Object Spatial Position Research Articles

Micro‐movement magnification in video signals using complex wavelet analysis

Magnifying micro-movements from natural video has recently been investigated by several computer vision researchers, due to its impact in numerous applications. In this study, the authors analyse video signals and try to magnify micro-movements/vibrations to make them visible. These micro-movements are typically undetectable and cannot be seen by basic human vision. They utilise complex wavelets to analyse sequential frames and detect any minor change in object's spatial position. They magnify some specific complex wavelet frequency bands by a multiplication factor and reconstruct back the video signal after some manipulation and modification to make these micro-movements seen and observable. They compare their work with recent techniques in micro-motion magnification (Freeman et al.) and try to show the merits of each technique. These micro-movements can later be utilised in different applications such as medical imaging, structural engineering, mechanical engineering, physical feature analysis and industrial engineering, as will be seen in their experiments.

Time-geographic density estimation for home range analysis

This research presents time-geographic density estimation (TGDE) as a new technique of animal home range analysis in geographic information science (GIS). TGDE combines methodologies of time geography and statistical density estimation to produce a continuous probability distribution of an object's spatial position over time. Once TGDE is applied to animal tracking data to create a density surface, home ranges and core areas can be delineated using specified contours of relative intensity (e.g., 95% or 50%). This article explores the use of TGDE for home range analysis using three data sets: a fixed-interval simulated data set and two variable-interval satellite tracks for a loggerhead sea turtle (Caretta caretta) corresponding to internesting and post-migration foraging periods. These applications are used to illustrate the influence of several parameters, including sample size, temporal sampling scheme, selected distance-weighted geoellipse function, and specified maximum velocity, on home range estimates. The results demonstrate how TGDE produces reasonable home range estimates even given irregular tracking data with wide temporal gaps. The advantages of TGDE as compared with traditional methods of home range estimation such as kernel density estimation are as follows: (1) intensities are not assigned to locations where the animal could not have been located given space and time constraints; (2) the density surface represents the actual uncertainty about an animal's spatial position during unsampled time periods; (3) the amount of smoothing applied is objectively specified based on the animal's movement velocity rather than arbitrarily chosen; and (4) uneven sampling intervals are easily accommodated since the density estimates are calculated based on the elapsed time between observed locations. In summary, TGDE is a useful method of home range estimation and shows promise for numerous applications to moving objects in GIS.

Detection of coronary calcifications from computed tomography scans for automated risk assessment of coronary artery disease

A fully automated method for coronary calcification detection from non-contrast-enhanced, ECG-gated multi-slice computed tomography (CT) data is presented. Candidates for coronary calcifications are extracted by thresholding and component labeling. These candidates include coronary calcifications, calcifications in the aorta and in the heart, and other high-density structures such as noise and bone. A dedicated set of 64 features is calculated for each candidate object. They characterize the object's spatial position relative to the heart and the aorta, for which an automatic segmentation scheme was developed, its size and shape, and its appearance, which is described by a set of approximated Gaussian derivatives for which an efficient computational scheme is presented. Three classification strategies were designed. The first one tested direct classification without feature selection. The second approach also utilized direct classification, but with feature selection. Finally, the third scheme employed two-stage classification. In a computationally inexpensive first stage, the most easily recognizable false positives were discarded. The second stage discriminated between more difficult to separate coronary calcium and other candidates. Performance of linear, quadratic, nearest neighbor, and support vector machine classifiers was compared. The method was tested on 76 scans containing 275 calcifications in the coronary arteries and 335 calcifications in the heart and aorta. The best performance was obtained employing a two-stage classification system with a k-nearest neighbor (k-NN) classifier and a feature selection scheme. The method detected 73.8% of coronary calcifications at the expense of on average 0.1 false positives per scan. A calcium score was computed for each scan and subjects were assigned one of four risk categories based on this score. The method assigned the correct risk category to 93.4% of all scans.