Creating a custom object detector was a challenge, but not now. There are many approaches for handling object detection. Of all, Haarcascades and HOG+SVM are very popular and best known for their performance. Though Haarcascades which were introduced by Viola and Jones are good in achieving decent accuracy, HOG+SVM proved to outperform the Haarcascades implementation. Here in this post we are going to build a object detector using HOG+SVM model. The output from our detector is something similar as shown below.
Object Tracking has been a challenging problem in the field of computer vision and lots of new techniques are being invented. In this post we build an end-to-end object tracker using two simple techniques to track an object in a video stream. The outcome of the project might looks like as shown below.
In the last post we discussed how to extract the sudoku region from the captured frame of a live stream and then we applied perspective transform to the extracted region and then we slide a window through each cell and recognized the digits in the cells. Now in this post we discuss how to solve the sudoku puzzle using Backtracking algorithm. There are several approaches to solve the sudoku puzzle. For example, you can find some of them in this paper.
Though there are several approaches to solve it, let’s stick to the traditional backtracking algorithm to solve the puzzle. For our understanding, just have a look at how the sudoku is presented below.
I was very much excited to solve the Sudoku puzzles that appeared in daily newspapers during childhood. In fact it’s a lot of fun solving them and even we (my friends) challenge ourselves to solve them first. By then we don’t know programming and other stuff, so we were not aware of finding the solution with ease. But when I was learning Data Structures and Algorithms long back I got encountered with the famous Backtracking algorithm and yes, solving a sudoku puzzle is one among the challenges that can be solved easily using that algorithm. Continue reading