Machine Vision

Course aim

To develop skills in applying a series of probabilistic models of images and objects in machine vision systems.

Course content

• Two-dimensional visual geometry: 2d transformation family. The homography. Estimating 2d transformations. Image panoramas.
• Three dimensional image geometry: The projective camera. Camera calibration. Recovering pose to a plane.
• More than one camera: The fundamental and essential matrices. Sparse stereo methods. Rectification. Building 3D models. Shape from sillhouette.
• Vision at a single pixel: background subtraction and color segmentations problems. Parametric, non-parametric and semi-parametric techniques. Fitting models with hidden variables.
• Connecting pixels: Dynamic programming for stereo vision. Markov random fields. MCMC methods. Graph cuts.
• Texture: Texture synthesis, super-resolution and denoising, image inpainting. The epitome of an image.
• Dense Object Recognition: Modelling covariances of pixel regions. Factor analysis and principle components analysis.
• Sparse Object Recognition: Bag of words, latent dirilecht allocation, probabilistic latent semantic analysis.
• Face Recognition: Probabilistic approaches to identity recognition. Face recognition in disparate viewing conditions.
• Shape Analysis: Point distribution models, active shape models, active appearance models.
• Tracking: The Kalman filter, the Condensation algorithm.

Textbook(s)

Nil

Prerequisite(s)

Successful completion of an appropriate Computer Science, Mathematics, or other Physical Science or Engineering undergraduate program with sufficient mathematical and programming content, plus some familiarity with digital imaging and digital image processing.

Corequisite(s)

Nil

Teaching method

Note: These components may or may not be scheduled in every study period. Please refer to the timetable for further details.

Assessment

Continuous assessment, Examination

Fees

EFTSL*: 0.125
Commonwealth Supported program (Band 2)
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Fee-paying program for domestic and international students
International students and students undertaking this course as part of a postgraduate fee paying program must refer to the relevant program home page to determine the cost for undertaking this course.

Non-award enrolment
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* Equivalent Full Time Study Load. Please note: all EFTSL values are published and calculated at ten decimal places. Values are displayed to three decimal places for ease of interpretation.