What is integral in image processing?

An Integral image is where each pixel represents the cumulative sum of a corresponding input pixel with all pixels above and left of the input pixel. This concept was introduced by Viola & Jones and is also known as Summed Area Table.

What is SURF in image processing?

In computer vision, speeded up robust features (SURF) is a patented local feature detector and descriptor. It can be used for tasks such as object recognition, image registration, classification, or 3D reconstruction. Its feature descriptor is based on the sum of the Haar wavelet response around the point of interest.

How does SURF algorithm work?

The steps of SURF algorithm contain three sections: interest points detecting, interest points describing and interest points matching. Interest points detecting uses a detector based on Hessian matrix, its stability and repeatability outperforms the existing state-of-the-art, e.g. a detector based on Harris.

What is an image matching technique?

Image matching techniques are the techniques used to find existence of a pattern within a source image. Matching methods can be classified in two categories i.e. Area based matching techniques and feature based matching techniques.

Why do we need integral images?

Integral images facilitate summation of pixels and can be performed in constant time, regardless of the neighborhood size. The following figure illustrates the summation of a subregion of an image, you can use the corresponding region of its integral image.

What benefits does an integral image bring to the Viola Jones technique?

Because Haar-like features are actually rectangular, and the integral image process allows us to find a feature within an image very easily as we already know the sum value of a particular square and to find the difference between two rectangles in the regular image, we just need to subtract two squares in the integral …

What is Matlab surf algorithm?

Object Recognition using Speeded-Up Robust Features (SURF) is composed of three steps: feature extraction, feature description, and feature matching. This example performs feature extraction, which is the first step of the SURF algorithm. The algorithm used here is based on the OpenSURF library implementation.

What is sift image processing?

The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999. SIFT keypoints of objects are first extracted from a set of reference images and stored in a database.

How matching of images takes place for any given image?

A common approach to image matching consists of detecting a set of interest points each associated with image descriptors from image data. Once the features and their descriptors have been extracted from two or more images, the next step is to establish some preliminary feature matches between these images.

What is image alignment?

Image alignment is the process of matching one image called template (let’s denote it as T) with another image, I (see the above figure). There are many applications for image alignment, such as tracking objects on video, motion analysis, and many other tasks of computer vision.