Harris operator / Harris Corner
Detector
• Harris Corner Detector uses local maxima in rotationally
invariantscalar
measures.
• This is derived from the auto-correlation matrix to locate keypoints for sparse
features matching.
• It is using a Gaussian weighting window instead of square patches, which
makes the detector response insensitive to in-plane image rotations.
• The minimum eigenvalue λ0
is not only
quantity that can be used to find keypoints
λmin is a variant of the “Harris operator” for feature (f) detection
• The trace is the sum of the diagonals, i.e., trace(H) = h11 + h22
• Very similar to λ- but less expensive (due to no square root)
The Harris operator
Harris
operator
Feature
Detection
Feature
Detection
Harris detector example
The circle sizes and colors indicate the scale at which each interest point was
detected.
Sample image Harris response
Feature detector properties
Adaptive non-maximal suppression (ANMS)
• Most of the feature detectors simply look for local maxima in the
interest function.
• It leads to an uneven distribution of feature points across the image.
e.g., points will be denser in regions of higher contrast.
• To mitigate this problem, only detect features
that are both local maxima and whose
response value is significantly (10%)
greater
than all of its neighbors within a radius r.
Feature detector properties
• The upper two images show the strongest 250 and 500 interest points.
• Lower two images show the interest points selected with ANMS along with the
corresponding suppression radius r.
• In ANMS, features have a much more uniform spatial distribution across the
image.
Qualitative
comparison of
selecting the top n
features using
ANMS
Feature detector properties
Measuring repeatability
• Measuring repeatability is the ratio between the number of keypoints
simultaneously present in all the images of the series (repeated keypoints)
over the total number of detections.
• It is used to assess keypoint detection performance i.e. feature detector
performance.
• Measures the detector’s ability to identify the same features (i.e., repeated
detections) despite variations in the viewing conditions.
Scale invariance
• Scale invariance is a feature of objects that do not change by variying the
scales of length.
• In image matching, Sufficient features may not exist in images with little
high frequency detail (e.g., clouds).
• So, extract features at a variety of scales, e.g., performing the same
operations at multiple resolutions.
Feature detector properties
Rotational invariance and orientation estimation
• Orientation is estimated by the average gradient within a region around the
keypoint.
• Gradient is computed using the histogram of orientations around the keypoint.
• A dominant orientation is computed by creating a Histogram of all the Gradient
Orientations (weighted by its magnitudes or after thresholding out small
gradients) and then finding the significant peaks in this distribution.
Affine invariance
• The surfaces are considered the same under affine (An affine function is the
composition of a linear function with a translation, so while the linear part fixes
the origin, the translation can map it somewhere else.) transformations, i.e.,
linear
transformations x ↦ Ax + b, including squeezing and shearing.
Feature descriptors
• A feature descriptor is an algorithm which
takes an image and outputs feature descriptors/feature
vectors.
• Feature descriptors encode interesting information of image into a series
of numbers that can be used to differentiate one feature from another.
• After detecting features(keypoints), must match
them, i.e., determine which features come from corresponding
locations in different images.
Some of Feature descriptors are:
1. Bias and gain normalization or multi-scale oriented patches
2. Scale Invariant Feature Transform (SIFT)
3. PCA-SIFT
4. Gradient location-orientation histogram (GLOH)
Bias and gain normalization or multi-scale oriented patches (MOPS) do
not exhibit large amounts of foreshortening such as image stitching. Patch
LOGIN WINDOW
insert into login (meter_no, username, name, password, user) values('"+smeter+"',
'"+susername+"', '"+sname+"', '"+spassword+"', '"+atype+"');
update login set username = '"+susername+"', password = '"+spassword+"', user =
'"+atype+"' where meter_no = '"+smeter+"'";
update login set username = '"+susername+"', password = '"+spassword+"', user =
'"+atype+"' where meter_no = '"+smeter+"'";
CALCULATE BILL DETAILS QUERIES
select * from customer where meter_no = '"+meternumber.getSelectedItem()+"'")
insert into bill values('"+meter+"', '"+month+"', '"+units+"', '"+totalbill+"',
'Not Paid');
