Part-1 : Why should I study differential equations?

What's the point of studying differential equations? Can we not do away with them? I almost never see an application of those as a computer science or an ECE systems student. Is it like the analog systems, we study for the legacy reason. Can we not do away from differential equations. I used to side … Continue reading Part-1 : Why should I study differential equations? →

Ubuntu Internet Sharing

I have this specific configuration wherein I have a PC (urop) with two network interfaces. One connected to the internet. Second network interface connected to another PC (tx2). I can access internet on urop. I can ssh to tx2 from urop. I want to accomplish the following. To access the internet from tx2. tx2 need not … Continue reading Ubuntu Internet Sharing →

My Trip to Inner Mongolia

I had this wonderful opportunity to visit Inner Mongolia (In China) to experience the majestic grasslands in September of 2017. I traveled to Hulunbuir (呼伦贝尔市). I traveled solo with a car which I drove myself with no knowledge of Chinese and no hotel bookings. Information about this part of the world is quite sparse on the … Continue reading My Trip to Inner Mongolia →

Apps to use in China for traveling

China can be quite overwhelming experience if you arrive here unprepared. I present some practical tips from my experience to make traveling in China easier. I believe this can be quite useful for a person traveling around in China without the knowledge of Chinese language. I highly recommend reading up travel wiki for China for practical … Continue reading Apps to use in China for traveling →

Optimal Triangulation for Tuning Keypoint Co-ordinates

Given a set of correct keypoint matches and a fundamental matrix, to optimize the coordinates of these key points such that they satisfy the epipolar constraint. A point (x,y) on the left image (pose: [I|0]) and (x',y') on the right image (pose: [R|t]). These points are undistorted and in normalized image coordinates. Having known the pose … Continue reading Optimal Triangulation for Tuning Keypoint Co-ordinates →

Image Keypoint Descriptors and Matching

[GitHub] Extracting keypoints from images, usually, corner points etc is usually the first step for geometric methods in computer vision. A typical workflow is: keypoints are extracted from images (SIFT, SURF, ORB etc.). At these keypoints descriptors are extracted (SURF, ORB etc). Usually a 32D vector at each keypoint. The nearest neighbor search is performed to … Continue reading Image Keypoint Descriptors and Matching →

HowTo – Pose Graph Bundle Adjustment

SLAM (Simultaneous Localization and Mapping) is one of the important practical areas in computer vision / robotics / image based modelling community. A SLAM system typically consists of a) odometry estimator (relative pose estimator), b) Bundle adjustment module, c) sensor fusion module (for visual-inertial system), d) mapping module. While there are several excellent resources, refer … Continue reading HowTo – Pose Graph Bundle Adjustment →

Generating randoms from a specified CDF

This post deals with generating random numbers given a CDF (Cumulative distribution function). CDF may be specified as an analytical function or as a table of values. We also assume that we have a source of pseudo-random uniformly distributed numbers. Probability Integral Transform At the core of this issue is the 'Probability Integral Transform'. It states that, … Continue reading Generating randoms from a specified CDF →

NetVLAD – Supervised Place Recognition

Download PPT - Google Docs Vector of locally aggregated descriptors (VLAD) [1] is a simple and popular technique for computing a fingerprint of an image for place recognition. It basically forms say K=64 clusters of SIFT like descriptors (descriptors at SIFT feature points). Then, for every descriptor subtracts it from cluster center and adds it up. … Continue reading NetVLAD – Supervised Place Recognition →

Recurrent Neural Net: Memo

RNN (Recurrent Neural nets) are used to model sequences. Unlike the usual feedforward nets which are stateless in terms on inputs, RNNs have memory. In particular, its inputs are the output of previous step and also new observation in current step. The basic RNN are notoriously hard to train. LSTM (Long short term memory) networks … Continue reading Recurrent Neural Net: Memo →