Recently, there has been a huge rise in the implementation of artificial intelligence solutions, with new deep learning architectures being built and deployed across various industries. Deep learning works primarily because of the vast amount of input data on which the deep neural net is trained. Hence, having a good labeled training dataset marks the first step in developing a highly accurate AI solution. Preparing labeled training datasets for Computer Vision problems is a painstaking task that involves image processing, manipulation, and finally image labeling. Thus, while dealing with hundreds and thousands of images, programmatic image manipulation and processing stands out as an efficient option for AI dataset creators. So getting familiar with image processing libraries is a convenient first step in creating a custom AI Solution.

Release 1.4 of the magick package introduces a new feature called image convolution that was requested by Thomas L. Pedersen. In this post we explain what this is all about. The new image_convolve() function applies a kernel over the image. Kernel convolution means that each pixel value is recalculated using the weighted neighborhood sum defined in the kernel matrix. Many operations in magick such as blurring, sharpening, and edge detection are actually special cases of image convolution.