This document provides an introduction to concepts in digital geometry including data models, the Euclidean and Cartesian worlds, vectors, planes, intersections, and transformations. It discusses how geometry is represented digitally versus how it appears visually. Key vector concepts like summation, dot products, and cross products are explained along with applications in computer graphics, computational geometry, and detecting properties like perpendicularity and parallelism. Parametric modeling is presented as a way to think of geometric entities in terms of parameters rather than fixed values.
Lecture notes of the course Future Models I (AR1TWF030), The Why Factory, Directed by Prof. Winy Mass, TU Delft, Faculty of Architecture and Built Environment
Preliminaries of Analytic Geometry and Linear Algebra 3D modellingPirouz Nourian
from my lecture notes for the course Geo1004 (2015), 3D modelling of the built environment, at TU Delft, faculty of Architecture and the Built Environment
Lecture notes of the course Future Models I (AR1TWF030), The Why Factory, Directed by Prof. Winy Mass, TU Delft, Faculty of Architecture and Built Environment
Preliminaries of Analytic Geometry and Linear Algebra 3D modellingPirouz Nourian
from my lecture notes for the course Geo1004 (2015), 3D modelling of the built environment, at TU Delft, faculty of Architecture and the Built Environment
Thesis report and full details: https://imatge.upc.edu/web/publications/contextless-object-recognition-shape-enriched-sift-and-bags-features
Author: Marcel Tella
Advisors: Xavier Giró-i-Nieto (UPC) and Matthias Zeppelzauer (TU Wien)
Degree: Telecommunications Engineering (5 years) at Telecom BCN-ETSETB (UPC)
Abstract:
Currently, there are highly competitive results in the field of object recognition based on the aggregation of point-based features. The aggregation process, typically with an average or max-pooling of the features generates a single vector that represents the image or region that contains the object.
The aggregated point-based features typically describe the texture around the points with descriptors such as SIFT. These descriptors present limitations for wired and textureless objects. A possible solution is the addition of shape-based information. Shape descriptors have been previously used to encode shape information and thus, recognise those types of objects. But generally an alignment step is required in order to match every point from one shape to other ones. The computational cost of the similarity assessment is high.
We purpose to enrich location and texture-based features with shape-based ones. Two main architectures are explored: On the one side, to enrich the SIFT descriptors with shape information before they are aggregated. On the other side, to create the standard Bag of Words histogram and concatenate a shape histogram, classifying them as a single vector.
We evaluate the proposed techniques and the novel features on the Caltech-101 dataset.
Results show that shape features increase the final performance. Our extension of the Bag of Words with a shape-based histogram(BoW+S) results in better performance. However, for a high number of shape features, BoW+S and enriched SIFT architectures tend to converge.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2014-member-meeting-scottkrig
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Scott Krig, author of the book "Computer Vision Metrics: Survey, Taxonomy, and Analysis," delivers the presentation "Introduction to Feature Descriptors in Vision: From Haar to SIFT" at the September 2014 Embedded Vision Alliance Member Meeting.
Prepared as a conference tutorial, MIC-Electrical, Athens, Greece, 5th April 2014, updated and delivered again in Beijing, China, 27 January 2015 to students from Complex Systems Group, CSRC and Dept. of Engineering Physics, Tsinghua University
Thesis report and full details: https://imatge.upc.edu/web/publications/contextless-object-recognition-shape-enriched-sift-and-bags-features
Author: Marcel Tella
Advisors: Xavier Giró-i-Nieto (UPC) and Matthias Zeppelzauer (TU Wien)
Degree: Telecommunications Engineering (5 years) at Telecom BCN-ETSETB (UPC)
Abstract:
Currently, there are highly competitive results in the field of object recognition based on the aggregation of point-based features. The aggregation process, typically with an average or max-pooling of the features generates a single vector that represents the image or region that contains the object.
The aggregated point-based features typically describe the texture around the points with descriptors such as SIFT. These descriptors present limitations for wired and textureless objects. A possible solution is the addition of shape-based information. Shape descriptors have been previously used to encode shape information and thus, recognise those types of objects. But generally an alignment step is required in order to match every point from one shape to other ones. The computational cost of the similarity assessment is high.
We purpose to enrich location and texture-based features with shape-based ones. Two main architectures are explored: On the one side, to enrich the SIFT descriptors with shape information before they are aggregated. On the other side, to create the standard Bag of Words histogram and concatenate a shape histogram, classifying them as a single vector.
We evaluate the proposed techniques and the novel features on the Caltech-101 dataset.
Results show that shape features increase the final performance. Our extension of the Bag of Words with a shape-based histogram(BoW+S) results in better performance. However, for a high number of shape features, BoW+S and enriched SIFT architectures tend to converge.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2014-member-meeting-scottkrig
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Scott Krig, author of the book "Computer Vision Metrics: Survey, Taxonomy, and Analysis," delivers the presentation "Introduction to Feature Descriptors in Vision: From Haar to SIFT" at the September 2014 Embedded Vision Alliance Member Meeting.
Prepared as a conference tutorial, MIC-Electrical, Athens, Greece, 5th April 2014, updated and delivered again in Beijing, China, 27 January 2015 to students from Complex Systems Group, CSRC and Dept. of Engineering Physics, Tsinghua University
Sharing historical maps and atlases in web appsAileen Buckley
Historical maps and atlases in print form are often difficult for large numbers of readers to find, access, and use. In this workshop, learn how to bring these valuable resources to life in web apps that not only provide users with access to the origin print content, but also add value through the user’s experience with the app. This workshop takes you step-by-step through a workflow for converting map collections and atlas content to a format that can be shared online. You’ll learn how to scan, georeference, and build metadata for the maps and atlas pages; how to convert the scanned map images to image services; and how to create a web app that gives users access to the scanned images and provides functionality for useful and engaging online map and atlas exploration.
Using Deep Learning to Derive 3D Cities from Satellite ImageryAstraea, Inc.
Detection and reconstruction of 3D buildings in urban areas has been a hot topic of research due to its many applications, including 3D population density studies, emergency planning, and building value estimation. Standard approaches to extract building footprint and measure building height rely on either aerial or space borne point cloud data, which in many areas is unavailable. In contrast, high resolution satellite imagery has become more readily available in recent years, and could provide enough information to estimate a building’s height. Recent successes of deep learning on semantic segmentation have shown that convolutional neural networks can be effective tools at extracting 2D building footprints. Using a digital surface model derived using FOSS and LiDAR data as ground truth, this study goes a step further by employing state of the art deep learning architectures such as U-net to infer both building footprints and estimated building heights in one pass from a single satellite image. This application of open deep learning frameworks can bring the benefits of 3D cities to a larger portion of the world.
“Accident Reconstruction” by Aleksis Liekna from Scope Technologies at Auto f...DevClub_lv
When an accident occurs, it is vital to analyze the chain of events that led to such outcome, especially if the consequences are tragic. Involved parties, including participants themselves, their family members, legal institutions and insurance companies require information and we are doing our best to keep them informed.In this session we will present our advancements in accident analysis, starting with the basics of accident reconstruction and changes in market demands and ending with displaying geographic information in 3D space as well as using Google StreetView API to re-create realistic 3D environments.
Aleksis is a software developer in Scope Technologies with more than 10 years of experience in various software development platforms, currently focusing on .NET, JavaScript and ThreeJS.
Lecture notes of the course Future Models I (AR1TWF030), The Why Factory, Directed by Prof. Winy Mass, TU Delft, Faculty of Architecture and Built Environment
Lecture notes of the course Future Models I (AR1TWF030), The Why Factory, Directed by Prof. Winy Mass, TU Delft, Faculty of Architecture and Built Environment
Ar1 twf030 lecture2.1: Geometry and Topology in Computational DesignPirouz Nourian
Lecture notes of the course Future Models I (AR1TWF030), The Why Factory, Directed by Prof. Winy Mass, TU Delft, Faculty of Architecture and Built Environment
This is a short presentation given in the context of a computational design course for MSc architectural engineering students. It is hopefully insightful for other engineering students as well.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Steel & Timber Design according to British Standard
Ar1 twf030 lecture1.2
1. 11
Preliminaries of
Basics of Linear Algebra & Computer Geometry
Dr.ir. Pirouz Nourian
Assistant Professor of Design Informatics
Department of Architectural Engineering & Technology
Faculty of Architecture and Built Environment
2. 22
Try to guess how a line or a circle is represented in a computer
“If it looks like a duck, swims like a duck, and quacks like a duck, then
it probably is a duck.”
Image: DUCK: GETTY Images; ILLUSTRATION: MARTIN O'NEILL, from
http://www.nature.com/nature/journal/v484/n7395/full/484451a.html?message-global=remove
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
3. 33
The image of a geometry is not the same as its representation
What you see is not what you get
Image: René Magritte, ceci n'est pas une pipe (this is not a pipe)
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
4. 44
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
5. 55
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
On terminology
• Geometry: Point (0D), Curve(1D), Surface(2D), Solid (3D) [free-form]
• Geometry: Point (0D), Line(1D), Polygon(2D), Polyhedron (3D) [piecewise linear]
• Topology: Vertex(0D), Edge(1D), Face(2D), Body(3D)
• Graph Theory: Object, Link, (and n-Cliques)
6. 66
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
WYSIWYG versus WYSIWYM
𝑥2
+ 𝑦2
= 𝑅2
The Product vs The Process
7. 77
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
Parametric Modeling & Design
• Thinking of parameters instead of numbers!
• Same rationales, many alternatives!
▪ We could model an actual circle as a particular instance of a generic circle, which is
the locus of points equidistant from a given point as C (center), at a given distance R
(Radius), on a plane p.
▪ Parametric modeling is essential for formulating design problems
▪ The same role algebra has had in the progress of mathematics, parametric modeling
will have in systematic (research-oriented) design.
𝑥 = 𝑟𝑐𝑜𝑠(𝑡)
𝑦 = 𝑟𝑠𝑖𝑛 𝑡
𝑡 ∈ [0,2𝜋]
𝑡 =
2𝜋𝑖
𝑛
|𝑖 ∈[1,n]⊂ ℕ
Plane
Radius
Circle
8. 88
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
Parametric Modeling & Design
• Thinking of parameters instead of numbers!
• Same rationales, many alternatives!
▪ We could model an actual circle as a particular instance of a generic circle, which is
the locus of points equidistant from a given point as C (center), at a given distance R
(Radius), on a plane p.
▪ Parametric modeling is essential for formulating design problems
▪ The same role algebra has had in the progress of mathematics, parametric modeling
will have in systematic (research-oriented) design.
𝑥 = 𝑟𝑐𝑜𝑠(𝑡)
𝑦 = 𝑟𝑠𝑖𝑛 𝑡
𝑡 ∈ [0,2𝜋]
𝑡 =
2𝜋𝑖
𝑛
|𝑖 ∈[1,n]⊂ ℕ
Plane
Radius
Circle
9. 99
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
10. 1010
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
11. 1111
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
12. 1212
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
Vectors in a Nutshell
Applications
• Any representation in Computer Graphics depends on vectors (points,
lines, etc. are all eventually based on vectors)
• Any transformation (e.g. moving objects, rotating them, etc.)
• It suffices to say there is no 3D geometry without vectors!
13. 1313
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
Vectors in a Nutshell
René Descartes
Image courtesy of David Rutten,
from Rhinoscript 101
14. 1414
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
Ԧ𝐴 = 𝑎 𝑥Ԧ𝒊 + 𝑎 𝑦 Ԧ𝒋 + 𝑎 𝑧 𝒌
𝐵 = 𝑏 𝑥Ԧ𝒊 + 𝑏 𝑦 Ԧ𝒋 + 𝑏 𝑧 𝒌
Ԧ𝐴 + 𝐵 = (𝑎 𝑥 + 𝑏 𝑥)Ԧ𝒊 + (𝑎 𝑦+𝑏 𝑦)Ԧ𝒋 + (𝑎 𝑧+𝑏 𝑧)𝒌
Euclidean Vector Length
Ԧ𝐴 = 𝑎 𝑥
2 + 𝑎 𝑦
2
+ 𝑎 𝑧
2
15. 1515
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
Exemplary application: detecting perpendicularity or similarity
𝑊 = 𝑭. 𝑫 = 𝑭 . 𝑫 cos 𝜃
16. 1616
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
Exemplary application: detecting perpendicularity or similarity
𝑊 = 𝑭. 𝑫 = 𝑭 . 𝑫 cos 𝜃
Other applications:
• Computing ‘flux’ in a vector field (e.g. solar irradiation)
• Detecting perpendicularly
• Computing angles (with the help of an Arc Cosine function)
• A very long list of techniques and tricks in computational
geometry & computer graphics
• You cannot get by without knowing about dot products! ☺
17. 1717
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
Dot Product: How is it calculated in analytic geometry?
𝜃
B
A
Ԧ𝒊. Ԧ𝒊 = Ԧ𝒋. Ԧ𝒋 = 𝒌. 𝒌 = 1
Ԧ𝒊. Ԧ𝒋 = Ԧ𝒋. Ԧ𝒊 = 0
Ԧ𝒋. 𝒌 = 𝒌. Ԧ𝒋 = 0
𝒌. Ԧ𝒊 = Ԧ𝒊. 𝒌 = 0
So we do not have to do it by ‘drawing’ vectors and finding the angle between them
with an angle ruler and a calculator! We do it algebraically instead.
18. 1818
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
Dot Product: How is it calculated in analytic geometry?
Ԧ𝐴 = 𝑎 𝑥 Ԧ𝒊 + 𝑎 𝑦 Ԧ𝒋 + 𝑎 𝑧 𝒌 = 𝑎 𝑥 𝑎 𝑦 𝑎 𝑧
𝒊
𝒋
𝒌
𝐵 = 𝑏 𝑥Ԧ𝒊 + 𝑏 𝑦 Ԧ𝒋 + 𝑏 𝑧 𝒌 = 𝑏 𝑥 𝑏 𝑦 𝑏 𝑧
𝒊
𝒋
𝒌
Ԧ𝐴. 𝐵 == Ԧ𝐴 . 𝐵 . 𝐶𝑜𝑠(𝜃)
𝜃
B
A
Ԧ𝐴. 𝐵 = 𝑎 𝑥 𝑎 𝑦 𝑎 𝑧
𝑏 𝑥
𝑏 𝑦
𝑏 𝑧
= 𝑎 𝑥 𝑏 𝑥 + 𝑎 𝑦 𝑏 𝑦 + 𝑎 𝑧 𝑏 𝑧
19. 1919
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
Images courtesy of Wiki Commons and
Raja Issa, Essential Mathematics for Computational Design
http://chortle.ccsu.edu/vectorlessons/vch12/vch12_4.html
20. 2020
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
Exemplary application: setting up a local coordinate system
• computing torque, electromotive force, etc in physics
• detecting parallelism
• a long list of techniques and tricks in computer graphics and computational
geometry
• computing volumes of polyhedrons
• Conclusion: you cannot get by without knowing about cross products either! ☺
21. 2121
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
Cross Product: How is it calculated in analytic geometry?
Images courtesy of
Raja Issa, Essential Mathematics for Computational Design
Ԧ𝒊 × Ԧ𝒊 = Ԧ𝒋 × Ԧ𝒋 = 𝒌 × 𝒌 = 𝟎
Ԧ𝒊 × Ԧ𝒋 = 𝒌
Ԧ𝒋 × 𝒌 = Ԧ𝒊
𝒌 × Ԧ𝒊 = Ԧ𝒋
Ԧ𝒋 × Ԧ𝒊 = −𝒌
𝒌 × Ԧ𝒋 = −Ԧ𝒊
Ԧ𝒊 × 𝒌 = −Ԧ𝒋
23. 2323
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
24. 2424
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
Images courtesy of David Rutten, Rhino Script 101
25. 2525
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
26. 2626
• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
http://geomalgorithms.com/a05-_intersect-1.html
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• Digital Geometry
• Data Models
• Euclidean World
• Cartesian World
• Vectors
o Sum
o Dot Product
o Cross Product
• Planes
o Locus
o Orientation
• Intersection
• Transformation
• Linear Transformations: Euclidean and Affine (Translation [movement], Rotation, Scaling,etc.)
• Homogenous Coordinate System
• Inverse Transforms?
• Non-Linear Transformations?
Images courtesy of Raja Issa, Essential Mathematics for Computational Design