Computer Analysis of Images and Patterns: 15th International by Jinhee Chun, Natsuda Kaothanthong (auth.), Richard Wilson,

By Jinhee Chun, Natsuda Kaothanthong (auth.), Richard Wilson, Edwin Hancock, Adrian Bors, William Smith (eds.)

The quantity set LNCS 8047 and 8048 constitutes the refereed complaints of the fifteenth foreign convention on laptop research of pictures and styles, CAIP 2013, held in York, united kingdom, in August 2013. The 142 papers offered have been conscientiously reviewed and chosen from 243 submissions. The scope of the convention spans the next parts: 3D television, biometrics, colour and texture, record research, graph-based equipment, photo and video indexing and database retrieval, picture and video processing, image-based modeling, kernel equipment, scientific imaging, cellular multimedia, model-based imaginative and prescient methods, movement research, common computation for electronic imagery, segmentation and grouping, and form illustration and analysis.

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Extra info for Computer Analysis of Images and Patterns: 15th International Conference, CAIP 2013, York, UK, August 27-29, 2013, Proceedings, Part II

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We use these similarity scores to learn to label a new pair of sketch-photo (drawn by the same person) as match/non-match by minimizing the estimation error E = σ∈φ, ς∈ϕ W (σi , f¯(ςi ) (see Figure 2). General-Specific Modeling. The size of the drawing profile for each eyewitness is small (10 pairs) and relying merely on the drawing profile results in poor estimation of the sketching bias τ . We therefore propose to also utilize the general information embedded in the entire dataset of sketches (710 sketch-photo pairs).

For instance in case of log-polar binning, if a coin image is rotated then it is more likely that the visual words of one sector will fall into the adjacent 24 H. Anwar, S. Zambanini, and M. Kampel sector resulting in change of histograms of both the sectors. Lastly, increasing the number of features per image for the construction of visual vocabulary tends to increase the performance in BoVWs image representation as shown in Fig. 5. Therefore we conclude from the results that for our problem, to add spatial information to BoVWs, circular tiling is the most suitable choice subject to the condition that a coarser grid is used for dense sampling.

For each partition, a histogram of M visual words is generated resulting in 4 histograms of M words. These Supporting Ancient Coin Classification 21 histograms are then concatenated into a single feature vector of size M × 4. Fig. 3 shows the rectangular spatial tiling of 2 × 2. [13] for the first time to develop their shape matching descriptor. Zhang and Mayo [14] used log-polar binning to incorporate spatial information to BoVWs. [15] on three benchmark datasets. When log-polar tiling is applied on a given image, this image is partitioned into sectors of various scales and orientation.

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