CONVOLUTION: KERNALS FOR FEATURE EXTRACTION IN IMAGE PROCESSING
An image as simple as 200x200 is too much information for a Nueral network or a computer to understand. The machine learning algorithm like logic regression cannot process the 200x200 information of image, even if it does, the results may not be favourable at all. The accuracy of Nueral Network totally depend upon the input we've given it. So in machine learning the NN's preprocess the input and extract necessary information from the input. Something advance such as CONVOLUTION NUERAL NETWORK(CNN) perform feature extraction. In order to extract feature from an image we need convolution in image processing. Convolution is process in signal processing which is used to combine 2 signals. More about convolution in here . Here the convolution in computer vision is for feature extraction . We extract important details from image like Edges,noise removal,etc... The extraction of important data can be achieved by using a KERNAL . The kernal method is used for sharping,embrose,smoo...