Category: Computer vision

  • Transfer learning and fine-tuning

    Transfer learning and fine-tuning

    Transfer learning and fine-tuning In this tutorial, you will learn how to classify images of cats and dogs by using transfer learning from a pre-trained network. A pre-trained model is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. You either use the pretrained model as is…

  • What is VGGNet?

    What is VGG? VGG stands for Visual Geometry Group; it is a standard deep Convolutional Neural Network (CNN) architecture with multiple layers. The “deep” refers to the number of layers with VGG-16 or VGG-19 consisting of 16 and 19 convolutional layers. The VGG architecture is the basis of ground-breaking object recognition models. Developed as a…

  • Regularization in CNNs

    Regularization in CNNs

    The Overfitting problemfor CNNs Techniques to address Overfitting 1. Data Augmentation 2. Batch Normalization 3. Spatial Dropout In Spatial Dropout, whole feature maps themselves are randomly dropped. Dropping a feature map, means making all the cells of that feature map 0, which would be as good as not using it.Let’s suppose we obtained 8 feature…

  • Introduction to Computer Vision

    Introduction to Computer Vision

    CNNs are a special type of neural network designed to work with image data. CNNs use convolutional layers – hidden layers which perform convolution operations. They have some different characteristics to ANNs: The first part in this architecture is the convolutional layer followed by the pooling layer and the second part is the fully connected…

  • The Emergence of Convolution

    The Emergence of Convolution

    Feature Extraction Conventional Feature Extraction Techniques The two most historically-important, manual feature extraction techniques have been: HOG SIFT HOG mainly focuses on thestructure and shape of anobject. In SIFT, image content is convertedinto local feature coordinates that arenot affected by rotation, scaling, orother image manipulations HOG is different from onlydetecting edges, as it alsoidentifies the…

  • Color Pixel Theory and Image Represention

    Color Pixel Theory and Image Represention

    2. Representation of Images 3. PIL (Python Imaging Library) Basics of Images Images have three major components: Size This represents the height andwidth of an image. It is usuallymeasured by number of pixels. Color Space This represents the differentpossible color spaces, likeGrayscale, RGB, HSV. The imageof the duck on the right isrepresented in RGB color…

  • What is Computer Vision?

    What is Computer Vision?

    Introduction to Computer Vision Use Cases for Computer Vision Autonomous Vehicles Retail Healthcare Smartphone Camera Applications Augmented Reality & Virtual Reality Banking, Financial Services & Insurance

  • Working with Images using OpenCV

    Working with Images using OpenCV

    Working with Images using OpenCV OpenCV stands for Open Source Computer Vision. In this notebook we will learn to use the OpenCV library. You can find out more about the OpenCV library here. OpenCV Here we will cover the following topics: We would encourage you to first go through the functions and operations discussed below, and then…