The Research On Dynamic Panorama

In this paper, we introduce a panorama method to represent dynamic scene: Dynamic panorama. We can enhance the limitation of panorama which can only represent static scene by combining video texture and panorama. The panorama combined with video texture which including dynamic component is called dynamic panorama. The dynamic panorama is the expansion of panorama. It overcomes the weakness of panorama's expressive force. It not only remains the advantage of the whole view browse of panorama, but also adds the dynamic feature to the virtual scene. It enhances the reality of user's browsing by simulating more scenes in the world. We will discuss how to mosaics some photos taken at a fixture to a panorama in detail; how to build video texture from film with dynamic objects in it; how to develop a browser tool for dynamic panorama. Panorama is an efficient way to realize the virtual reality. It can show users a high qualified virtual environment, without making models of the real objects. Therefore it is widely used in the internet. There are procedures to make a panorama: projection,image mosaics,and converse projection. We present a novel algorithms of image mosaics, which is based on PSO and multiresolution algorithm and has high mosaics reliability compared with some popular algorithms of image mosaic. There are many algorithms to solve the problem of image mosaics, such as image mosaics based on feature lines, image mosaics based on the maximal grads and image mosaics based on the best seam-line. However they all have some disadvantages. For example, the algorithm of image mosaics based on feature lines uses the first image's pixels which are in two parallel lines with a certain interval between them to match that of the other image, and count their dispersion. Then compare the dipersion with the template, and the line with the minimum dispersion is the best matching position. This introduces excessive calculation. Another example is the algorithm of image mosaics based on the maximal grads. It gets two curves made of the maximal grads points first, then decides the most similitude parts of the two curves, and gets the matching position of two images. Although the calculation is decreased in this way, it could not find a suitable position easily when images are complex enough. Another example is the algorithm of image mosaics based on the best seam-line. It matches the two images by using the phasic emendation method. Then it finds a seam-line by using a simple and static rule according to the thought of dynamic programming Finally, mosaics the images according to the seam-line. This algorithm will not find the suitable seam-line when the difference of lighteness of the images is large. The image mosaics algorithm based on PSO presented in this paper improves the algorithms mentioned above in certain aspect. Particle swarm optimization (PSO) is an evolutionary computation technique developed by Dr. Eberhart and Dr. Kennedy, inspired by social behavior of bird flocking. Similar to genetic algorithms (GA), PSO is a population based optimization tool. The system is initialized with a population of random solutions and searches for optima by updating generations particles, are "flown" through the problem space by following the current optimum particles. PSO is easy to implement and there are few parameters need to be adjusted. PSO has been successfully applied in many areas: function optimization, artificial neural network training, fuzzy system control, and other areas where GA can be applied. The image mosaics algorithm based on PSO presented in this paper finds an image block with better features, therefore it can find the matching block in the suitable solution space efficiently. It's more reliable in matching images, because the block we select has more features. And its solution space calculated according to position and size of the block is much smaller than the half of the image. As a result our algorithm reduces the amount of compute and yields better reliability. Because two images may have obvious difference of brightness, our algorithm eliminates the sense of seam in the position of mosaics by using the technology of Multiresolution after finding the suitable position of mosaic. Its principle is extending the two images that need to mosaic first, then analyze the images with the Multiresolution method, and analyze them into a series of images that their bandwidth close to single frequency. We mosaic images those in the same resolution. At last we add them all, and then we can get the clear and seamless image. The experiment shows us that our algorithm is fast and can solve the problem of sense of seam. Another pivotal part of dynamic panorama is how to build scene of dynamic. We synthesize the dynamic scene using the technology of video texture in this paper. Video texture is a media type between image and video. It is limitless and continuous that made of a limited video. Although every frame in the video texture will be repeatedly played, the video texture is...