AI-Driven Sensing for Small Object Recognition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 6495
Special Issue Editors
Interests: embedded systems; micro-processor and robot; stochastic signal analysis; multi-media technology and application; cloud computing; compile principle
2. Department of Electrical & Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: resource allocation; learning (artificial intelligence)
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; machine learning; cybersecurity and privacy; cyber-physical systems; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Small-object recognition is a fundamental research problem in the field of computer vision and sensing. It is difficult to detect small-objects due to many practical factors, such as indistinguishable features, low resolution, complicated backgrounds, and limited contextual information. The success of small-object recognition could enable many important tasks, such as object tracking, instance segmentation, image captioning, action recognition, and scene understanding. Small-object recognition also plays an important role in real-world scenarios, e.g., urban planning, robotic vision, autonomous driving, intelligent transportation, intelligent healthcare, military reconnaissance and surveillance, where objects of interest could be of a very small size. The integration of advanced artificial intelligence (AI) and sensing technologies could contribute potential solutions to this challenging task. As a result, this Special Issue aims to present the latest advances in deep models, algorithms, and applications that contribute to AI-driven small-object recognition. The main topics include (but are not limited to):
- Effective feature extraction for small-object recognition;
- Data augmentation for small-object recognition;
- Cross-/multi-modal learning for small-object recognition;
- Meta learning for small-object recognition;
- Explainable models for small-object recognition;
- Robust learning for small-object recognition;
- Novel benchmarks for small-object recognition;
- Novel applications for small-object recognition.
Prof. Dr. Jianqiang Li
Prof. Dr. Victor C. M. Leung
Dr. Houbing Song
Dr. Jie Chen
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. Sensors 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.
