An Introduction to Point Set Registration using Iterative Closest Point and Trimmed Iterative Closest Point with Implementation

Photo by Ivan Sanford on Unsplash

What is Point Set Registration?

Point set (or cloud) registration¹ is a widely used technique in the field of computer vision, pattern recognition, robotics and image processing. It finds extensive application in many fields like medical image processing, intelligent or autonomous vehicles , robotic manipulation, SLAM, panorama stitching to name a few².

The main purpose of point set registration is to find correspondences and estimate the transformation (rotation and translation) between two or more point sets. …


Autoencoders are one of the simplest and most popular neural networks. There are many versions of them but the underlining principle and essence of the network remains the same. In this article, we first touch upon a vanilla autoncoder — it’s architecture, equations and implementation. We follow this up with an interesting application of autoencoders in the field of remote sensing for feature extraction.

Photo by NASA on Unsplash

Understanding Autoencoders

An autoencoder is a neural network that consists of an input layer, one or more hidden layer(s) and an output layer, pretty much like any other neural network that you may have heard of or worked…


In Part 1 of this series, I explained the whats and hows of Autoencoders. While there are ample examples listing the use of autoencoders for de-noising or dimensionality reduction, in this article I would like to demonstrate how it can also be used in applications like remote-sensing to extract features from satellite images.

In this example, I used a vanilla autoencoder defined in the first part of this article to extract features from fully Polarimetric SAR (PolSAR) image taken over Oberpfaffenhofen, Wessling, Germany. The Oberpfaffenhofen PolSAR image of size 6640 x 1390 pixels has a resolution of 1.5 m per…


An autoencoder is a neural network that consists of an input layer, one or more hidden layer(s) and an output layer, pretty much like any other neural network that you may have heard of or worked with.

But there are some very specific characteristics that an autoencoder possesses which makes it unique and they are:

  1. The input and output layers of an autoencoder always have the same dimension. The hidden layer can be any dimension(preferably lesser than the input and output layers) but the input and the output dimensions should always match! Why is it so? …

There’s a chance (almost a certainty) that if you’re working in Robotics, Computer Vision or Machine Learning, you are required to work on Linux. In fact irrespective of the field you’re working in, Linux always plays a very important role in the life of a software developer.

There’s always been a tussle between which OS is best but when it comes to engineering and software development, Linux wins hands down and the fact that most engineering tools are based on or developed for Linux OS just proves it further.

But, having worked on Windows systems all my life, I still…


We are surrounded by Autonomous Systems. Whether it is self-driving cars or home automation system, autonomous systems is the essence of new age technology.

One of the key factors while developing an autonomous system is the exchange of data between different modules. This data exchange takes place over a sensor network and since these networks are wireless, they form Wireless Sensor Networks (WSN).

WSN consists of a collection of individual nodes that interact with their environment to sense or control physical parameters and they collaborate with each other to fulfill the task. …

Anupama Rajkumar

Student for life. An aspiring researcher in CV and ML. Here to share all I’ve done or learnt. https://www.linkedin.com/in/anupama-rajkumar-910a222a/

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