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# Tag: detection

### #011 TF YOLO V3 Object Detection in TensorFlow 2.0

YOU ONLY LOOK ONCE Highlights: Prior to Yolo majority of approaches for object detection tried to adapt the classifiers for the purpose of detection. In YOLO, an object detection has been framed as a regression problem to spatially separated bounding boxes and associated class probabilities. In this post we will learn about the YOLO Object Detection system, and how to implement such a system in TensorFlow 2.0. About Yolo:Our unified architecture is extremely fast. Our…

### OpenCV #013 Harris Corner Detector – Theory

Highlights: In this post we will learn about Harris Corner Detector and how can we use this method to detect corners. We will give a brief overview how this method works, but we’ll not go so seriously with mathematics. We promise 🙂 What is Harris Corner Detector? In many computer vision and machine learning applications we need some feature points which we will track or which will assist us to compare and detect objects or…

### OpenCV #010 Circle Detection Using Hough Transform

Highlights: In this post we will learn about analysing a given image to find circles detected in that image. Tutorial Overview: Intro Detecting Circles with Hough Hough Transform for Circles Code 1. Intro In the previous post, we saw how we can detect and find lines on images using Hough Transform. Now let’s move to something just a little bit more complicated, circles. Let’s start with the equation of a circle:  ( x_{i}-a )^{2}+(…

### OpenCV #013 Harris Corner Detector – Experiment

Highlights: In this post we will continue working on Harris Corner Detector. In the previous post we presented the basic idea behind this algorithm, and here we will wrap this up and show how this method works in OpenCV. Tutorial Overview: Interpreting the Second Moment Matrix Interpreting the Eigenvalues Harris Corner Response Function Harris Detector Algorithm Code 1. Interpreting the Second Moment Matrix Let’s first consider the case, where the gradient at every point in…

### #028 CNN Anchor Boxes

Anchor Boxes As we can see from our previous posts, object detection is quite challenging. This is the final challenge that we are going to explain. Then, we will develop a holistic YOLO algorithm. One scenario that we may encounter in practice is that several objects of interest are present in the same grid cell. This is shown in the figure below. In this case, we can use the idea of $$Anchor\enspace boxes$$ to…