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Lecture

Computer Vision Basics: Image Processing and Feature Detection

In course

MICRO-452: Basics of mobile robotics

This course gives an overview of the field of mobile robotics, introducing to the main techniques and allowing to test them in exercises and a team-based project. ...

Description

This lecture covers the basics of computer vision, focusing on image processing techniques such as filtering for noise reduction, edge detection, and feature detection using methods like the Hough Transform and Pattern Matching. It also delves into topics like camera calibration, 3D reconstruction, and simultaneous localization and mapping (SLAM). The importance of cameras in computer vision is highlighted, along with the process of extracting real-world information from images. Various algorithms for feature detection, including interest point detectors, are discussed, emphasizing their role in object recognition, 3D structure analysis, and motion tracking.

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Instructor

Francesco Mondada

Dr. Mondada received his M.Sc. in micro-engineering in 1991 and his Doctoral degree in 1997 at EPFL. During his thesis he co-founded the company K-Team, being both CEO and president of the company for about 5 years. He is one of the three main developers of the Khepera robot, considered as a standard in bio-inspired robotics and used by more than 1,000 universities and research centers worldwide. Fully back in research in 2000 and after a short period at CALTECH, he participated to the SWARM-BOTS project as the main developer of the s-bot robot platform, which was ranked on position 39 in the list of The 50 Best Robots Ever (fiction or real) by the Wired Journal in 2006. The SWARM-BOTS project was selected as FET-IST success story by the EU commission. He is author of more than 100 papers in the field of bio-inspired robotics and system level robot design. He is co-editor of several international conference proceedings. In November 2005 he received the EPFL Latsis University prize for his contributions to bio-inspired robotics. In 2011 he received the "Crédit Suisse Award for Best Teaching" from EPFL and in 2012 the "polysphère" award from the students as best teacher in the school of engineering. His interests include the development of innovative mechatronic solutions for mobile and modular robots, the creation of know-how for future embedded applications, and making robot platforms more accessible for education, research, and industrial development.

Official source

Ontological neighbourhood

Information engineering

Artificial intelligence: Computer vision

Related concepts (32)

Feature (computer vision)

In computer vision and , a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, or to shapes defined in terms of curves or boundaries between different image regions.

Corner detection

Corner detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. Corner detection is frequently used in motion detection, , video tracking, image mosaicing, panorama stitching, 3D reconstruction and object recognition. Corner detection overlaps with the topic of interest point detection. A corner can be defined as the intersection of two edges. A corner can also be defined as a point for which there are two dominant and different edge directions in a local neighbourhood of the point.

Edge detection

Edge detection includes a variety of mathematical methods that aim at identifying edges, curves in a at which the image brightness changes sharply or, more formally, has discontinuities. The same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in , machine vision and computer vision, particularly in the areas of feature detection and feature extraction.

Digital image processing

Digital image processing is the use of a digital computer to process s through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over . It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems.

Canny edge detector

The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. It was developed by John F. Canny in 1986. Canny also produced a computational theory of edge detection explaining why the technique works. Canny edge detection is a technique to extract useful structural information from different vision objects and dramatically reduce the amount of data to be processed. It has been widely applied in various computer vision systems.

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