The aim of geom3d library is to handle and visualize 3D geometric primitives such as points, lines, planes, polyhedra… It provides low-level functions for manipulating 3D geometric primitives, making easier the development of more complex geometric algorithms.
Μήνας: Ιούλιος 2014
How-To: Create custom surface models from scattered MATLAB data
Those wishing to model a surface from data in the form of z(x,y) from scattered or semi-scattered data have had few options in matlab – mainly griddata.
Griddata is a valuable tool for interpolation of scattered data. However it fails when there are replicates or when the data has many collinear points. Griddata is also unable to extrapolate beyond the convex hull of the data unless the ‘v4’ option is used, which is slow.
Gridfit solves all of these problems, although it is not an interpolant. It builds a surface over a complete lattice, extrapolating smoothly into the corners. You have control of the amount of smoothing done, as well as interpolation methods, which solver to use, etc.
Καταπολεμώντας τη διαδικτυακή παρενόχληση – on the frontline
Κακόβουλο κείμενα, με σεξουαλικά υπονοούμενα,…
Καταπολεμώντας τη διαδικτυακή παρενόχληση – on the frontline
Κακόβουλο κείμενα, με σεξουαλικά υπονοούμενα,…
Introduction to A*
In games we often want to find paths from one location to another. A* is a commonly recommended algorithm. Breadth First Search, Dijkstra’s Algorithm, and A* are graph search algorithms that use the same basic structure. They represent the map as a graph and then find a path in that graph. If you haven’t seen node-and-edge graphs before, here’s my introductory article. Breadth First Search is the simplest of the graph search algorithms, so let’s start there, and we’ll work our way up to A*.
Η πληροφορική είναι η λύση για την άνοια; Όλα τα αποτελέσματα σημαντικής έρευνας!
Το νέο “φάρμακο” κατά της άνοιας, φαίνεται αποτελούν οι σύγχρονες τεχνολογίες της πληροφορικής, κρίνοντας από τα αποτελέσματα που παρουσιάζει, στα δύο χρόνια πιλοτικής λειτουργίας το σύστημα LLMCare, αποτέλεσμα του ερευνητικού έργου «Μνήμες Διάρκειας» (Long Lasting Memories – LLM).
Το LLMCare βασίζεται σε σύγχρονες τεχνολογίες πληροφορικής και αποτελεί μια ενοποιημένη τεχνολογική πλατφόρμα, που στοχεύει στη νοητική και σωματική άσκηση με τη χρήση λογισμικού μέσα σε ένα διασκεδαστικό περιβάλλον υποβοηθούμενης διαβίωσης.
Διανομές Linux: Οι 14 Γνωστότερες / Καλύτερες…
Σύμφωνα με τη Wikipedia, αυτή τη στιγμή υπάρχουν περισσότερες από 250 διαφορετικές διανομές Linux, η συντριπτική πλειοψηφία των οποίων είναι διαθέσιμη δωρεάν. Στον οδηγό αυτό θα παρουσιάσουμε τις 14 σημαντικότερες διανομές Linux…
Matlab: Rubik’s Cube Simulator and Solver
Get files and more info from here (free mathworks account is required).
This program allows you to generate a randomly scramble cube of arbitrary dimension which can then be manipulated manually or solved by the computer. You can also input your own state using a webcam (3x3x3), or simply enter the colors of each facelet (2,3,4x.x.).
There are several built-in solving mechanisms available:
– God’s Algorithm for the 2x2x2: this is the optimal solution for the given state (in half-turn metric).
– Thistlethwaite 45 (T45) for the 3x3x3: this algorithm will always find a solution of 45 moves or less, averaging at 31.
– Layer-by-Layer (Beginners’) Solution: this is the method commonly used by beginners to solve the cube. More intuitive than T45, but also more extensive and less effective.
– 423T45 for the 4x4x4 (read 4 to 3, T45): this algorithm brings the cube to a state which can be handled like it was a 3x3x3 cube. When this is achieved, T45 can be applied to solve it (~180 moves on avg).
– Inverse Scramble for all cubes: it is like cheating, but when the scramble is known, each cube can be solved by inversing the sequence.
All of the above methods (with exception of the inverse scramble, which is trivial) are explained extensively in the included PDF. The PDF also contains a vast theoretical description of the cube.
Matlab: Face Detection and Tracking Using CAMShift
Object detection and tracking are important in many computer vision applications including activity recognition, automotive safety, and surveillance. In this example, you will develop a simple face tracking system by dividing the tracking problem into three separate problems:
- Detect a face to track
- Identify facial features to track
- Track the face.
Matlab: Image Segmentation Tutorial (“BlobsDemo”)
Get files from here (free mathworks account is required).
Perfect for the beginner, this demo illustrates simple object detection (segmentation, feature extraction), measurement, and filtering. Requires the Image Processing Toolbox (IPT) because it demonstrates some functions supplied by that toolbox, plus it uses the “coins” demo image supplied with that toolbox. If you have the IPT (you can check by typing ver on the command line), you should be able to run this demo code simply by copying and pasting this code into a new editor window, and then clicking the green “run” triangle on the toolbar.
First finds all the objects, then filters results to pick out objects of certain sizes. The basic concepts of thresholding, labeling, and regionprops are demonstrated with a simple example.
It’s a good tutorial for those users new to MATLAB’s image processing capabilities to learn on, before they go on to more sophisticated algorithms.
