Special Issue "Applications of Intelligent Systems in Computer Vision"

Submission Deadline: 30 August 2021 (closed)
Guest Editors
Dr. Nitin Mittal, Chandigarh University, India.
Dr. Amit Kant Pandit, Shri Mata Vaishno Devi University, J&K, India.
Dr. Diego Oliva, Universidad de Guadalajara, Mexico.
Dr. Simrandeep Singh, Chandigarh University, India.

Summary

Nowadays the use of digital images and videos has been extended to different fields as surveillance, manufacturing, medicine, agriculture, machine/robot vision, pattern recognition etc. They are easy to obtain from various environments, and it is necessary to recognize the objects contained in the scenes. In order to achieve this goal, several computational approaches have been developed However, considering the technological advances problems as image size, noise, illumination and security, it is necessary to develop new methodologies and to improve the classical algorithms. On the other hand, a tendency is that the computer vision and image processing systems should be able to automatic extract the desired features for a particular task. Computational Intelligence (CI) approaches are alternative solutions for automatic computer vision and image processing systems; they include the use of tools as machine learning and soft computing. Researchers from all over the world are working hard creating new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision.

This proposal aims to provide a collection of high-quality research works that address broad challenges in both theoretical and application aspects of soft computing and machine learning in image processing and computer vision. We invite researchers and colleagues to contribute original work that will stimulate the continuing effort on the application of CI approaches to solve image-processing and computer vision problems.

We invite all researchers and practitioners who are developing algorithms, systems, and applications, to share their results, ideas, and experiences.

 

Topics of interest include, but are not limited to, the following:

· Computer vision based mobile applications and clinical information assessment

· Automated agricultural growth mapping and crop yield production

· Fog Vision Enhancement and applications

· Healthcare, Automotive, Robotics, Surveillance and Remote Sensing through imaging

· Colour compensation and enhancement of underwater images

· Multi-temporal, multi-modal and multi-focus image fusion

· Pharmaceutical automated inspection and sorting

· Optical character recognition and pattern recognition

· Blob detection, extraction and image matching

· Robust image representation for scalable visual retrieval and recognition

· Futuristic biomedical analysis through hybrid techniques

· Computer aided Analysis in oncology and radiology

· Hybrid intelligent systems in Biometric Recognition

· Recent trends for low dose radiation imaging

· Efficient algorithms in Super-resolution imaging

· Artificial Intelligence: Knowledge representation and Reasoning

· Machine learning in healthcare systems

· Deep learning for sensor dependent activity recognition

· Advances in graph-based representation in pattern recognition

· Application of Internet of things for implicit Biometric authentication and monitoring

· Multimedia analysis, computer vision, natural language processing for visual question answering

· Imaging in Digital Industrial Forensics and Bio cybernetics

· Ambient assistive living using artificial intelligence and imaging


Keywords
Image segmentation
Bio medical Images
Meta heuristic
Machine Vision
Pattern recognition
Image Fusion
Image De noising
Image Enhancement
Satellite images
Soft computing

Published Papers
  • Automated Identification Algorithm Using CNN for Computer Vision in Smart Refrigerators
  • Abstract Machine Learning has evolved with a variety of algorithms to enable state-of-the-art computer vision applications. In particular the need for automating the process of real-time food item identification, there is a huge surge of research so as to make smarter refrigerators. According to a survey by the Food and Agriculture Organization of the United Nations (FAO), it has been found that 1.3 billion tons of food is wasted by consumers around the world due to either food spoilage or expiry and a large amount of food is wasted from homes and restaurants itself. Smart refrigerators have been very successful in… More
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  • A Novel Hybrid Tunicate Swarm Naked Mole-Rat Algorithm for Image Segmentation and Numerical Optimization
  • Abstract This paper provides a new optimization algorithm named as tunicate swarm naked mole-rat algorithm (TSNMRA) which uses hybridization concept of tunicate swarm algorithm (TSA) and naked mole-rat algorithm (NMRA). This newly developed algorithm uses the characteristics of both algorithms (TSA and NMRA) and enhance the exploration abilities of NMRA. Apart from the hybridization concept, important parameter of NMRA such as mating factor is made to be self-adaptive with the help of simulated annealing mutation operator and there is no need to define its value manually. For evaluating the working capabilities of proposed TSNMRA, it is tested for 100-digit challenge (CEC… More
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  • Deep Learning Based Automated Diagnosis of Skin Diseases Using Dermoscopy
  • Abstract Biomedical image analysis has been exploited considerably by recent technology involvements, carrying about a pattern shift towards ‘automation’ and ‘error free diagnosis’ classification methods with markedly improved accurate diagnosis productivity and cost effectiveness. This paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy images. The proposed model has four convolutional layers, two maxpool layers, one fully connected layer and three dense layers. All the convolutional layers are using the kernel size of 3 * 3 whereas the maxpool layer is using the kernel size of 2 * 2. The dermoscopy images… More
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  • Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach
  • Abstract Autism Spectrum Disorder (ASD) is a developmental disorder whose symptoms become noticeable in early years of the age though it can be present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completely but can be reduced if detected early. An early diagnosis is hampered by the variation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergence of deep learning approaches in various fields, medical experts can be assisted in early diagnosis of ASD. It… More
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  • Deep Learning Based Automated Detection of Diseases from Apple Leaf Images
  • Abstract In Agriculture Sciences, detection of diseases is one of the most challenging tasks. The mis-interpretations of plant diseases often lead to wrong pesticide selection, resulting in damage of crops. Hence, the automatic recognition of the diseases at earlier stages is important as well as economical for better quality and quantity of fruits. Computer aided detection (CAD) has proven as a supportive tool for disease detection and classification, thus allowing the identification of diseases and reducing the rate of degradation of fruit quality. In this research work, a model based on convolutional neural network with 19 convolutional layers has been proposed… More
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