What is meant by Haar like features?

Haar-like features are digital image features used in object recognition. A Haar-like feature considers adjacent rectangular regions at a specific location in a detection window, sums up the pixel intensities in each region and calculates the difference between these sums.

How is Haar features calculated?

The haar calculation is done by finding out the difference of the average of the pixel values at the darker region and the average of the pixel values at the lighter region. If the difference is close to 1, then there is an edge detected by the haar feature.

How many types of Haar like features exists?

There are 8 different Haar features, which can make 6 systems that would contain 4, 4, 5, 6, 7, or 8 of these Haar features. The two methods used for computing thresholds are the average of means and the optimal threshold methods. The implemented systems have been trained using a handpicked database.

What does Haar cascade stand for?

Haar Cascade is a machine learning-based approach where a lot of positive and negative images are used to train the classifier. Positive images – These images contain the images which we want our classifier to identify. Negative Images – Images of everything else, which do not contain the object we want to detect.

What does Haar stand for?

HAAR High Altitude Acute Response Miscellaneous » Unclassified Rate it:
HAAR Huntsville Area Association of Realtors Business » Real Estate Rate it:
HAAR HATTIESBURG Area Association of REALTORS Community » Associations — and more… Rate it:
HAAR Hate African American Recognition Regional » African Rate it:

What do we call Haar in English?

: a cold wet sea fog.

What is a Haar classifier?

Haar Cascade classifier is an effective object detection approach which was proposed by Paul Viola and Michael Jones in their paper, “Rapid Object Detection using a Boosted Cascade of Simple Features” in 2001. Based on the training it is then used to detect the objects in the other images.

What causes haar?

Causes. Haar is typically formed over the sea and is blown to the land by the wind. This commonly occurs when warmer moist air moves over the relatively cooler North Sea causing the moisture in the air to condense, forming haar.

How are Haar like features used in face detection?

Haar-like feature. Therefore a common Haar feature for face detection is a set of two adjacent rectangles that lie above the eye and the cheek region. The position of these rectangles is defined relative to a detection window that acts like a bounding box to the target object (the face in this case).

Why do we need Haar like features in a classifier?

Because such a Haar-like feature is only a weak learner or classifier (its detection quality is slightly better than random guessing) a large number of Haar-like features are necessary to describe an object with sufficient accuracy.

How many lookups are needed for a Haar like feature?

Each Haar-like feature may need more than four lookups, depending on how it was defined. Viola and Jones’s 2-rectangle features need six lookups, 3-rectangle features need eight lookups, and 4-rectangle features need nine lookups. Lienhart and Maydt introduced the concept of a tilted (45°) Haar-like feature.

Who are the authors of the Haar like feature?

A publication by Papageorgiou et al. discussed working with an alternate feature set based on Haar wavelets instead of the usual image intensities. Paul Viola and Michael Jones adapted the idea of using Haar wavelets and developed the so-called Haar-like features.