Computer-Vision

Generative models are a class of machine learning that learn a representation of the data trained on and they model the data itself. Ideally, generative models should satisfy the following key requirements in a real environment: High quality samples refers to those samples that captures the underlying patterns and structures present in the data making them indistinguishable from human observers. Fast Sampling is about the efficiency of image generation and the computational overhead that can cause generative models. Mode Coverage/Diversity points out how the model is able to generate a full range of mods and diverse patterns present in the training data Fig. 1. The Generative Learning Trilemma ...

Traditional computer vision techniques involve methods and algorithms that do not rely on deep learning or neural networks. Instead, these approaches are not data-driven and they use classical approaches to process and analyze images. So, in this post, we’ll explore three thresholding techniques! Thresholding When the task is to distinguish the background from the foreground, thresholding provides a straightforward solution. We will use this image as an example. ...