Search for tag: "informatics"

Informatics 1: Data & Analysis 2014/15 Lecture 18: Hypothesis Testing and Correlation This lecture looks at multidimensional datasets and the idea of correlation: where values from two sets of…

Informatics 1: Data & Analysis 2013/14 Lecture 17: Data Scales and Summary Statistics This lecture gives a general overview of statistics and their role in analysing quantities of data. Most of…

Informatics 1: Data & Analysis 2014/15 Lecture 16: Vector Spaces for Information Retrieval This lecture presents various techniques to support effective information retrieval: term-frequency…

Informatics 1: Data & Analysis 2012/13 Lecture 15: Information Retrieval Unstructured data; specifying the Information Retrieval problem and evaluating solutions.

Informatics 1: Data & Analysis 2016/17 Lecture 9: Trees and XML From the strict rectangles of structured data to the more generous triangles of semistructured data. This lecture gives an…

Informatics 1: Data & Analysis 2014/15 Lecture 13: Annotation of Corpora Looking at some of the annotations used to mark up text corpora, such as parts-of-speech (POS) and sentence structure.…

Informatics 1: Data & Analysis 2016/17 Lecture 10: Structuring XML Defining and using schemas to capture particular kinds of XML document. Link: Web page for this lecture

Informatics 1: Data & Analysis 2016/17 Lecture 12: Corpora In computational linguistics and in theoretical linguistics a corpus is a body of written or spoken text used for study of a…

Informatics 1: Data & Analysis 2016/17 Lecture 11: Navigating XML using XPath How to query the information held in an XML document: also feedback on the feedback, what's behind Path…

Professor Alberado Pardo presents his research as part of the Digital Education series of seminars hosted Professor Dragan Gasevic of the School of Informatics.

We start with an introduction to the set of videos, summarizing the six prototype computer vision systems.

We review coordinate geometry reference frame transformations and how to represent these.

We introduce a simple polycurve model that will be used to represent flat shapes and later extended for 3D shapes.

With the model to data correspondences, we can estimate the 2D transformation that maps the model onto the data.

Because the matching and pose estimation phases can produce spurious matches and poses, we introduce a verification step that eliminates bad hypotheses.

We next turn the boundary edge points into a more compact set of straight line segments.