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Lecture

Mathematical Operations: Image Processing

In course

BIO-695: Image Processing for Life Science

Registration details will be announced via email. It takes place yearly from Sept./October to December & intends to teach image processing with a strong emphasis of applications in life sciences. The idea is to enable the participants to solve image-proces ...

Description

This lecture covers various mathematical operations used in image processing, such as image normalization, intensity flattening, image-to-image operations like addition, subtraction, multiplication, and division, as well as techniques for checking image artifacts and performing flatfield correction.

In MOOC

Image Processing and Analysis for Life Scientists

Instructors (4)

Olivier Burri

Nicolas René Chiaruttini

Arne Seitz

04/1996-06/2000 Scientific co-worker at Philipps-University of Marburg, Dep. of Physical Chemistry 07/2000-10/2002 Post Doc at Max-Planck Unit for Structural Molecular Biology in Hamburg 11/2002-11/2005 Post Doc at European Molecular Biolohy Laboratory (EMBL), Cell Biology and Cell Biophysics Programme 11/2005-03/2009 Staff Scientist at Advanced Light Microscopy Facility, EMBL 04/2009- Head of Bioimaging and Optics platforme (BIOP), Ecole Polytechnique Fédérale de Lausanne (EPFL)

Romain Guiet

Official source

Quiz

Related concepts (8)

Image segmentation

In and computer vision, image segmentation is the process of partitioning a into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

Operation (mathematics)

In mathematics, an operation is a function which takes zero or more input values (also called "operands" or "arguments") to a well-defined output value. The number of operands is the arity of the operation. The most commonly studied operations are binary operations (i.e., operations of arity 2), such as addition and multiplication, and unary operations (i.e., operations of arity 1), such as additive inverse and multiplicative inverse. An operation of arity zero, or nullary operation, is a constant.

Mathematical notation

Mathematical notation consists of using symbols for representing operations, unspecified numbers, relations, and any other mathematical objects and assembling them into expressions and formulas. Mathematical notation is widely used in mathematics, science, and engineering for representing complex concepts and properties in a concise, unambiguous, and accurate way. For example, Albert Einstein's equation is the quantitative representation in mathematical notation of the mass–energy equivalence.

Order of operations

In mathematics and computer programming, the order of operations (or operator precedence) is a collection of rules that reflect conventions about which procedures to perform first in order to evaluate a given mathematical expression. For example, in mathematics and most computer languages, multiplication is granted a higher precedence than addition, and it has been this way since the introduction of modern algebraic notation. Thus, the expression 1 + 2 × 3 is interpreted to have the value 1 + (2 × 3) = 7, and not (1 + 2) × 3 = 9.

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.

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