Title: Advances in Image Processing, Computer Vision and Deep Learning

Aim and Scope

This session mainly focuses to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of Image Processing Technologies, Computer Vision and Deep Learning. The field of computer vision is shifting from statistical methods to deep learning neural network methods. There are still many challenging problems to solve in computer vision. Nevertheless, deep learning methods are achieving state-of-the-art results on some specific problems.It is not just the performance of deep learning models on benchmark problems that is most interesting; it is the fact that a single model can learn meaning from images and performs vision tasks, obviating the need for a pipeline of specialized and hand-crafted methods. It is interdisciplinary platform for researcher scholars to present and discuss the most recent innovations, trends, practical challenges encountered and solutions adopted in the fields of Image Processing Technologies, Computer Vision and Deep Learning.

List of Topics

Topics to be discussed in this special session include (but are not limited to) the following:

  • Image Classification
  • Image Segmentation
  • Object Detection
  • Object Segmentation
  • Image Captioning
  • Image Style Transfer
  • Image Colorization
  • Image Reconstruction
  • Image Super-Resolution
  • Image Synthesis
  • Biomedical Imaging
  • Computer Vision
  • Natural Language Processing
  • Generative adversarial Networks
  • Unsupervised Deep Learning Architectures
  • Speech Processing
  • Question Answering
  • Transfer Learning
  • Q Learning
  • Machine Translation
  • Time Series/Sequence Models
  • Neural Networks
  • Convolutional Networks
  • AutoEncoders
  • Deep Belief Networks
  • Recurrent Neural Networks
  • Long Short Term Memory
  • Deep and restricted Boltzmann Machines
  • Deep Reinforcement Learning
  • Hybrid Models of Deep Learning

Paper Submission Guidelines

Authors are invited to submit papers to the special session. Full papers must be submitted through the conference paper submission site (https://easychair.org/conferences/?conf=icspn2021). Authors must add (Special session: SS03) at the beginning of paper title during the submission. Paper should be submitted in PDF format electronically with maximum length of 10 pages as per the Springer LNNS series format. All manuscripts will be reviewed and judged on merits including originality, significance, interest, correctness, clarity, and relevance to the broader community. Papers are strongly encouraged to report experiences, measurements, and user studies, and to provide an appropriate quantitative evaluation.

Submitted papers must include original work, and may not be under consideration for another conference or journal. They should also not be under review or be submitted to another workshop, conference, or journal during the review process. Authors should submit full papers electronically following the instructions from the ICSPN 2021 conference web site. Accepted papers will appear in proceedings available electronically. Authors of accepted papers are expected to register for and present their work at the special session (in Online mode).

Special Session Chair(s):

Important Dates

  • Submission Deadline: June 20, 2021
  • Decision Notification: July 20, 2021
  • Registration: August 10, 2021

Publication:

All accepted and registered papers of this special session will be published by the conference proceedings published by Springer in LNNS Series.

Journal Publication: Authors of a selected set of top-quality papers from the special session will be invited to submit extended version of their papers in the special issues of SCIE/SSCI/Scopus/WoS indexed journals.

Best Paper: All the regular papers will be considered for the BEST PAPER AWARD.

NOTE: While submitting paper in this special session, please specify “Advances in Image Processing, Computer Vision and Deep Learning” at the top (above paper title) of the first page of your paper.