By Anson Antony S
The Scale-Invariant Feature Transform (SIFT) is one of the most widely used algorithms for feature detection in computer vision. It plays a crucial role in image matching, object recognition, and panorama stitching by detecting and describing local features that are invariant to scale, rotation, and illumination. One of SIFT’s key strengths is its robustness to occlusion, making it suitable for real-world applications where objects may not be fully visible in every frame.
An interest point refers to a region in an image that can be robustly detected and is distinct from its surroundings. Raw images are hard to match because of variations in size, orientation, and lighting. For example, comparing two images with different camera angles or lighting conditions can be challenging. Interest points are vital in making image matching more effective, as they represent consistent and identifiable regions of an object.
While lines and edges are important features in images, they are often not considered the best candidates for feature detection. This is because edges may vary greatly with changes in illumination or viewing angle, and matching lines can result in many ambiguous points. Instead, interest points focus on more distinct regions like corners or textured areas, which provide better robustness in image matching.
Blobs are regions in an image that have higher contrast compared to their surroundings. Detecting blobs is a critical step in identifying key interest points, typically done using the Gaussian Filter.
A Gaussian filter smooths the image, reducing noise and small details, allowing for multi-scale analysis.