Multiphase Joint Segmentation-Registration and Object Tracking for Layered Images

Abstract

In this paper we propose to jointly segment and register objects of interest in layered images. Layered imaging refers to imageries taken from different perspectives and possibly by different sensors. Registration and segmentation are therefore the two main tasks which contribute to the bottom level, data alignment, of the multisensor data fusion hierarchical structures. Most exploitations of two layered images assumed that scanners are at very high altitudes and that only one transformation ties the two images. Our data are however taken at mid-range and therefore requires segmentation to assist us examining different object regions in a divide-and-conquer fashion. Our approach is a combination of multiphase active contour method with a joint segmentation-registration technique (which we called MPJSR) carried out in a local moving window prior to a global optimization. To further address layered video sequences and tracking objects in frames, we propose a simple adaptation of optical flow calculations along the active contours in a pair of layered image sequences. The experimental results show that the whole integrated algorithm is able to delineate the objects of interest, align them for a pair of layered frames and keep track of the objects over time.

Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest

“…A number of studies were focused on jointly tackling the problem of registration and semantic segmentation for mainly video sequences or medical images [48]- [51]. Similar research efforts were focused on jointly addressing the tasks of segmentation and tracking in image video sequences [52]- [54].…”

An Effective Multiple Moving Objects Tracking using Bayesian Particle Filter-Based Median Enhanced Laplacian Thresholding

“…The true positive rate is defined as the ratio of number of correctly detected frames from the video sequence to the total number of the frames in the video sequence. The true positive rate is formularized as given below, * 100 (14) When higher the true positive rate, the method is said to be more efficient and it is measured in terms of percentage (%). Then the false positive rate is defined as the ratio of number of incorrectly detected frames from the video sequence to the total number of the frames in the video sequence.…”

“…The layered sensing of image refers to the imageries obtained from several aspects by different sensors. In [14], object tracking for layered images was performed using joint segmentation and registration technique. In [15], detection of objects using boosting algorithm and Coimbatore.…”

Εντοπισμός Μεταβολών Σε Δορυφορικά Δεδομένα Πολύ Υψηλής Ανάλυσης

“…Addressing the registration and the detection problem under a joint/unified manner has been proposed, mainly, for dense multi-temporal datasets like video sequences, image tracking and medical imagery [19,35,32,36,76,86,66,40]. However, in sparse multi-temporal datasets the registration may fail to recover correspondences in image regions with important changes as the majority of the approaches consider the images identical.…”