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Extra resources for Advanced Concepts for Intelligent Vision Systems: 12th International Conference, ACIVS 2010, Sydney, Australia, December 13-16, 2010, Proceedings, Part II

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3 e−9[|log10 ρ−log10 9|] ρ<7 ρ≥7 (1) where ρ = μ2 + ν 2 . e. U {C(i, j)} = DCT −1 H( μ2 + ν 2 )Ω(μ, ν) (2) where Ω(μ, ν) is the 2D DCT of the image and DCT −1 is the 2D inverse DCT. B. Mansoor and A. Anwar and L2 norm: H2 = 1 K K [ k=1 N−1 1 N2 ˆk (i, j)}|2 ]1/2 |U {Ck (i, j)} − U {C (4) i,j=0 Spectral Distance Measure. In spectral distance measures, the complex Fourier of the images is taken to give the distortion penalty functions [11], [12]. Considering the discrete Fourier transform of k th band of the original and coded images by Γk (μ, ν) and Γˆk (μ, ν) respectively.

4 Experimental Results To test our method, we considered a sequence of images recorded at a typical crossroad scenario (see Fig. 4). 5 frames per second. The images were taken from the top of a 28m tower. Each image has a resolution of 480 × 640 pixels (width x height). Besides very mild vibrations, the camera’s optical and positional parameters remained fixed. The first task was to construct the edge background model. Starting from the edge image, computed using the Canny’s edge detector[1] on the incoming image stream {Ik (x)}, we estimate the image gradient ∇Ik (x) of every edge pixel.

The topology-based image fragment matching has two parameters determining its computational complexity (see Eq. e. n – the number of matched keypoint pairs and m, which is the number of neighbors of a given keypoint pair that are tested for the topological consistency. If m is too small, we loose to a certain extent the scale invariance and, additionally, the quality degrades because of insufficient amounts of data available. On the other hand, too large neighborhoods dramatically increase the computational costs due to O(nm3 ) factor in the computational complexity.

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