Image Processing and Capsule Networks ICIPCN 2020 1st edition by Joy Iong Zong Chen, Joao Manuel Tavares, Subarna Shakya, Abdullah Iliyasu ISBN 3030518590 9783030518592

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ISBN 10:  3030518590
ISBN 13: 978-3030518592
Author:       Joy Iong-Zong Chen, João Manuel R. S. Tavares, Subarna Shakya, Abdullah M. Iliyasu

Image Processing and Capsule Networks ICIPCN 2020 1st Table of contents:

1. Efficient GAN-Based Remote Sensing Image Change Detection under Noise Conditions

• Context: This topic involves using Generative Adversarial Networks (GANs) for detecting changes in remote sensing images, such as satellite or aerial imagery, while handling noise conditions. Remote sensing image change detection is important for applications like environmental monitoring or urban planning. The challenge addressed here is the efficiency of GANs in detecting changes despite noisy data or distorted images.

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2. Recognition of Handwritten Digits by Image Processing Methods and Classification Models

• Context: This research focuses on techniques for recognizing handwritten digits, likely similar to the MNIST dataset, using image processing and classification models. It may involve methods like preprocessing, feature extraction, and applying classification algorithms (e.g., support vector machines, neural networks) to improve recognition accuracy.

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3. Convolutional Neural Network with Multi-Column Characteristics Extraction for Image Classification

• Context: Convolutional Neural Networks (CNNs) are widely used for image classification tasks. This research likely explores an enhancement to CNNs by using multi-column characteristics extraction, where multiple columns of the network are employed to extract different features from images simultaneously, potentially improving the classification performance.

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4. Face Detection Based on Image Stitching for Class Attendance Checking

• Context: This topic uses image stitching techniques to detect faces in images for applications like class attendance monitoring. The idea is to combine multiple images or video frames to create a comprehensive view of a classroom, then apply face detection algorithms to identify and mark the attendance of students.

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5. Image Processing Technique for Effective Analysis of the Cytotoxic Activity in Human Breast Cancer Cell Lines

• Context: This research focuses on using image processing methods to analyze cytotoxic activity in human breast cancer cell lines. The objective is likely to identify cancer cell response to drugs or treatments by analyzing images of cell cultures. This could involve segmenting and quantifying cell death, proliferation, or other characteristics indicative of treatment efficacy.

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6. Development of an Algorithm for Vertebrae Identification Using Speeded Up Robust Features (SURF)

• Context: The use of SURF (Speeded Up Robust Features) for vertebrae identification is relevant in medical imaging, particularly for analyzing X-rays, MRIs, or CT scans. The algorithm would help in automatically identifying and labeling vertebrae in spinal images, which could aid in diagnostic procedures or medical robotics applications.

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7. Comparison of Machine Learning Algorithms for Smart License Number Plate Detection System

• Context: This research compares various machine learning algorithms for license plate detection systems. Such systems are used in automatic vehicle identification for security, tolling, or parking management. The comparison might involve different machine learning techniques, such as deep learning, support vector machines, and decision trees, to assess which method is best suited for recognizing license plates under various conditions (e.g., lighting, orientation, noise).

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