In this course, you will build on the skills learned in Introduction to Image Processing to work through common complications such as noise. You’ll use spatial filters to deal with different types of artifacts. You’ll learn new approaches to segmentation such as edge detection and clustering. You’ll also analyze regions of interest and calculate properties such as size, orientation, and location.

By the end of this course, you’ll be able to separate and analyze regions in your own images. You’ll apply your skills to segment an MRI image of a brain to separate different tissues. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your image processing tasks. You will be provided with free access to MATLAB for the duration of the course to complete your work. To be successful in this course you should have a background in basic math and some exposure to MATLAB. If you want to familiarize yourself with MATLAB check out the free, two-hour MATLAB Onramp. Experience with image processing is not required.

In this course, you will build on the skills acquired in Image Segmentation, Filtering, and Region Analysis to explore large sets of images and video files. It’s impractical to manually inspect results in large data sets. Automating image processing allows you to do your work more efficiently.

At the end of this course, you’ll apply all the skills learned in this specialization to a final project. You’ll take the role of an engineer being asked to monitor traffic on a busy road. You’ll detect cars from a noisy video and analyze the results. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your image processing tasks. You will be provided with free access to MATLAB for the duration of the course to complete your work. To be successful in this course you should have a background in basic math and some exposure to MATLAB. If you want to familiarize yourself with MATLAB check out the free, two-hour MATLAB Onramp. Experience with image processing is not required.

In the first course of the Computer Vision for Engineering and Science specialization, you’ll be introduced to computer vision. You'll learn and use the most common algorithms for feature detection, extraction, and matching to align satellite images and stitch images together to create a single image of a larger scene.

Features are used in applications like motion estimation, object tracking, and machine learning. You’ll use features to estimate geometric transformations between images and perform image registration. Registration is important whenever you need to compare images of the same scene taken at different times or combine images acquired from different scientific instruments, as is common with hyperspectral and medical images. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your computer vision tasks. You will be provided free access to MATLAB for the course duration to complete your work. To be successful in this course, it will help to have some prior image processing experience. If you are new to image data, it’s recommended to first complete the Image Processing for Engineering and Science specialization.

In the second course of the Computer Vision for Engineering and Science specialization, you will perform two of the most common computer vision tasks: classifying images and detecting objects. You will apply the entire machine learning workflow, from preparing your data to evaluating your results. By the end of this course, you’ll train machine learning models to classify images of street signs and detect material defects.

You will use MATLAB throughout this course.  MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your computer vision tasks.  You will be provided free access to MATLAB for the course duration to complete your work. To be successful in this specialization, it will help to have some prior image processing experience.  If you are new to image data, it’s recommended to first complete the Image Processing for Engineering and Science specialization.

In the third and final course of the Computer Vision for Engineering and Science specialization, you will learn to track objects and detect motion in videos. Tracking objects and detecting motion are difficult tasks but are required for applications as varied as microbiology and autonomous systems. To track objects, you first need to detect them. You’ll use pre-trained deep neural networks to perform object detection. You’ll also use optical flow to detect motion and use the results to detect moving objects.

At the end of this course, you’ll apply all the skills learned in this specialization to a final project. You’ll take the role of an engineer being asked to track cars on a busy highway with the added challenge of counting each vehicle and its direction. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science and provides the capabilities you need to accomplish your computer vision tasks. You will be provided free access to MATLAB for the course duration to complete your work. To be successful in this specialization, it will help to have some prior image processing experience. If you are new to image data, it’s recommended to first complete the Image Processing for Engineering and Science specialization.

Starting with zero deep learning knowledge, this foundational course will guide you to effectively train cutting-edge models for image classification purposes. From analyzing medical images to recognizing traffic signs, classification is important for many applications. Classification models also serve as the backbone for more complicated object detection models. Through hands-on projects, you will train and evaluate models to classify street signs and identify the letters of American Sign Language. By completing this course, you will develop a strong foundation in deep learning for image analysis and will be equipped with the skills to tackle real-world computer vision challenges.

By the end of this course, you will be able to: • Explain how deep learning networks find image features and make predictions • Retrain common models like GoogLeNet and ResNet for specific applications • Investigate model behavior to identify errors and determine potential fixes • Improve model performance by tuning hyperparameters • Complete the entire deep learning workflow in a final project For the duration of the course, you will have free access to MATLAB, software used by top employers worldwide. The courses draw on the applications using MATLAB, so you spend less time coding and more time applying deep learning concepts.

Detecting and locating objects is one of the most common uses of deep learning for computer vision. Applications include helping autonomous systems navigate complex environments, locating medical conditions like tumors, and identifying ready-to-harvest crops in agriculture. In the course projects, you will apply detection models to real-world scenarios and train a model to detect various parking signs. Completing this course will give you the skills to train detection models for your application.

By the end of this course, you will be able to: • Explain how deep learning networks locate and classify objects in images • Retrain popular YOLO deep learning models for your application • Use a variety of metrics to evaluate prediction results • Visualize results to gain insights into model performance • Improve model performance by adjusting important model parameters • Analyze labeled images to identify and fix potential shortcomings in your data For the duration of the course, you will have free access to MATLAB, software used by top employers worldwide. The courses draw on the applications using MATLAB, so you spend less time coding and more time applying deep learning concepts.

Visual inspection and medical imaging are two applications that aim to find anything unusual in images. In this course, you’ll train and calibrate specialized models known as anomaly detectors to identify defects. You’ll also use advanced techniques to overcome common data challenges with deep learning. AI-assisted labeling is a technique to auto-label images, saving time and money when you have tens of thousands of images. If you have too few images, you’ll generate synthetic training images using data augmentation for situations where acquiring more data is expensive or impossible.

By the end of this course, you will be able to: • Train anomaly detection models • Generate synthetic training images using data augmentation • Use AI-assisted annotation to label images and video files • Import models from 3rd party tools like PyTorch • Describe approaches to using your model outside of MATLAB For the duration of the course, you will have free access to MATLAB, software used by top employers worldwide. The courses draw on the applications using MATLAB, so you spend less time coding and more time applying deep learning concepts.