1.1.1 Context
Non-photorealistic rendering (NPR) techniques started emerging about a decade ago. Non-photo-realistic is just what the name suggests - images created not with the goal to reproduce original imagery as close as possible, but to produce dierent representations of a model, something that might even be considered artistic. The rst goals of these techniques were to somehow imitate real life painting eects articially. Since the rst trials many dierent methods appeared, both in 2 and 3D, for many dierent purposes. Yet the rst goal has always remained: produce visually pleasant representations of model images or create such images articially with dedicated tools.
Non-photorealistic rendering has been used since then for many dierent purposes: to create painting-like eects on images, to produce cartoon-like renderings, to create dierent types of sketches, drawings, or even to aid the creation of illustrations from ordinary images (i.e. in medical imaging).
Stroke based rendering (SBR) methods form a special subset of non-photorealistic rendering
1.1.1. Context 2
techniques. In SBR the painterly eects are produced by simulating real life brush stroke to an extent, and the painting process itself, to produce images by a so called articial painting process.
Simulated brushes can be of a wide variety from simple geometrical shapes to multilevel height maps or complex curves.
As we will show later, there are many ways of generating painting-like images from ordinary pictures. The rough outline of these algorithms are as follows. We take an image that we wish to create a painterly rendered abstraction from, a model, and a blank image that we call canvas, on which we will create the painterly rendered image. The rendered image will consist of a series of brush strokes that will be placed on the canvas. The strokes can have dierent size, shape, orientation and color. These properties are the so-called stroke parameters which are the data needed to be known if we would wish to reconstruct the painted image later from the stroke-series it consists of. Strokes can be very versatile, and almost every researcher came up with their own version of the idea of a stroke. There are methods which use simple shapes (e.g. rectangles, lines, etc.) as strokes, others that use long curves with constant or varying width, then there are ones who use dierent templates or texture patterns as stroke models. The methods dier at most in the way they place the strokes on the canvas in order to obtain a plausible artistical representation of the model image.
There are manual, semi-automatic and fully automatic painting techniques. A manual paint-ing method is a paintpaint-ing application, fully controlled by the user. Stroke sizes, positions, color, i.e.
everything is determined by the user. In manual painting approaches sometimes external haptic devices are also used to aid the artist in creating articial paintings. In semi-automatic painting techniques some parameters (for example stroke curve shapes or thickness) are determined by the user, but color/texture, lling and placing is done automatically. In automatic painting techniques the whole painting process is automatically controlled by the algorithm (sometimes parameters need to be set before starting the rendering process).
SBR algorithms can again be classied by a dierent property, into two main groups: greedy and optimization algorithms. Greedy algorithms usually consist of a method which tries to place strokes which locally or globally seem to improve the image on the canvas, with no or minimal consideration of stroke neighborhoods, stroke density or other parameters. Optimization algorithms usually contain some kind of built-in intelligence in the stroke placement and/or acceptance steps, which help the painting process converge faster or to converge more to some global minimum rather than to a local dead-end.
Usually the output of the rendering process is a series of stroke parameters, which can be
1.1.2. Goals 3
stored and later be used to reconstruct the painted images (and possibly also for other purposes as we will show later on).
1.1.2 Goals
Our work in the eld of NPR/SBR has been concentrated around 2D, model based SBR techniques.
The goals we pursued were to achieve automatic painterly eects on model images and videos, and at the same time to investigate dierent aspects of SBR like optimizations, coding, compression, scalability, etc.
Thus in our work and research in 2D SBR the source is a usual 2D image which is used as a model for the painting process. All data needed for the painterly transformation is obtained from this model image using dierent image analysis techniques. Our additions to the world of 2D SBR has been various, the most important probably having been a combined point of view not just from computer graphics but also from image processing and computer vision. This gave the opportunity to see dierent aspects of SBR besides the generation of painterly eects.
Also, we have to state that our goal has not been to produce painting or painter-aiding applications in which some real painter can create paintings by using simulated digital counterparts of real painters' tools, as in [5]. Our methods and techniques provide means to create painterly eects automatically, by using model images and stroke templates, without no user interaction.
Basically we have continuously targeted areas which have not been fully covered by graphics research in the eld of 2D SBR, mostly by combining techniques from image processing, pattern recognition and image coding. Thus, while we have been producing painterly eect generation methods, we always have also been dealing with storing, compressing and managing such produced images.
This thesis work presents most of our previous research in the eld and also some new achievements that are being published or being prepared for publication.
1.1.3 Motivation
The author's motivation in this work comes from the challenging, and widely unresolved tasks the 2D NPR/SBR techniques contain. As we will show in the next section, there are many NPR, and among them many 2D SBR methods. Some of them target specic application areas, like pencil or charcoal drawings, others try to simulate watercolor painting, there are ones that mimic oil paintings, of course there are many drawing-aiding applications which create an interactive