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. The proposed methods also perform up to 135 times faster than spectrogram cross correlation.

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. Image features are derived from edge information from human annotated images. The features are combined within a discriminatively trained Conditional Random Field (CRF). Loopy belief propagation is used to infer segmentations when an unsegmented video sequence is given. Powell's method is applied to find CRF parameters by minimizing the difference between ground truth annotations and the inferred contours. We also describe how the endocardium centre points are calculated from a single human-provided centre point in the first frame, through minimization of frame alignment error. RESULTS: We present and analyse the results of segmentation. The algorithm exhibits robustness against inclusion of the papillary muscles by integrating shape and motion information. Possible future improvements are identified. CONCLUSIONS: The presented model integrates shape and motion information to segment the inner and outer contours in the presence of papillary muscles. On the Sunnybrook dataset we find an average Dice metric of 0.91 ± 0.02 and 0.93 ± 0.02 for the inner and outer segmentations, respectively. Particularly problematic are patients with hypertrophy where the blood pool disappears from view at end-systole.

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. We assume that a dynamic version of the static image is available (typically obtained during an earlier registration process). We then derive a hidden Markov model from the static image and match it to the dynamic version of the image. This match results in the estimated pen trajectory of the static image.