Image Processing and Object Detection Using AI

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 1277

Special Issue Editors


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Guest Editor
School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi'an 710121, China
Interests: image processing; image fusion; artificial intelligence; machine learning; medical signal and image processing
School of Computer Science and Technology, Xidian University, Xi'an 710071, China
Interests: computer vison; deep learning; object detection

Special Issue Information

Dear Colleagues,

With the advances seen in modern technology over the past decade, the involvement of artificial intelligence (AI) has been pivotal in enhancing the effectiveness and efficiency in many systems and in all fields of knowledge, including medical diagnosis, healthcare, vehicular technologies, agriculture, policing, industrial manufacturing, finance, sports, and many other domains. The purpose of this Special Issue is to report on the advances in state-of-the-art research on image processing and object detection by using AI. This includes the design and development of novel algorithms based on AI, such as machine learning and deep learning, for applications in image processing and object detection.

Manuscripts are required to show significant improvements in a variety of learning methods, problem conceptualization, data collecting and processing, and feature engineering through critical comparisons with existing methodologies.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Image/video processing;
  • Computer vision and image processing;
  • Medical image fusion;
  • Medical image segmentation;
  • (Multiple) object detection;
  • (Multiple) object tracking;
  • Image/video captioning and visual question and answering;
  • Time series analysis via deep learning;
  • Pattern recognition in engineering and biomedical sciences;
  • Computer aided detection/diagnosis;
  • Computer assisted surgery;
  • Multimodal information fusion.

We look forward to receiving your contributions.

Dr. Weiwei Kong
Dr. Ruyi Liu
Guest Editors

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Keywords

  • image processing
  • object detection
  • artificial intelligence
  • deep learning
  • machine learning
  • computer vision
  • object tracking

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Published Papers (1 paper)

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Research

14 pages, 1261 KiB  
Article
Technique for Kernel Matching Pursuit Based on Intuitionistic Fuzzy c-Means Clustering
by Yang Lei and Minqing Zhang
Electronics 2024, 13(14), 2777; https://doi.org/10.3390/electronics13142777 - 15 Jul 2024
Cited by 1 | Viewed by 785
Abstract
Kernel matching pursuit (KMP) requires every step of the searching process to be global optimal searching in the redundant dictionary of functions in order to select the best matching signal structure. Namely, the dictionary learning time of KMP is too long. To solve [...] Read more.
Kernel matching pursuit (KMP) requires every step of the searching process to be global optimal searching in the redundant dictionary of functions in order to select the best matching signal structure. Namely, the dictionary learning time of KMP is too long. To solve the above drawbacks, a rough dataset was divided into some small-sized dictionaries to substitute local searching for global searching by using the property superiority of dynamic clustering performance, which is also superior in the intuitionistic fuzzy c-means (IFCM) algorithm. Then, we proposed a novel technique for KMP based on IFCM (IFCM-KMP). Subsequently, three tests including classification, effectiveness, and time complexity were carried out on four practical sample datasets, the conclusions of which fully demonstrate that the IFCM-KMP algorithm is superior to FCM and KMP. Full article
(This article belongs to the Special Issue Image Processing and Object Detection Using AI)
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