What we want is an orthogonal color space without correlations between the axes. This complicates any color modification process. This implies that if we want to change the appearance of a pixel's color in a coherent way, we must modify all color channels in tandem. For example, in RGB space, most pixels will have large values for the red and green channel if the blue channel is large. When a typical three channel image is represented in any of the most well-known color spaces, there will be correlations between the different channels'val-ues. Our goal is to do so with a simple algorithm, and our core strategy is to choose a suitable color space and then to apply simple oper-ations there.
We can imagine many methods for applying the colors of one image to another. Figure 1 shows an example of this process, where we applied the colors of a sunset photograph to a daytime computer graphics rendering. This article describes a method for a more general form of color correction that borrows one image's color characteristics from anoth-er. Often this means removing a dominant and undesirable color cast, such as the yellow in photos taken under incandescent illumination. With the presented methods feature-tracking with real time frame rates can be achieved on the GPU and meanwhile the CPU can be used for other tasks. dynamic branching and multiple render targets (MRT) in the fragment processor.
Vr panorama stitcher series#
The approach works well on Geforce6 series graphics board and above and takes advantage of new hardware features, e.g. We present results of the various stages for feature vector generation of our GPU implementation and compare it to the CPU version of the SIFT algorithm. For the generation of feature-vectors the Scale Invariant Feature Transform (SIFT) method is used due to its high stability against rotation, scale and lighting condition changes of the processed images.
The focus lies on the generation of feature vectors from input images in the various stages. In this paper we present methods and techniques that take advantage of modern graphics hardware for real-time tracking and recognition of feature-points.
Vr panorama stitcher free#
With the addition of free programmable components to modern graphics hardware, graphics processing units (GPUs) become increasingly interesting for general purpose computations, especially due to utilizing parallel buffer processing. The results obtained are satisfactory in terms of stability, quality, execution time and reduction of the computational complexity. Among the advantages of our method is solving problems related to outliers that can, in existing methods, affect the reliability of the mosaic.
In this case, the transformation estimation will be based on the regions seeds that provide the best correlation score. The calculation of the transformation model is based on the VORONOI diagram that divides images into regions to be used in the matching instead of control points. Our approach, in this regard, comes to solve this problem. The random selection of matching points used in existing methods, using Random Sample Consensus (RANSAC) or the threshold of the execution process (iteration number) cannot generally provide sufficient precision. In this article, we propose a new method of image stitching that computes, in a robust manner, the transformation model applied to creating a panorama that is close to reality.
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