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.

Summary

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.


Keywords
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
  • An Optimized Convolution Neural Network Architecture for Paddy Disease Classification
  • Abstract Plant disease classification based on digital pictures is challenging. Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize, identify, and diagnose plant diseases in the previous decade. Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries. However, some diseases that are blocking the improvement in paddy production are considered as an ominous threat. Convolution Neural Network (CNN) has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and technology.… More
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  • Use of Local Region Maps on Convolutional LSTM for Single-Image HDR Reconstruction
  • Abstract Low dynamic range (LDR) images captured by consumer cameras have a limited luminance range. As the conventional method for generating high dynamic range (HDR) images involves merging multiple-exposure LDR images of the same scene (assuming a stationary scene), we introduce a learning-based model for single-image HDR reconstruction. An input LDR image is sequentially segmented into the local region maps based on the cumulative histogram of the input brightness distribution. Using the local region maps, SParam-Net estimates the parameters of an inverse tone mapping function to generate a pseudo-HDR image. We process the segmented region maps as the input sequences on… More
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  • 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
  •   Views:680       Downloads:567        Download PDF