Detection of cetacean and fish sounds using normalized summation of harmonics and spectrogram masking.

There is a growing interest in the ability to detect and classify animal vocalizations in large scale bioacoustic databases for the purposes of conservation and research. To aid in this, two methods are proposed for the quick and accurate detection of harmonic cetacean and fish vocalizations: Normalized summation of sound harmonics and spectrogram masking. These methods utilize a normalization scheme that enables robust performance, achieving 30% more precision and recall than traditional spectrogram cross correlation in the presence of wideband noise and low signal-to-noise ratios.

Left ventricular segmentation from MRI datasets with edge modelling conditional random fields.

Background: This paper considers automatic segmentation of the left cardiac ventricle in short axis magnetic resonance images. Various aspects, such as the presence of papillary muscles near the endocardium border, makes simple threshold based segmentation difficult.

Methods: The endo- and epicardium are modelled as two series of radii which are inter-related using features describing shape and motion.

Estimating the pen trajectories of static signatures using hidden Markov models.

Static signatures originate as handwritten images on documents and by definition do not contain any dynamic information. This lack of information makes static signature verification systems significantly less reliable than their dynamic counterparts. This study involves extracting dynamic information from static images, specifically the pen trajectory while the signature was created.