By Rasmus R. Paulsen, Kim S. Pedersen
ISBN-10: 3319196642
ISBN-13: 9783319196640
ISBN-10: 3319196650
ISBN-13: 9783319196657
This publication constitutes the refereed complaints of the nineteenth Scandinavian convention on snapshot research, SCIA 2015, held in Copenhagen, Denmark, in June 2015. The forty five revised papers offered have been conscientiously reviewed and chosen from sixty seven submissions. The contributions are dependent in topical sections on novel purposes of imaginative and prescient platforms, trend acceptance, laptop studying, function extraction, segmentation, 3D imaginative and prescient to clinical and biomedical snapshot analysis.
Read or Download Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings PDF
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Extra info for Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings
Example text
The idea for segmentation is to utilize the strong reconstructive properties of sparse coding by building a dictionary for each texture class. High segmentation performance is obtained by utilizing that the texture class used for learning the dictionary can be reconstructed well whereas other texture classes cannot. Methods focusing on optimal reconstruction have been suggested [26,30], and improved performance has been obtained by also optimizing for discrimination [20,21]. Recently Gao et al.
1305-00032B) and NEXIM project (grant no. 11-116226), both funded by the Danish Council for Strategic Research. B. A. Dahl References 1. : An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. TPAMI 26(9), 1124–1137 (2004) 2. : Colour, texture, and motion in level set based segmentation and tracking. Image and Vision Computing 28(3), 376–390 (2010) 3. : A tv flow based local scale measure for texture discrimination. G. ) ECCV 2004. LNCS, vol. 3022, pp. 578–590.
As a final step we normalise the histogram vectors in order to make the description robust to image contrast variations. We have experimented with different normalisation schemes (L1 , L2 and RootSIFT [1]) without observing significant differences in discriminative performance. We choose L1 normalisation. 5 Gradient Orientation Histograms For completeness, we also list the gradient orientation histogram which will be used in our experiments. The gradient orientation θg and its magnitude mg are computed from θg (x; σ) = arctan2 (Ly , Lx ) , mg = L2x + L2y (16) Similar to the shape index orientation we can construct a histogram by choosing a set of ng bin centres bg1 , .
Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings by Rasmus R. Paulsen, Kim S. Pedersen
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