15 Questions
What are l+, x+, l-, and x- relevant for in feature detection?
They are relevant for identifying shifts with the smallest and largest change in the feature scoring function.
What is the feature scoring function in feature detection?
The feature scoring function aims for E(u,v) to be large for small shifts in all directions.
How can points with large response be identified in feature detection?
By choosing those points where l- is a local maximum as features.
What is the formula for the rewritten feature scoring function in terms of u, v, and H?
$E(u, v) = u^T H v$
How are x+ and x- defined in the context of feature detection?
x+ is the direction of the largest increase in the feature scoring function, while x- is the direction of the smallest increase.
Corner Detection: A significant change in all directions indicates a ______ region
corner
In corner detection, we should easily recognize a point by looking through a small ______
window
Feature Detection: Summing up the squared differences defines an SSD 'error' of E(u,v) by comparing each pixel before and after by considering the ______
window
In image gradients, a 'flat' region exhibits no change in ______ directions
all
Edge Region: No change along the ______ direction
edge
The Taylor Series expansion of I helps in making a first-order approximation when the motion (u,v) is ______
small
The shorthand for the partial derivative of I with respect to x is ______
Ix
Plugging the Taylor Series expansion into the formula on the previous slide helps in understanding the behavior of points in the context of ______ detection
feature
In feature detection, shifting a window in any direction should result in a large change in ______
intensity
Corner regions exhibit a significant change in ______ directions
all
Learn about corner detection and matching with topics including motivating feature detection, Harris Corner Detector Theory, and matching with patches as features. Understand the process of building a panorama through aligning images and finding corresponding pairs of feature points.
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