Neural Network: Filtering VS Convolution in Image Processing

— An Ultimate Guide for You to Understand —

In order to successfully get useful information while handling the convolutional neural network, it is necessarily to apply certain methods and techniques in order to gain an output data ready and efficient.

In image processing, filtering is a mathematical operation designed to extract certain features from the image; it relies on enhancing filters (or kernels) on the region covered to get the info we are looking for. Filters are like a small Matrice with pre-defined values that go all over the images to get us or highlight us the needed information such as edges or shapes. Filtering result us a new image that we call feature map, with the feature needed.

Coming to convolution, typically it is an also mathematical operation that goes with the filtering, it basically involves multiplying the values from the filter and the ones from local regions, and then summing it up these products. It goes all over the image like the filtering, and it is quite useful specially it is detecting edges or blurring.

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