Special Issue "Application of Machine-Learning in Computer Vision"

Submission Deadline: 31 July 2021 (closed)
Guest Editors
Dr. Mujtaba Husnain, The Islamia University of Bahawalpur, Pakistan.
Dr. Malik Muhammad Saad Missen, The Islamia University of Bahawalpur, Pakistan.
Dr. Mickael Coustaty, University of La Rochelle, France.


Machine learning (ML) deals with the specified algorithms that make the computer system capable of learning from the experience without being explicitly programmed. This domain comes under the umbrella of Artificial Intelligence (AI) and it is widely used in a range of disciplines namely, computer vision, medical diagnosis, image processing, signal processing and, robot control. The purpose of our Special Issue is to contribute to the demonstration of new algorithms and application domains of ML to solve problems in various research areas. Eventually, we are to promote research and development of deep learning for multimodal data, by publishing high-quality research articles and reviews/tutorials in this rapidly growing interdisciplinary field. This special issue will focus on collecting the latest research results on ML and its application.

Topics of interest include, but are not limited to:
• Machine learning algorithms in medical imaging
• Scene Understanding
• 3D visual perception
• Human Analysis and modeling
• Document Image Processing
• Optical and Handwritten Character Recognition

Published Papers
  • Encoder-Decoder Based LSTM Model to Advance User QoE in 360-Degree Video
  • Abstract The development of multimedia content has resulted in a massive increase in network traffic for video streaming. It demands such types of solutions that can be addressed to obtain the user's Quality-of-Experience (QoE). 360-degree videos have already taken up the user's behavior by storm. However, the users only focus on the part of 360-degree videos, known as a viewport. Despite the immense hype, 360-degree videos convey a loathsome side effect about viewport prediction, making viewers feel uncomfortable because user viewport needs to be pre-fetched in advance. Ideally, we can minimize the bandwidth consumption if we know what the user motion… More
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  • A Transfer Learning-Based Approach to Detect Cerebral Microbleeds
  • Abstract Cerebral microbleeds are small chronic vascular diseases that occur because of irregularities in the cerebrum vessels. Individuals and elderly people with brain injury and dementia can have small microbleeds in their brains. A recent study has shown that cerebral microbleeds could be remarkably risky in terms of life and can be riskier for patients with dementia. In this study, we proposed an efficient approach to automatically identify microbleeds by reducing the false positives in openly available susceptibility-weighted imaging (SWI) data samples. The proposed structure comprises two different pre-trained convolutional models with four stages. These stages include (i) skull removal and… More
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  • Effective Video Summarization Approach Based on Visual Attention
  • Abstract Video summarization is applied to reduce redundancy and develop a concise representation of key frames in the video, more recently, video summaries have been used through visual attention modeling. In these schemes, the frames that stand out visually are extracted as key frames based on human attention modeling theories. The schemes for modeling visual attention have proven to be effective for video summaries. Nevertheless, the high cost of computing in such techniques restricts their usability in everyday situations. In this context, we propose a method based on KFE (key frame extraction) technique, which is recommended based on an efficient and… More
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