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Showing posts with the label Computer Vision

Build Your First Nueral Network: Basic Image Classification Using Keras

Image classification is one of the most important problem to solve in machine learning. It can provide vital solutions to a variety of computer vision problems, such as face recognition, character recognition, object avoidance in autonomous vehicles and many others. Convolutional Neural Network (CNN), since its inception has been used for image classification and other computer vision problems. It is called convolutional neural network because of convolutional layer. Keras is a high level library which provides an easy way to get started with machine learning and neural networks. It will be used here to implement CNN to classify handwritten digits of MNIST dataset.
Image Classification is  a process to determine which of the given classes an input image belongs to. CNNs represent a huge breakthrough in image classification. In most cases, CNN outperforms other image classification methods and provides near to human-level accuracy. CNN models do not simply spit the class name the inp…

7 Awesome Examples of Computer Vision

Though early experiments in computer vision started from the 1950s and it was initially put to use to distinguish between handwritten and typed text from the 1970s. Today the applications for computer vision have increased exponentially. In this article, we will share with you some of the recent implementation trends of computer vision. What's Computer Vision (CV)? Computer vision is the use of computers which process visual data and then make conclusions from it or gain understanding about the situation and the surroundings. One of the factors behind the growth of computer vision is the amount of data today which we use subsequently to train and improve computer vision machines.
We have a bulk amount of visual data in the form of images and videos produced by built-in cameras of our phones alone. However, while visual data can include photos and videos, it can also get information from other sources and detectors. Besides with the massive amount of visual information (over 3 bill…