By Sebastiano Battiato
This publication constitutes the completely refereed court cases of the sixteenth overseas convention on complicated techniques for clever imaginative and prescient platforms, ACIVS 2015, held Catania, Italy, in October 2015. The seventy six revised complete papers have been conscientiously chosen from 129 submissions.
Read or Download Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings PDF
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Additional resources for Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings
Perceptual blur and ringing metrics: Application to JPEG2000. Signal Processing: Image Communications 3(2), 163–172 (2004) 11. : A no-reference perceptural image sharpness metric based on a cumulative probability of blur detection. In: International Workshop on Quality of Multimedia Experience, QoMEx 2009 (2009) 12. : Determination of image edge width by unsharp masking. Computational Mathematics and Modeling 25(1), 72–78 (2014) 13. : Neural edge enhancer for supervised edge enhancement from noisy images.
B) and (c) The same image being represented in 3D, with intensity being the third dimension. Both (b) and (c) when viewed from diﬀerent angles, exhibits a set of second order statistics such as ridges, valleys, summits, edges etc. 1 Related Work In one dimension, the ﬁrst order gradient extracted at a point gives the slope of the curve at the given point. In case of an image, the ﬁrst order gradient at a pixel measures the slope of the luminance proﬁle at that pixel. Several local image descriptors such as SIFT , GLOH , DAISY , and LBP  are based on ﬁrst order gradient information of the image.
These sharp peaks correspond to the two directions of the curve (an entering and leaving path). In case of a junction, the number of peaks corresponds to the number of crest lines (ridges/valleys) in the junction (point 4 in Fig. 4). We obtain the same information for the bent lines (illustrated in point 2 on Fig. 4). Finally, due to the strong smoothing (parameter μ), D is close to 0 in the presence of noise without any crest line nor edge (illustrated in point 10 in Fig. 4). This illustrates the robustness of this method in the presence of noise.
Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings by Sebastiano Battiato