Computer Vision – ACCV 2010: 10th Asian Conference on by Timo Tossavainen (auth.), Ron Kimmel, Reinhard Klette,

By Timo Tossavainen (auth.), Ron Kimmel, Reinhard Klette, Akihiro Sugimoto (eds.)

The four-volume set LNCS 6492-6495 constitutes the completely refereed post-proceedings of the tenth Asian convention on laptop imaginative and prescient, ACCV 2009, held in Queenstown, New Zealand in November 2010.

All jointly the 4 volumes current 206 revised papers selected
from a complete of 739 Submissions. All present matters in desktop imaginative and prescient are addressed starting from algorithms that try and immediately comprehend the content material of pictures, optical equipment coupled with computational thoughts that improve and increase photographs, and taking pictures and studying the world's geometry whereas getting ready the better point photograph and form realizing. Novel gemometry ideas, statistical studying equipment, and sleek algebraic strategies are handled besides.

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Extra info for Computer Vision – ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part III

Example text

By quantitative description of the weather effects with the airlight A, we can design depth-variant thresholds by A+δ (δ >25), and detect surface objects from the dark channel image by labeling pixels whose intensities are less than their corresponding thresholds. 4 Experimental Results In our experiments, the dark channel image is computed using Marcel van Herk’s fast local minimum operator [8] with 8 × 8 rectangular kernels. To obtain robust estimation of the airlight, we use cˆ= c−σ (σ = 20) instead of c in our experiments, according to the Corollary 1.

Because objects-participating local patches always contain low intensities in at least one color channel, we detect suspicious small surface objects using the dark channel prior. Then, we estimate the approximate depth map of maritime scenes from a single image, based on the theory of perspective projection. Finally, using the estimated depth map and the atmospheric scattering model, we design spatial-variant thresholds to identify small surface objects from noisy backgrounds, without contrast enhancement.

4 Experimental Results In our experiments, the dark channel image is computed using Marcel van Herk’s fast local minimum operator [8] with 8 × 8 rectangular kernels. To obtain robust estimation of the airlight, we use cˆ= c−σ (σ = 20) instead of c in our experiments, according to the Corollary 1. However, the estimated airlight will shrink due to the adjustment of c. g. δ = 10). Figure 4 shows the results of our algorithm on the surveillance videos of the lake. As can been seen, our algorithm can achieve good performance for objects at various distances on both sunny days and rainy days.

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