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The Computer Vision Workshop

The Computer Vision Workshop

By : Hafsa Asad , Vishwesh Ravi Shrimali , Nikhil Singh
4.3 (6)
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The Computer Vision Workshop

The Computer Vision Workshop

4.3 (6)
By: Hafsa Asad , Vishwesh Ravi Shrimali , Nikhil Singh

Overview of this book

Computer Vision (CV) has become an important aspect of AI technology. From driverless cars to medical diagnostics and monitoring the health of crops to fraud detection in banking, computer vision is used across all domains to automate tasks. The Computer Vision Workshop will help you understand how computers master the art of processing digital images and videos to mimic human activities. Starting with an introduction to the OpenCV library, you'll learn how to write your first script using basic image processing operations. You'll then get to grips with essential image and video processing techniques such as histograms, contours, and face processing. As you progress, you'll become familiar with advanced computer vision and deep learning concepts, such as object detection, tracking, and recognition, and finally shift your focus from 2D to 3D visualization. This CV course will enable you to experiment with camera calibration and explore both passive and active canonical 3D reconstruction methods. By the end of this book, you'll have developed the practical skills necessary for building powerful applications to solve computer vision problems.
Table of Contents (10 chapters)
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Model Conversion Using Model Optimizer

We have already discussed in detail why we need OpenVINO's optimization power. Now, let's understand how Model Optimizer helps with that. Generally speaking, OpenVINO's optimization takes place in two steps: one at the Model Optimizer stage and one at the Inference Engine stage. We will investigate the Inference Engine in the next section, but for now, let's focus on Model Optimizer.

Model Optimizer carries out two main operations – quantization and fusion.

Let's start by understanding quantization. As you may recall, we talked about precision in Exercise 8.01, Downloading the Pedestrian and Vehicle Detection Model, when we said that FP16 means that the number representation is going to use 16 bits of memory and will be represented in floating-point format. Quantization is closely related to that. To understand it, let's look at an example. Let's say we want to represent a number, 0.578156, in a...

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