Search for tag: "artificial intelligence"

Dr Torgny Roxå

Strong and weak ties – changing teaching cultures in higher education in programme Torgny Roxå Lund University, Sweden Learning & Teaching Conference 2018

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From  Paolo Drusi A week ago 0 likes 9 views 0  

Advanced Vision: Course overview

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

From  Joanna Malgorzata Równicka 6 Months ago 1 likes 107 views 0  

Advanced Vision: Coordinate geometry transformation review

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

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 2 views 0  

Advanced Vision: Polycurve models

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

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 1 views 0  

Advanced Vision: 2D pose estimation

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

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 79 views 0  

Advanced Vision: 2D match verification

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

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 64 views 0  

Advanced Vision: Straight line finding

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

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 99 views 0  

Advanced Vision: Part boundary finding

First we find the edge points that surround the part, by analyzing a binary image.

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 124 views 0  

Advanced Vision: 2D model matching

Then we match those straight line segments to a rigid geometric model using the Interpretation Tree model matching algorithm.

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 126 views 0  

Advanced Vision: Adaptive change detection

When the background is changing due to lighting or leave motion, etc, simple subtraction is inadequate.We introduce a non-parametric statistical approach to modeling the background and then detect…

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 102 views 0  

Advanced Vision: Rigid flat part recognition introduction

We introduce a simple flat rigid part recognition system that is based on matching part edges to a geometric model. This will also require estimating the reference frame transformation that maps the…

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 138 views 0  

Advanced Vision: Ball tracking with the Kalman filter

The Kalman filter is applied to the falling ball tracking problem.

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 93 views 0  

Advanced Vision: Ball tracking with Condensation Tracking

We extend the ball tracking example to use Condensation tracking, which allows tracking of the ball through bounces and stopping.

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 77 views 0  

Advanced Vision: Condensation Tracking

When a target can change state, tracking can lose the target. If you have a model of different possible successor states, then the Condensation tracking algorithm can explore these…

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 92 views 0  

Advanced Vision: Example of adaptive change detection

We demonstrate the adaptive change detection algorithm on an indoor scene where the lighting is slowly changing, and where there are compression artifacts in the video.

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 85 views 0  

Advanced Vision: Kalman filter

We introduce the Kalman Filter, which is an important algorithm for estimation of variables given data that arrives periodically. Here it is used to track the ball, predict its position and refine…

From  Joanna Malgorzata Równicka 6 Months ago 0 likes 90 views 0