To this aim, we report experimental results on the BIOGIGA database with four feature extraction approaches (contour coordinates, shape contexts, Fourier descriptors and landmarks) and three classification methods (Euclidean distance, dynamic time warping and support vector machines).
As a widely used method of edge extraction, the wavelet transform modulus maximum algorithm is also employed to extract contours.
As can be seen, there are three principal stages: the contour extraction stage, the feature extraction stage and the comparison stage.
Fig. 9 Contour extraction result: a original image, b our contour extraction, c Gabor energy filter extraction, and d Gabor energy filtering augmented with surround inhibition extraction.
We have selected four different approaches for the feature extraction stage: (i) contour coordinates themselves, (ii) shape contexts [24], (iii) Fourier descriptors of the coordinates [31] and (iv) silhouette landmarks [6].
However, when working with images extracted from real MMW sensors in which the contour extraction stage may be more difficult due to the presence of noise, illumination variation and so forth, we believe approaches such as SC-LM may be more robust than the CC as SC-LM approach uses richer information.
This problem is addressed as follows: Figure 11 Discrete Contours Extraction: (a) Examples of multi-resolution contours.
The contour extraction module implements standard image processing algorithms for contour extraction.
Experimental results (Fig. 9) show that this simple contour extraction method is competitive compared with more complex contour extraction methods like the Gabor energy filter [38] and the Gabor energy filtering augmented with surround inhibition [39].
Algorithm 2: melody extraction using pitch contour characterization In [1], the authors propose a method for melody extraction from polyphonic music by pitch contour extraction and characterization.
