Preprocess The Image

#1

Sir, what is meant by Preprocess the Image and Why we do it?

#2

Hey @Management718,

Pre-processing is a common name for operations with images at the lowest level of abstraction — both input and output are intensity images. These iconic images are of the same kind as the original data captured by the sensor, with an intensity image usually represented by a matrix of image function values (brightness ) The aim of pre-processing is an improvement of the image data that suppresses unwilling distortions or enhances some image features important for further processing, although geometric transformations of images (e.g. rotation, scaling, and translation) are also classified among pre-processing methods. Since here similar techniques are used.

#3

Sir what is meant by image features in the image? Which features are extracted in CNN regnet? Sir please explain in simple words?

#4

Features are the information extracted from images in terms of numerical values that are difficult to understand and correlate by human. Suppose we consider the image as data the information extracted from the data is known as features. Generally, features extracted from an image are of much more lower dimension than the original image. The reduction in dimentionality reduces the overheads of processing the bunch of images.

Basically there are two types of features are extracted from the images based on the application. They are local and global features. Features are sometimes referred to as descriptors. Global descriptors are generally used in image retrieval, object detection and classification, while the local descriptors are used for object recognition/identification. There is a large difference between detection and identification. Detection is finding the existence of something/object (Finding whether an object is exist in image/video) where as Recognition is finding the identity (Recognizing a person/object) of an object.

Global features describe the image as a whole to the generalize the entire object where as the local features describe the image patches (key points in the image) of an object. Global features include contour representations, shape descriptors, and texture features and local features represents the texture in an image patch. Shape Matrices, Invariant Moments (Hu, Zerinke), Histogram Oriented Gradients (HOG) and Co-HOG are some examples of global descriptors. SIFT, SURF, LBP, BRISK, MSER and FREAK are some examples of local descriptors.