Applications of Advanced Graphics Animation Digital Image processing Morphing Fractals Ray Tracing
Animation Art of creating moving images Widely used in education, training, entertainment, advertising. Formal definition of computer animation is ” generation of time sequence of visually changing objects in their shapes, colour, orientation, translation, rotation etc.”
Computer animation is divided into two broad processes. To design and creation of animation sequence To display animation sequence In general ,an animation sequence is designed with the following steps: 1.Storyboard Layout 2.Object Definitions 3.Key-frame specifications 4.Generations of in-between frames
The storyboard is an outline of the action. An object definition is given for each participant in the action. A key frame is a detailed drawing of the scene at a certain time in the animation sequence. In-betweens are the intermediate frames. Approximately 1500 frames are needed for a clip of one minute of film.
In practice, the animation is created by three traditional ways. Key frame system- key frame is defined Parameterized system- parameter may be degree of freedom, motion path - motion path can be linear or non-linear or random linear- car on a straight road non-linear- motion of a cricket ball random- flying butterfly Scripting system – user writes some functions to control animation.
Methods to define motion Direct motion specification - clock Goal specified motion – cricket ball Kinematics motion specification- motion can be specified by position, velocity, acceleration
Digital Image Processing It is composed of finite number of elements. Each of these elements consists of location information and pixel details. The digital images may be fun images, medical images, biometric images, satellite images etc.
Sometimes they contain some noise or some clarity is needed. For doing this, some process is performed on the images, which is known as Digital Image. Processing. The main objectives of digital image processing are: 1) To improve pictorial information 2) To analyze pictorial information 3) To optimize image storage for efficient representation and transmission.
Image processing is divided intothree major categories1) Image compression2) Image enhancement and restoration3) Image extraction
1) Image compression Size of digital image is very large Difficult to transmit due to large size For example, animation series or video. For this reason, we need to reduce the size of the image- it is known as image compression
2) Image enhancement andrestoration In this method, the images are processed for data error detection, removal of noise and distortion occurs while scanning the picture or recording the scene It is used to restore the original scene in the form of image.
There are two types of noises that can be present in an image. 1) Random 2) Non-random Some vital information regarding random pixels is missing as may happen during scanning of a picture is known as random noise. When some strips or lines disturb the image, it is known as non-random type of noise.
In the noise removal, filteringtechniques are used. Linear Filter Multidimensional filter Median filter Laplacian filter Gaussian filter Mean filter Kuwahara filter Sigma filter
Other image enhancement tasks are colour and brightness adjustment etc. Ex. Black and white image Negative image Image with glowing edge Blurred image Image with different background
Image Extraction Classify the image into various parts. Segmentation and clustering are the methods to analyze a picture by the classification.
Applications of digital imageprocessing Medical science Entertainment Remote sensing Robotics Biometrics
Fractals The objects which are neither 2-dimensional or 3- dimensional. They are in some fractional dimensions like 1.5 and so on. Ex. Clouds, curve, trees, mountains etc.
Fractals are divided into threegroups 1) Exact self-similar fractals Exactly identical at different scales 2) Quasi self-similar fractals Nearly identical at different scales 3) Statistical self-similar fractals Have numerical or statistical measures across the scale.
Ray tracing Finds the colour information of each and every pixel in the scene by tracing a ray of light that is reflected back to the viewer’s eye from the pixel position.