Computer vision is a field of machine learning that processes images to solve real visual problems. In this article, I’m going to introduce you to some very useful computer vision projects and tasks that will help you boost your portfolio.
Humans have no problem identifying objects and the environment around them. However, it is not so easy for computers to identify and distinguish between the various models, visuals, images and objects in the environment. This is where computer vision comes in handy.
In the simplest terms, computer vision is the discipline in a broad field of Machine Learning that teaches machines to see. Its objective is to extract meaning from pixels.
Face Detection using Webcam
Face Detection using Open Source Computer Vision Library in python(OpenCV), we will use OpenCV in the detection of our face using a webcam.
OpenCV is built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. You can see the full article on Face Detection using webcam here.
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OpenCV for Machine Learning
OpenCV is a bunch of stuff mainly dealing with processing images and videos on your computer. This is a standard library for Computer Vision for Python tasks.
Perhaps this is the fundamental question that comes to mind. Well, that means “Open Source Computer Vision Library” launched by some avid coders in 1999 to incorporate image processing into a wide variety of coding languages. OpenCV is not limited to Python only, it also supports C and C++. You can learn OpenCV for Machine Learning from here.
Using the HOG features of Machine Learning, we can build up a simple facial detection algorithm with any Image processing estimator, here we will use a linear support vector machine, and it’s steps are as follows:
- Obtain a set of image thumbnails of faces to constitute “positive” training samples.
- Obtain a set of image thumbnails of nonfaces to constitute “negative” training samples.
- Extract HOG features from these training samples.
- Train a linear SVM classifier on these samples.
- For an “unknown” image, pass a sliding window across the image, using the model to evaluate whether that window contains a face or not.
- If detections overlap, combine them into a single window.
You can learn the implementation of Machine Learning for Image Processing from here.
After the above computer vision projects, you will learn a lot about computer vision and its implementation with Machine Learning. From the above computer vision projects and tasks, you will understand how you can implement machine learning for computer vision in real-world tasks. Now let’s have a look at some Advanced Computer Vision Projects.
Image Recognition with Machine Learning
Humans take no effort to distinguish a dog, cat, or flying saucer. But this process is quite difficult for a computer to emulate: it only looks easy because God designs our brains incredibly well to recognize images.
One common example of image recognition with machine learning is optical character recognition. In this machine learning task based on computer vision, I will take you through building an Image Recognition model with Machine Learning using PyTorch. See the full project here.
Being a practitioner in Machine Learning, you must have gone through an image classification, where the goal is to assign a label or a class to the input image. Now, suppose you want to get where the object is present inside the image, the shape of the object, or what pixel represents what object.
In such a case, you have to play with the segment of the image, from which I mean to say to give a label to each pixel of the image. The goal of Image Segmentation is to train a Neural Network which can return a pixel-wise mask of the image. See the full project here.
Face Landmarks Detection
Have you ever thought about how Snapchat manages to apply amazing filters according to your face? It has been programmed to detect some marks on your face to project a filter according to those marks. In Machine Learning those marks are known as Face Landmarks. In this task, you will learn the implementation of Machine Learning on how you can detect face Landmarks with Machine Learning. See the full project here.
Data Augmentation is a technique in Deep Learning which helps in adding value to our base dataset by adding the gathered information from various sources to improve the quality of data of an organisation. Data Augmentation is one of the most important processes that makes the data very much informational.
Improving the data is very important for every business because data is considered as the oil of business. Data Augmentation can be applied to any form of the dataset, which mainly includes text, images, and audio. See the implementation of data augmentation for images. See the full project here.
Image Features Extraction
A local image characteristic is a tiny patch in the image that is indifferent to the image scaling, rotation, and lighting change. It’s like the tip of a tower or the corner of a window in the image below. In this computer vision task, I will walk you through the task of image features extraction with Machine Learning. See the full project here.
Colour Recognition with Python
In this computer vision project, I’ll walk you through a colour recognition task with Python. This process is also known as colour detection. We are going to create a basic application that will help us detect colours in an image. The program will also return the RGB values of the colours, which is useful. See the full project here.
I hope you liked this article on Computer Vision projects for machine learning. Feel free to ask your valuable questions.