Measuring volume of stockpile using imaging station

It is crucial to know cutting and filling volumes in many surveys, mining, quarry and engineering field of works like dredging and embankment project. Generally, volume calculation is completed using conventional surveying methods. The trapezoidal method and classical cross sectioning have been presented in the literature. In other way around, by using conventional surveying methods, the volume calculation required a lot of time, laborers and risky as the big machineries running around the work areas. Digital close range photogrammetry has been insufficient for the volume calculation of the material need to calculation of volume in risk areas or in short time. In this case, long range surveying and scanning method is an alternative method to volume calculation. By the development of scanning and imaging technologies, Topcon Imaging Station (IS) used for three dimensional modeling (3D) surveying of objects in many field such as topographic survey, mining, construction and as-built survey, etc disciplines has become a productive, faster and accurate method. This study concern is getting the stockpile volume by using Topcon IS known as advanced technology instrument which promotes both scanning and long-range surveying. The instrument, a highly-developed technology specialized with Image Master Software; distinctive software that provides capabilities to reconstructed 3D modeling after the volume data was processed. Three dimensional (3D) surfaces are created through Triangulated Irregular Network (TIN) method that supports time saving and more accurate volume calculation. The volume calculated by Image Master (IM) then compared with the volume calculated by 12D software which the data obtained by using total station and prism. The results have been analyzed with respect to different volumes, density factor, three dimensional (3D) models of stockpile and time taken for data acquisition and data processin

Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation

Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise

PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

We present a compact but effective CNN model for optical flow, called PWC-Net. PWC-Net has been designed according to simple and well-established principles: pyramidal processing, warping, and the use of a cost volume. Cast in a learnable feature pyramid, PWC-Net uses the cur- rent optical flow estimate to warp the CNN features of the second image. It then uses the warped features and features of the first image to construct a cost volume, which is processed by a CNN to estimate the optical flow. PWC-Net is 17 times smaller in size and easier to train than the recent FlowNet2 model. Moreover, it outperforms all published optical flow methods on the MPI Sintel final pass and KITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024x436) images. Our models are available on https://github.com/NVlabs/PWC-Net.Comment: CVPR 2018 camera ready version (with github link to Caffe and PyTorch code

User-initialized active contour segmentation and golden-angle real-time cardiovascular magnetic resonance enable accurate assessment of LV function in patients with sinus rhythm and arrhythmias.

BackgroundData obtained during arrhythmia is retained in real-time cardiovascular magnetic resonance (rt-CMR), but there is limited and inconsistent evidence to show that rt-CMR can accurately assess beat-to-beat variation in left ventricular (LV) function or during an arrhythmia.MethodsMulti-slice, short axis cine and real-time golden-angle radial CMR data was collected in 22 clinical patients (18 in sinus rhythm and 4 patients with arrhythmia). A user-initialized active contour segmentation (ACS) software was validated via comparison to manual segmentation on clinically accepted software. For each image in the 2D acquisitions, slice volume was calculated and global LV volumes were estimated via summation across the LV using multiple slices. Real-time imaging data was reconstructed using different image exposure times and frame rates to evaluate the effect of temporal resolution on measured function in each slice via ACS. Finally, global volumetric function of ectopic and non-ectopic beats was measured using ACS in patients with arrhythmias.ResultsACS provides global LV volume measurements that are not significantly different from manual quantification of retrospectively gated cine images in sinus rhythm patients. With an exposure time of 95.2 ms and a frame rate of > 89 frames per second, golden-angle real-time imaging accurately captures hemodynamic function over a range of patient heart rates. In four patients with frequent ectopic contractions, initial quantification of the impact of ectopic beats on hemodynamic function was demonstrated.ConclusionUser-initialized active contours and golden-angle real-time radial CMR can be used to determine time-varying LV function in patients. These methods will be very useful for the assessment of LV function in patients with frequent arrhythmias