Home » Image Processing Table of Contents Adversarial Learning with Keras and TensorFlow (Part 3): Exploring Adversarial Attacks Using Neural Structured Learning (NSL) Introduction to Advanced Adversarial Techniques in Machine Learning Harnessing NSL for Robust Model Training: Insights from Part 2 Deep Dive into Adversarial Attack Formulations: PGD and FGSM Explored Building an End-to-End Adversarial Application with Keras and TensorFlow Adversarial Attacks: Unraveling the Intricacies of Crafting Deceptive Samples Recap of Adversarial Sample Generation: The Foundation of Adversarial ..read more

In this tutorial, we’ll learn about a step-by-step implementation and utilization of OpenCV’s Contour Approximation. When I first chanced upon the concept of Contour Approximation, the first question that hit me was: Why? Throughout my journey in Machine Learning and its related fields, I was always taught that Data is everything. Data is currency. The more you have of it, the more you are likely to succeed. Figure 1 aptly describes the scenario. Figure 1: Data is the New World Currency. Hence, I didn’t quite understand the concept of approximating the data points of a curve. Wouldn’t that m ..read more

In this tutorial, you will learn about image gradients and how to compute Sobel gradients and Scharr gradients using OpenCV’s cv2.Sobel function. Image gradients are a fundamental building block of many computer vision and image processing routines. We use gradients for detecting edges in images, which allows us to find contours and outlines of objects in images We use them as inputs for quantifying images through feature extraction — in fact, highly successful and well-known image descriptors such as Histogram of Oriented Gradients and SIFT are built upon image gradient representations Gra ..read more

In this tutorial, you will learn how to use OpenCV and the cv2.threshold function to apply basic thresholding and Otsu thresholding. Thresholding is one of the most common (and basic) segmentation techniques in computer vision and it allows us to separate the foreground (i.e., the objects that we are interested in) from the background of the image. Thresholding comes in three forms: We have simple thresholding where we manually supply parameters to segment the image — this works extremely well in controlled lighting conditions where we can ensure high contrast between the foreground and bac ..read more

In this tutorial, you will learn about applying morphological operations with OpenCV. The morphological operations we’ll be covering include: Erosion Dilation Opening Closing Morphological gradient Black hat Top hat (also called “White hat”) These image processing operations are applied to grayscale or binary images and are used for preprocessing for OCR algorithms, detecting barcodes, detecting license plates, and more. And sometimes a clever use of morphological operations can allow you to avoid more complicated (and computationally expensive) machine learning and deep learning algorithm ..read more

A picture is worth a thousand words. — English idiom We’ve heard this adage countless times in our lives. It simply means that a complex idea can be conveyed in a single image. Whether examining the line chart of our stock portfolio investments, looking at the spread of an upcoming football game, or simply taking in the art and brush strokes of a painting master, we are constantly ingesting visual content, interpreting the meaning, and storing the knowledge for later use. However, for computers, interpreting the contents of an image is less trivial — all our computer sees is a big matrix of ..read more

In this tutorial you will learn how to detect low contrast images using OpenCV and scikit-image. Whenever I teach the fundamentals of computer vision and image processing to students eager to learn, one of the first things I teach is: “It’s far easier to write code for images captured in controlled lighting conditions than in dynamic conditions with no guarantees.” If you are able to control the environment and, most importantly, the lighting when you capture an image, the easier it will be to write code to process the image. With controlled lighting conditions you’re able to hard-code par ..read more

In this tutorial, you will learn how to use OpenCV and the cv2.imread function to: Load an input image from disk Determine the image’s width, height, and number of channels Display the loaded image to our screen Write the image back out to disk as a different image filetype By the end of this guide, you will have a good understanding of how to load images from disk with OpenCV. To learn how to load an image from disk using OpenCV and cv2.imread, just keep reading. Looking for the source code to this post? Jump Right To The Downloads Section OpenCV load image (cv2.imread) In the first par ..read more

In this tutorial, we’ll learn about a step-by-step implementation and utilization of OpenCV’s Contour Approximation. When I first chanced upon the concept of Contour Approximation, the first question that hit me was: Why? Throughout my journey in Machine Learning and its related fields, I was always taught that Data is everything. Data is currency. The more you have of it, the more you are likely to succeed. Figure 1 aptly describes the scenario. Figure 1: Data is the New World Currency. Hence, I didn’t quite understand the concept of approximating the data points of a curve. Wouldn’t that m ..read more

In this tutorial, you will learn about image gradients and how to compute Sobel gradients and Scharr gradients using OpenCV’s cv2.Sobel function. Image gradients are a fundamental building block of many computer vision and image processing routines. We use gradients for detecting edges in images, which allows us to find contours and outlines of objects in images We use them as inputs for quantifying images through feature extraction — in fact, highly successful and well-known image descriptors such as Histogram of Oriented Gradients and SIFT are built upon image gradient representations Gra ..read more