In non-photorealistic rendering research

people found that many arts' textures or backgrounds are composed of many small elements with repetitive distribution. Many researchers tried to use procedure based approaches to simulate those pattern effects

but those methods could only generate limited kinds of patterns even after a tedious complex and difficult parameters' adjustment. Some researchers considered sample-based methods and have been proved to be very effective in non-photorealistic effects simulation. Vector texture pattern means the pattern which consists of limited types of elements which are arranged with certain rules or features. Those patterns are very common in our daily life

both from natural objects and manmade patterns. Vector texture pattern synthesis can obtain the rules of the elements' distribution from a given sample pattern and generate a large pattern which shares the same rules with the sample pattern. The element in those texture patterns can represent any independent visual information geometry or texture. The study of automatic synthesis method of these patterns has become an important topic

as these patterns not only plays a significant role in hand-painting simulation but also be used in virtual scene generation such as the generation of a forest. Furthermore

automatic synthesis of pattern means we use the computer

a machine

not a human being to analyze and understand the rules of the elements distribution

this involves in some artificial intelligence which also makes the study of vector texture patterns synthesis a more meaningful research topic. In this paper

a new sample based vector texture synthesis method is proposed. In our method

we put forward a Neighborhood Histogram for the comparison and matching of element's neighborhood. The neighborhood space of an element is divided into small grids by a bunch of radials and a series of concentric circles centered at this element. A histogram can be generated by counting the number of elements positioned in each grid. The histogram represents the neighborhood information of current element. It is used to search the matching element in sample pattern through histogram comparison

and hence synthesize the destination pattern with the matching element. The method includes three stages: sample acquisition

sample analysis and pattern synthesis. A sample pattern can be obtained by reading in a pattern file with specified format or being constructed interactively. We also need to obtain the elements information from the sample pattern

including position

types etc. The main task of sample analysis is acquiring the neighborhood information between elements in the sample pattern and building histogram for each element. We first estimate the radius of the element's neighborhood region according to the space arrangement among elements in sample pattern;then we will construct each element's neighborhood and build the histogram of the neighborhood. In the pattern synthesis process

first

we select an element in random

put it in the center of the synthetic area and copy its neighborhood elements to the corresponding positions in the synthetic area. The first element is named as the center element

and the center element and its neighborhood elements form an init pattern of our synthesis pattern. Next

we will extend the synthesis pattern step by step. In each extension

we choose the element nearest to the center element as an extending-element;then search the matching element in sample pattern whose histogram is the most similar to extending one's;copy the neighborhood elements of the matching element to the synthetic area. Repeating the process to extend the synthesis pattern

we can get the final result.We employ our method on regular

near-regular and random patterns respectively. We find that our method can accurately grasp the rules of sample pattern distribution and get good synthesis results. Most of the previous methods use Delaunay triangulation to get the relationship of elements in sample pattern

which means the relatively position of two close elements are defined in an accurate and fixed form. In synthesis process

as there is no element in sample pattern which shares the exact same neighborhood information with the one in the synthesis pattern

this usually causes difference accumulated and affects the final synthesis pattern. Some realization will slow down the speed of the program. Our method divides the neighborhood of an element into small grids and counts the number of elements positioned in each grid which used as a parameter in synthesis process. We use a certain range in relative orientation not an accurate relative orientation to calculate the neighborhood information of the element

making it some fuzzy in boundary

which can reduce the difference accumulation in synthesis process. Our method doesn't need complex elements relationship analysis and frequently comparison of elements positions

achieving higher performance. Our method can guarantee the local similarity between sample pattern and synthesis pattern

but as we didn't take the overall elements' density into consideration

this may lead to imbalance in element proportion. How to add this parameter into the synthesis process to achieve similarity between sample pattern and the synthesis pattern on the whole level is our first priority at present. For further extension

we could take vector field into consideration and generate effects with controllable gradual changes. In addition

our method doesn't involve in elements' parameters such as orientation

color

size and so on. Taking these factors into consideration and achieving good results with more expressive force is another extension we are trying to accomplish.