detection is driven by an optimization using a constructed spherical clustering-validity index, and the method’s practical potential is confirmed by comparisons with state-of-the-art circular detection techniques. Full article
Show Figures

Figure 1

28 pages, 6518 KB  
Article
Fine-Grained Pose-Aware Visual Fusion for Emotion Recognition in Conversational Video Streams
by Constantin Bogdan Popescu and Corneliu Florea
Mathematics 2026, 14(14), 2639; https://doi.org/10.3390/math14142639 - 20 Jul 2026
Viewed by 399
Abstract
Despite the rapid advancement of Emotion Recognition in Conversation (ERC), prevailing systems that primarily integrate language and speech exhibit substantial performance disparities on underrepresented emotion classes (e.g., Fear, Disgust). This study investigates whether fine-grained non-verbal visual modalities (facial action units, hand gestures, and [...] Read more.
Despite the rapid advancement of Emotion Recognition in Conversation (ERC), prevailing systems that primarily integrate language and speech exhibit substantial performance disparities on underrepresented emotion classes (e.g., Fear, Disgust). This study investigates whether fine-grained non-verbal visual modalities (facial action units, hand gestures, and body pose) can effectively mitigate these biases. We propose a multi-stream fusion architecture combining language, speech, and engineered pose-aware visual features, trained with class-imbalance-aware objectives. Experiments on MELD demonstrate that hybrid pose augmentation improves F1 on the least frequent classes: Fear +9.19%, Disgust +6.07%, Sadness +6.46%. We achieve an overall weighted F1 of 68.79%, competitive with recent state-of-the-art systems while uniquely targeting minority-class debiasing. These results establish fine-grained body language as a critical debiasing signal, recovering accuracy on the subtle expressions that text and speech alone fail to capture. Full article
Show Figures

Figure 1

Back to TopTop