What is Digital Image processing?

An image can be defined as a two-dimensional function, f(x,y) # x and y are spatial (plane) coordinates # The function value f at any pair of coordinates (x,y) is called the intensity, or gray scale of the image # When x, y and the values of f are all finite, discrete quantities, we call the image a digital image # A digital image is composed of a finite number of elements called picture (image) elements, pels, and pixels

DIP refers to processing digital images by means of a digital computer. The definition of image processing to be a discipline in which both input and output of a process are images is too limiting Close relationship to computer vision (close to AI), which aims to emulate human vision

APPLICATIONS Hollywood film makers use novel morphing technologies to generate special effects Disney uses morphing to speed up the production of cartoons

Face morphing

Procedures adopted Pre-processing: When getting an image containing human faces, it is always better to do some pre-processing such like removing the noisy backgrounds, clipping to get a proper facial image, and scaling the image to a reasonable size.

Noise reduction

Features finding: Features finding: 4 major feature points, namely the two eyes, and the two endpoints of the mouth. Within the scope of this project, we developed an eye-finding algorithm that successfully detects eyes at 84% rate. 4 major feature points, namely the two eyes, and the two endpoints of the mouth. Within the scope of this project, we developed an eye-finding algorithm that successfully detects eyes at 84% rate.

Eye finding

Mouth finding

After finding the eyes, we can specify the mouth as the red-most region below the eyes. The red-ness function is given by Redness = ( R > G * 1.2 ? ) * ( R > Rth ? ) * { R / (G + epsilon ) } where Rth is a threshold, and epsilon is a small number for avoiding division by zero. where Rth is a threshold, and epsilon is a small number for avoiding division by zero.

Image Partitioning # The edges of the face also need to be carefully considered in the morphing algorithm. # The edges of the face also need to be carefully considered in the morphing algorithm. # If the face edges do not match well in the morphing process, the morphed image will look strange on the face edges. # We generate 6 more feature points around the face edge, which are the intersection points of the extension line of the first 4 facial feature points with the face edges. # We generate 6 more feature points around the face edge, which are the intersection points of the extension line of the first 4 facial feature points with the face edges.

Demonstration of morphing process 1.Original images scaled to same size

2. Performing coordinate transformations on the partitioned images to match the feature points of these two images.

3. Cross-dissolving the two images to generate a new image.

Recent evolutions (1)Morphing between the faces of different people.

(2) Morphing between different images of the same person

Conclusion An automatic face morphing algorithm is proposed. The algorithm consists of a feature finder followed by a face-morphed that utilizes affine and bilinear coordinate transforms. Feature extraction is the key technique toward building entirely automatic face morphing algorithms.

eyes are the most important features of human faces. Therefore, in this project we developed an eye finder based on the idea that eyes are, generally speaking, more complicated than the rest of the face. We demonstrated that a hybrid image of two human faces can be generated by morphing, and the hybrid face we generated indeed resembles each of the two "parent" faces.

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