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Image fusion is a process where two or more images are combined to create a single, more informative or visually enhanced image. This technique is used in various fields like photography, remote sensing, medical imaging, and computer vision.

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The goal of image fusion is to enhance the overall quality of the final image by combining information from multiple sources while retaining the most relevant details.

Feature-Level Fusion: Instead of combining pixel values, this method focuses on merging the features extracted from images. It's often used in computer vision for object recognition and tracking.

Libraries and Frameworks for Machine Learning Image Processing:

OpenCV-Python is a library of Python bindings designed to solve computer vision problems.

Huge library of image processing algorithms

Open Source + Great Community

Works on both images and videos

Java API Extension

Works with GPUs

Cross-Platform

Supervised Learning: KNN is a supervised learning algorithm, meaning it learns from labeled data to make predictions. 

Instance-Based Learning: KNN is also considered an instance-based or lazy learning algorithm because it stores the entire training dataset and performs computations only when making predictions. 

Distance Metric: KNN relies on a distance metric to measure the similarity or distance between data points. 

Finding Nearest Neighbors:

For a new, unlabeled data point, KNN identifies the "k" nearest neighbors in the training data based on the chosen distance metric.

Classification: In classification tasks, the new data point is assigned to the class that is most common among its "k" nearest neighbors.

Regression: In regression tasks, the predicted value for the new data point is the average of the values of its "k" nearest neighbors. 

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