Camera System Performance Derived from Natural Scenes

The Modulation Transfer Function (MTF) is a well-established measure of camera system performance, commonly employed to characterize optical and image capture systems. It is a measure based on Linear System Theory; thus, its use relies on the assumption that the system is linear and stationary. This is not the case with modern-day camera systems that incorporate non-linear image signal processes (ISP) to improve the output image. Non-linearities result in variations in camera system performance, which are dependent upon the specific input signals. This paper discusses the development of a novel framework, designed to acquire MTFs directly from images of natural complex scenes, thus making the use of traditional test charts with set patterns redundant. The framework is based on extraction, characterization and classification of edges found within images of natural scenes. Scene derived performance measures aim to characterize non-linear image processes incorporated in modern cameras more faithfully. Further, they can produce ‘live’ performance measures, acquired directly from camera feeds

Split-screen single-camera stereoscopic PIV application to a turbulent confined swirling layer with free surface

An annular liquid wall jet, or vortex tube, generated by helical injection inside a tube is studied experimentally as a possible means of fusion reactor shielding. The hollow confined vortex/swirling layer exhibits simultaneously all the complexities of swirling turbulence, free surface, droplet formation, bubble entrapment; all posing challenging diagnostic issues. The construction of flow apparatus and the choice of working liquid and seeding particles facilitate unimpeded optical access to the flow field. A split-screen, single-camera stereoscopic particle image velocimetry (SPIV) scheme is employed for flow field characterization. Image calibration and free surface identification issues are discussed. The interference in measurements of laser beam reflection at the interface are identified and discussed. Selected velocity measurements and turbulence statistics are presented at Re_λ = 70 (Re = 3500 based on mean layer thickness)

Multiple Spatial Frequencies Pyramid WaveFront Sensing

A modification of the pyramid wavefront sensor is described. In this conceptually new class of devices, the perturbations are split at the level of the focal plane depending upon their spatial frequencies, and then measured separately. The aim of this approach is to increase the accuracy in the determination of some range of spatial frequency perturbations, or a certain classes of modes, disentangling them from the noise associated to the Poissonian fluctuations of the light coming from the perturbations outside of the range of interest or from the background in the pupil planes; the latter case specifically when the pyramid wavefront sensor is used with a large modulation. While the limits and the effectiveness of this approach should be further investigated, a number of variations on the concept are shown, including a generalization of the spatial filtering in the point-diffraction wavefront sensor. The simplest application, a generalization to the pyramid of the well-known spatially filtering in wavefront sensing, is showing promise as a significant limiting magnitude advance. Applications are further speculated in the area of extreme adaptive optics and when serving spectroscopic instrumentation where “light in the bucket” rather than Strehl performance is required

Constrained snake vs. conventional snake for carotid ultrasound automated IMT measurements on multi-center data sets

Accurate intima-media thickness (IMT) measurement of the carotid artery from minimal plaque ultrasound images is a relevant clinical need, since IMT increase is related to the progression of atherosclerosis. In this paper, we describe a novel dual snake-based model for the high-performance carotid IMT measurement, called Carotid Measurement Using Dual Snakes (CMUDS). Snakes (which are deformable contours) adapt to the lumen-intima (LI) and media-adventitia (MA) interfaces, thus enabling the IMT computation as distance between the LI and MA snakes. However, traditional snakes might be unable to maintain a correct distance and in some spatial location along the artery, it might even collapse between them or diverge. The technical improvement of this work is the definition of a dual snake-based constrained system, which prevents the LI and MA snakes from collapsing or bleeding, thus optimizing the IMT estimation. The CMUDS system consists of two parametric models automatically initialized using the far adventitia border which we automatically traced by using a previously developed multi-resolution approach. The dual snakes evolve simultaneously and are constrained by the distances between them, ensuring the regularization of LI/MA topology. We benchmarked our automated CMUDS with the previous conventional semi-automated snake system called Carotid Measurement Using Single Snake (CMUSS). Two independent readers manually traced the LIMA boundaries of a multi-institutional, multi-ethnic, and multi-scanner database of 665 CCA longitudinal 2D images. We evaluated our system performance by comparing it with the gold standard as traced by clinical readers. CMUDS and CMUSS correctly processed 100% of the 665 images. Comparing the performance with respect to the two readers, our automatically measured IMT was on average very close to that of the two readers (IMT measurement biases for CMUSS was equal to −0.011 ± 0.329 mm and −0.045 ± 0.317 mm, respectively, while for CMUDS, it was 0.030 ± 0.284 mm and −0.004 ± 0.273 mm, respectively). The Figure-of-Merit of the system was 98.5% and 94.4% for CMUSS, while 96.0% and 99.6% for CMUDS, respectively. Results showed that the dual-snake system CMUDS reduced the IMT measurement error accuracy (Wilcoxon, p < 0.02) and the IMT error variability (Fisher, p < 3 × 10−2). We propose the CMUDS technique for use in large multi-centric studies, where the need for a standard, accurate, and automated IMT measurement technique is require

Neural network edge detector

Extracting edges from images is a widely used first step in image processing. A different view on the well known enhancement/thresholding approach for edge detection is presented in this paper. The structure of a two layer feed forward neural network is comparable to the structure of enhancement/thresholding edge detectors. It is possible to calculate an optimal edge detector with a certain predefined network structure and training set, by training the neural network with examples of edge and non-edge patterns. The back propagation learning rule is used for optimization of the network. The choice of the network structure and the training set are very important, because they determine the final behaviour of the network. The paper describes which network structures were selected and how the training sets were generated. Some of the experiments are described, and observations of the convolution kernels for edge enhancement that are formed during training. Finally the results are evaluated and compared with the results of edge detectors based on the Sobel, Marr-Hildreth and Canny edge enhancement algorithms. It appears that the neural network edge detector can be made very robust against noise and blur and in most tests outperforms the others.</p

Identification and measurement of fibers in scanning electron microscopy images using a high-order correlation process

Includes bibliographical references.This work describes the development of a dedicated system capable of identifying, measuring and counting various types of fibers and other objects in digitized scanning electron micrograph (SEM) imagery. The system uses a recursive high order correlation (HOC) process to extract the corner pixels of the fibers. The objects are defined by grouping connected corners, so that morphometric analysis can be performed. The method developed performs satisfactorily when the density of fibers per image ranges from low to medium. Simulation results for several cases are presented, along with a discussion on the capabilities and limitations of the current version of the system.This work was supported by Schuller, Inc., Denver, CO

Perceived Blur in Naturally Contoured Images Depends on Phase

Perceived blur is an important measure of image quality and clinical visual function. The magnitude of image blur varies across space and time under natural viewing conditions owing to changes in pupil size and accommodation. Blur is frequently studied in the laboratory with a variety of digital filters, without comparing how the choice of filter affects blur perception. We examine the perception of image blur in synthetic images composed of contours whose orientation and curvature spatial properties matched those of natural images but whose blur could be directly controlled. The images were blurred by manipulating the slope of the amplitude spectrum, Gaussian low-pass filtering or filtering with a Sinc function, which, unlike slope or Gaussian filtering, introduces periodic phase reversals similar to those in optically blurred images. For slope-filtered images, blur discrimination thresholds for over-sharpened images were extremely high and perceived blur could not be matched with either Gaussian or Sinc filtered images, suggesting that directly manipulating image slope does not simulate the perception of blur. For Gaussian- and Sinc-blurred images, blur discrimination thresholds were dipper-shaped and were well-fit with a simple variance discrimination model and with a contrast detection threshold model, but the latter required different contrast sensitivity functions for different types of blur. Blur matches between Gaussian- and Sinc-blurred images were used to test several models of blur perception and were in good agreement with models based on luminance slope, but not with spatial frequency based models. Collectively, these results show that the relative phases of image components, in addition to their relative amplitudes, determines perceived blur