Do you think, when you train a Convolutional Neural Network (CNN) to classify between images it is exactly understanding the image as we humans perceive? It’s difficult to answer, as for most of the times Deep learning models are often considered to be a black box. We feed in the data and then we get the output. Whatever happens in between this flow is very difficult to debug. Though we get the accurate predictions, it may not be true that they are intelligent enough to perceive the same way as we do. Continue reading
Hello, all! I hope you got excited by the title itself. What if I tell you that building a face recognition system is not so difficult? Yes, it is, and of course very exciting. Let’s build a complete face recognition system which enables you to enroll a new candidate into the system and perform recognition with higher accuracy!
Ever wondered, how does the Google reverse image search engine works which take in an image and returns you the most similar images in a fraction of a second? How does the Pinterest let you search the visually similar images of the selected objects? Sounds interesting? Do you want to understand and build similar kind of a system? If yes then you are at the right place. Continue reading
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.