Human Intelligence, 1988 “It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.”
at an earlier date – only possible to use as augmented reality. Designed for performance, not accuracy. Source: https://play.google.com/store/apps/ details?id=com.google.android.apps.unveil
images on webpages based on colour, size, and basic content analysis – not object recognition. Does not provide contextual information – relies on image sources. Because the database contains more than just artwork, presents a lot of irrelevant images for a query image.
For each recognised painting, use an SQL database to provide contextual information: title of the work, artist name, painting description, year of publication, and artist year of birth Provide a user interface for running on Windows 7 and up, built in Visual C#
Visual C# (.NET) Based on SQL, widely accepted industry standard Good performance, high scalability Source: http://blogs.msdn.com/b/jerrynixon/archive/ 2012/02/26/sql-express-v-localdb-v-sql-compact- edition.aspx
Seeing Robot Rover (1980) by Hans Moravec Robot discovers its own path through a maze First attempts failed until using stereo vision Stereo vision requires identifying the same objects across multiple images, to get a sense of depth based on distance between cameras Moravec found that the most useful points to identify across images were corners
Lowe Algorithm named SIFT (Scale Invariant Feature Transform) Based on properties of human vision Two-stage process: detector and descriptor Detector finds the most interesting areas of an image Descriptor creates a multidimensional vector based on the interest point and its surroundings Results show good invariance to scale, orientation, illumination, and changes to perspective
Tuytelaars Based on SIFT Faster than SIFT Shows good invariance to to scale, rotation, illumination, and changes to perspective Provides ability to improve accuracy at cost to performance: SURF-128
Lowe and Muja FLANN: Fast Library for Approximate Nearest Neighbors Used for comparing features of database images to the query image SURF features are represented as multidimensional vectors The more dimensions, the more values in each that the search algorithm needs to consider FLANN performs preliminary analysis on a data set to apply the most effective search algorithm
the Tate Modern gallery Photographs deliberately demonstrate changes to perspective For testing, each is transformed to show invariance to scale and rotation Database contains images and details of the 11 artworks photographed
large increase in processing time for rotated images was not expected Processing Time in Milliseconds 0 1750 3500 5250 7000 Transformation 25% 50% 75% 100% BW C CC
in order to distinguish them from others in the database This indicates that feature extraction is not the most appropriate matching method for these kinds of images Josef Albers’ Homage to the Square set is an example of simple images that do not match well with feature extraction
70% target. For 100%, black and white, and rotated image sets, accuracy fell below 70% target. Simple artworks brought down average accuracy. Uses SQL database to provide contextual information for matched paintings: title, artist name, description, publication year, and artist year of birth. Visual C# GUI for Windows.
with the findings: SURF-128 not as invariant to orientation as claimed. More accurate with images closer in size to those in the database. Not the most appropriate solution for paintings with few identifying features.