Page 347 - AI Ver 3.0 class 10_Flipbook
P. 347

After completing the activity, we conclude that,

                 In image processing, a wide variety of features can be extracted from an image, such as blobs, edges, and corners.
                 These features play a crucial role in enabling various tasks, such as object detection, image segmentation, and
                 pattern recognition. The analysis derived from these features depends on the specific application at hand.
                 A fundamental question that often arises is: which features are most effective for a given task? As demonstrated
                 in the previous activity, corners are particularly valuable features because they are unique and can be identified at
                 specific locations in an image. In contrast, edges, while also useful, are spread across a line and tend to look the
                 same along their length, making them less distinctive for precise localisation.
                 This information highlights that corners are excellent features to extract from
                 an  image,  as  they  provide  precise  and  reliable  information.  Edges,  while
                 slightly less distinctive, are still beneficial and can be used to complement
                 corner-based  analysis.  Additionally,  combining  these  features  with  other
                 attributes, such as texture and intensity variations, can further enhance the
                 accuracy and effectiveness of image processing tasks.

                 Let  us  understand  the  concept  of  image  feature  with  the  help  of  another
                 activity.
                 Observe the images provided and apply the idea of identifying good features in an image.
                 In the image above, how can we accurately determine the exact location of every patch? Here’s how different
                 patches behave:

                    • Blue Patch: This is a flat area, making it challenging to locate and track. No matter where you move the blue
                   patch within the image, it appears the same, offering no distinct features for identification.
                    • Black Patch: This represents an edge in the image. If you move the black patch along the edge (parallel to it),
                   the appearance remains unchanged, making it difficult to determine its precise position.
                    • Red Patch: This corresponds to a corner in the image. Unlike the other patches, wherever you move the red
                   patch, it looks different, making it unique and easily identifiable.

                 Corners, like the red patch, are considered good features in an image because they are distinct and can be
                 accurately tracked or located.

                                Reboot



                       1.  In image processing, which type of feature is most commonly used for tracking objects in an image?




                       2.   What could be the benefit of combining features like texture and intensity with edges and corners in
                          image processing?












                                                                                    Computer Vision (Practical)  345
   342   343   344   345   346   347   348   349   350   351   352