Image processing is not only limited to image compression. Its essentially a signal processing in which input is some kind of image. Output of image processing can be anything from a different or related image or specific parameters or features of input image.
For example, Image can be processed to identify the objects in it. Objects in an image can be identified by calculating image's gradient in x and y direction.
Gradient is essentially a derivative of an image in both direction. It represents the directional change in intensity or color in an image.
Dx = F(x+1) - F(x) , Dy = F(y+1) - F(y)
Since, Gradient is a directional change in the intensity, Its value is typically vary high on the edges as compared to any other region. Thus gradient helps in identifying the edges of objects in an image.
In the following picture, intensity gradient has been calculated. It is clearly visible that gradients are more prominent at the edges then at other points. This helps us to extract the various parameters/objects from the image.
Gradient of an image finds its applications in various image processing fields like pedestrian crossing, vehicle's number plate recognition, contrast enhancement in televisions etc.
For example, Image can be processed to identify the objects in it. Objects in an image can be identified by calculating image's gradient in x and y direction.
Gradient is essentially a derivative of an image in both direction. It represents the directional change in intensity or color in an image.
Dx = F(x+1) - F(x) , Dy = F(y+1) - F(y)
Since, Gradient is a directional change in the intensity, Its value is typically vary high on the edges as compared to any other region. Thus gradient helps in identifying the edges of objects in an image.
In the following picture, intensity gradient has been calculated. It is clearly visible that gradients are more prominent at the edges then at other points. This helps us to extract the various parameters/objects from the image.
Gradient of an image finds its applications in various image processing fields like pedestrian crossing, vehicle's number plate recognition, contrast enhancement in televisions etc.
