Part 1 on Neural Style Transfer — Neural Style Transfer deals with two sets of images: Content image and Style image. It recreates the content image in the style of the Style image. Here are the required inputs to the model for image style transfer: A Content Image –an image to which we want to transfer style…

Object detection and image segmentation are essential tasks in computer vision, involving the identification and localization of objects within images. To evaluate the accuracy of these techniques, metrics like Jaccard’s Index and Dice Coefficient are used. Jaccard’s Index measures the degree of overlap between bounding boxes or masks, while Dice Coefficient quantifies the similarity between two masks. This article delves into the concepts and applications of these evaluation metrics.

Convolutional Networks for Biomedical Image Segmentation — U-Net is one of the most famous image segmentation architectures. It was proposed in 2015 by Olaf Ronneberger, Philipp Fischer, Thomas Brox (University of Freiburg, Germany). [1] One should read the full paper to really relish the architechture. Following is an outlines treatment, suitable for beginners. An end-to-end segmentation technique…

In semantic segmentation, we associate each pixel of an image with a corresponding class of what is being represented. So, it’s an inference task at pixel level and is also called a dense prediction task. …

A Basic Introduction — Upsampling means increase the dimensions of an image. It is used in decoders of segmentation algorithms, creating an image of a random vector sample with GANs and improving an image’s quality, etc. The up-sampling technique increases the resolution as well as the size of the image. There are two ways…

An Intuitive Introduction — Images Neural networks take in numbers and output numbers. And that is what an image is in computers. It is a series of individual color pixels- each pixel a mix of one/three colors: Greyscale/RGB. There is only one-color channel in a grayscale image. So, a grayscale image is represented as (height…

U-Net Architechture (src: https://arxiv.org/abs/1505.04597) In this article I go through a few DL concepts used in Medical Imaging. This is a very limited piece, and depends highly on (can be considered a limited summary of) this paper: [https://arxiv.org/abs/1702.05747]. …

GANs are used to generate realistic images. A typical GAN model is made of two neural networks which compete (in a way) and hence the term ‘adversarial’. One is them is called Generator, and the other is Discriminator. — GENERATIVE MODELS A discriminative model learns features from the input data and uses the information about them to determine boundaries in observed data and use those (statistical boundaries) to make predictions like the kind of regression or classification. …