Geometric Feature Learning: Unlocking Visual Insights through Geometric Feature Learning
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Geometric feature learning is a technique combining machine learning and computer vision to solve visual tasks. The main goal of this method is to find a set of representative features of geometric form to represent an object by collecting geometric features from images and learning them using efficient machine learning methods. Humans solve visual tasks and can give fast response to the environment by extracting perceptual information from what they see. Researchers simulate humans' ability of recognizing objects to solve computer vision problems. For example, M. Mata et al.(2002) applied feature learning techniques to the mobile robot navigation tasks in order to avoid obstacles. They used genetic algorithms for learning features and recognizing objects (figures). Geometric feature learning methods can not only solve recognition problems but also predict subsequent actions by analyzing a set of sequential input sensory images, usually some extracting features of images. Through learning, some hypothesis of the next action are given and according to the probability of each hypothesis give a most probable action. This technique is widely used in the area of artificial intelligence.
How you will benefit
(I) Insights, and validations about the following topics:
Chapter 1: Geometric Feature Learning
Chapter 2: Pattern Recognition
Chapter 3: Hough Transform
Chapter 4: Loss Function
Chapter 5: Expectation-Maximization Algorithm
Chapter 6: Rejection Sampling
Chapter 7: Array Processing
Chapter 8: Autoencoder
Chapter 9: Stochastic Approximation
Chapter 10: Chessboard Detection
(II) Answering the public top questions about geometric feature learning.
(III) Real world examples for the usage of geometric feature learning in many fields.
Who this book is for
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Geometric Feature Learning.
