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While different optical flow techniques continue to appear, there has been a lack of quantitative evaluation of existing methods. For a common set of real and synthetic image sequences, we report the results of a number of regularly cited optical flow techniques, including instances of differential, matching, energy-based, and phase-based methods. Our comparisons are primarily empirical, and concentrate on the accuracy, reliability, and density of the velocity measurements; they show that performance can differ significantly among the techniques we implemented.

Two-dimensional image motion is the projection of the three-dimensional motion of objects, relative to a visual sensor, onto its image plane. Sequences of time-orderedimages allow the estimation of projected two-dimensional image motion as either instantaneous image velocities or discrete image displacements. These are usually called the optical flow field or the image velocity field . Provided that optical flow is a reliable approximation to two-dimensional image motion, it may then be used to recover the three-dimensional motion of the visual sensor (to within a scale factor) and the three-dimensional surface structure (shape or relative depth) through assumptions concerning the structure of the optical flow field, the three-dimensional environment, and the motion of the sensor. Optical flow may also be used to perform motion detection, object segmentation, time-to-collision and focus of expansion calculations, motion compensated encoding, and stereo disparity measurement. We investigate the computation of optical flow in this survey: widely known methods for estimating optical flow are classified and examined by scrutinizing the hypothesis and assumptions they use. The survey concludes with a discussion of current research issues.

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We propose a multi-layer, real-time vehicle detection and tracking system using stereo vision, multi-view AdaBoost detectors, and optical flow. By adopting a ground plane estimate extracted from stereo information, we generate a sparse set of hypotheses and apply trained AdaBoost classifiers in addition to fast disparity histogramming, for Hypothesis Verification (HV) purposes. Our tracking system employs one Kalman filter per detected vehicle and motion vectors from optical flow, as a means to increase its robustness. An acceptable detection rate with few false positives is obtained at 25 fps with generic hardware.

(2015) Impact of target volume segmentation accuracy and variability on treatment planning for 4D-CT-based non-small cell lung cancer radiotherapy, Acta Oncologica, 54:3,[322][323][324][325][326][327][328][329][330][331][332] AbstrAct background. Accurate target volume segmentation is crucial for success in image-guided radiotherapy. However, variability in anatomical segmentation is one of the most significant contributors to uncertainty in radiotherapy treatment planning. This is especially true for lung cancer where target volumes are subject to varying magnitudes of respiratory motion. Material and methods.This study aims to analyze multiple observer target volume segmentations and subsequent intensity-modulated radiotherapy (IMRT) treatment plans defined by those segmentations against a reference standard for lung cancer patients imaged with four-dimensional computed tomography (4D-CT). Target volume segmentations of 10 patients were performed manually by six physicians, allowing for the calculation of ground truth estimate segmentations via the simultaneous truth and performance level estimation (STAPLE) algorithm. Segmentation variability was assessed in terms of distance-and volume-based metrics. Treatment plans defined by these segmentations were then subject to dosimetric evaluation consisting of both physical and radiobiological analysis of optimized 3D dose distributions. results. Significant differences were noticed amongst observers in comparison to STAPLE segmentations and this variability directly extended into the treatment planning stages in the context of all dosimetric parameters used in this study. Mean primary tumor control probability (TCP) ranged from (22.6  11.9)% to (33.7  0.6)%, with standard deviation ranging from 0.5% to 11.9%. However, mean normal tissue complication probabilities (NTCP) based on treatment plans for each physician-derived target volume well as the NTCP derived from STAPLE-based treatment plans demonstrated no discernible trends and variability appeared to be patient-specific. This type of variability demonstrated the large-scale impact that target volume segmentation uncertainty can play in IMRT treatment planning. conclusions. Significant target volume segmentation and dosimetric variability exists in IMRT treatment planning amongst experts in the presence of a reference standard for 4D-CT-based lung cancer radiotherapy. Future work is needed to mitigate this uncertainty and ensure highly accurate and effective radiotherapy for lung cancer patients.

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We report results from the preliminary trials of Colibri, a dedicated fast-photometry array for the detection of small Kuiper belt objects through serendipitous stellar occultations. Colibri's novel data processing pipeline analyzed 4000 star hours with two overlapping-field EMCCD cameras, detecting no Kuiper belt objects and one false positive occultation event in a high ecliptic latitude field. No occultations would be expected at these latitudes, allowing these results to provide a control sample for the upcoming main Colibri campaign. The empirical false positive rate found by the processing pipeline is consistent with the 0.002% simulationdetermined false positive rate. We also describe Colibri's software design, kernel sets for modeling stellar occultations, and method for retrieving occultation parameters from noisy diffraction curves. Colibri's main campaign will begin in mid-2018, operating at a 40 Hz sampling rate.