Digital image processing is a broader field that encompasses various techniques and algorithms, including but not limited to machine learning. Image processing techniques can be both traditional (non-machine learning based) and machine learning-based. Machine learning algorithms can be used for image processing tasks, particularly when there is a need for automated feature extraction and pattern recognition. One popular machine learning algorithm used for image processing is the Convolutional Neural Network (CNN). CNNs are specifically designed for processing images and have shown remarkable performance in tasks such as image classification, object detection, and image segmentation. CNNs work by learning hierarchical representations of image data through multiple layers of convolutional and pooling operations. These networks are trained on a large dataset of labeled images, enabling them to learn and recognize complex patterns and features in images. Other machine learning algorithms, such as Support Vector Machines (SVMs), Random Forests, and Deep Belief Networks, can also be used for image processing tasks. However, CNNs have gained significant popularity and achieved state-of-the-art performance in many image-related applications. It's important to note that not all image processing tasks require machine learning algorithms. Traditional image processing techniques, such as filtering, edge detection, morphological operations, and histogram equalization, can be effective for tasks like image enhancement, noise reduction, and basic feature extraction. In summary, digital image processing can involve both traditional techniques and machine learning algorithms. Machine learning algorithms, particularly CNNs, have shown great promise in various image processing tasks due to their ability to automatically learn and extract features from images.

Dr Joy Bakshi thank you for your contribution to the discussion

In machine learning, image processing tasks are frequently completed using Kernel Methods. These operations are a collection of tools for finding and analyzing photograph patterns. Image processing is used in pattern recognition to identify the items in an image, and machine learning is then used to train the system to recognize changes in patterns. Pattern recognition is utilized in computer assisted diagnosis, handwriting recognition, image identification, character recognition etc. It follows deep learning algorithms where the machine is first trained with the specific features of human faces, such as the shape of the face, the distance between the eyes, etc. After teaching the machine these human face features, it will start to accept all objects in an image that resemble a human face. Digital image processing is the use of a digital computer to process digital images through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.Image recognition, in the context of machine vision, is the ability of software to identify objects, places, people, writing and actions in digital images. Computers can use machine vision technologies in combination with a camera and artificial intelligence (AI) software to achieve image recognition.CNN stands for Convolutional Neural Network and is a type of deep learning algorithm used for analyzing and processing images. Preprocessing allows us to eliminate unwanted distortions and improve specific qualities that are essential for the application we are working on. Those characteristics could change depending on the application. An image must be preprocessed in order for software to function correctly and produce the desired results. The analog image processing is applied on analog signals and it processes only two-dimensional signals. The digital image processing is applied to digital signals that work on analyzing and manipulating the images. Analog signal is time-varying signals so the images formed under analog image processing get varied. Feature mapping using the scale-invariant feature transform (SIFT) algorithm. Decision Tree algorithm in machine learning is one of the most popular algorithm in use today; this is a supervised learning algorithm that is used for classifying problems.

Dr Rana Hamza Shakil thank you for your contribution to the discussion

The leading architecture used for image recognition and detection tasks is that of convolutional neural networks (CNNs). Convolutional neural networks consist of several layers, each of them perceiving small parts of an image. The widely used algorithms in this context include denoising, region growing, edge detection, etc. The contrast equalization is often performed in image-processing and contrast limited adaptive histogram equalization (CLAHE) is a very popular method as a preprocessing step to do it. Feature mapping using the scale-invariant feature transform (SIFT) algorithm. The accuracies of the four ML algorithms, we just explored for our CIFAR-10 dataset, can be summarized using the graph shown above. Random Forest Classifier shows the best performance with 47% accuracy followed by KNN with 34% accuracy, NB with 30% accuracy, and Decision Tree with 27% accuracy. The k-Nearest Neighbor classifier is by far the most simple machine learning and image classification algorithm. In fact, it's so simple that it doesn't actually “learn” anything. Image classifiers rely on Convolutional Neural Networks (CNNs) to process an image. CNNs are a special form of neural network with a specific architecture of layers. The four types of CNN layers are the convolutional layer, ReLU layer, pooling layer, and fully connected layer. The main advantage of SVM is that it can be used for both classification and regression problems. SVM draws a decision boundary which is a hyperplane between any two classes in order to separate them or classify them. SVM also used in Object Detection and image classification. Image processing is used in pattern recognition to identify the items in an image, and machine learning is then used to train the system to recognize changes in patterns. Pattern recognition is utilized in computer assisted diagnosis, handwriting recognition, image identification, character recognition etc. In machine learning, image processing tasks are frequently completed using Kernel Methods. These operations are a collection of tools for finding and analyzing photograph patterns. Digital image processing is the use of a digital computer to process digital images through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.