A Look at Image Segmentation using CNNs

Mohit Jain

Image segmentation is the task in which we assign a label to pixels (all or some in the image) instead of just one label for the whole image. As a result, image segmentation is also categorized as a dense prediction task. Unlike detection using rectangular bounding boxes, segmentation provides pixel accurate locations of objects in an image. Therefore, image segmentation plays a very important role in medical analysis, object detection in satellite images, iris recognition, autonomous vehicles, and many more tasks.

With the advancements in deep learning methods, image segmentation has greatly improved in the last few years; in terms of both accuracy and speed. We can now generate segmentations of an image within a fraction of a second and still be very accurate and precise.

The Goal of this Post

Through this post, we’ll cover the intuition behind some of the main techniques and architectures used in image segmentation…

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A Gentle Introduction to Markov Chain Monte Carlo (MCMC)

The Clever Machine

Applying probabilistic models to data usually involves integrating a complex, multi-dimensional probability distribution. For example, calculating the expectation/mean of a model distribution involves such an integration. Many (most) times, these integrals are not calculable due to the high dimensionality of the distribution or because there is no closed-form expression for the integral available using calculus. Markov Chain Monte Carlo (MCMC) is a method that allows one to approximate complex integrals using stochastic sampling routines. As MCMC’s name indicates, the method is composed of two components, the Markov chain and Monte Carlo integration.

Monte Carlo integration is a powerful technique that exploits stochastic sampling of the distribution in question in order to approximate the difficult integration. However, in order to use Monte Carlo integration it is necessary to be able to sample from the probability distribution in question, which may be difficult or impossible to do directly. This…

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