Camera Spatial Frequency Response Derived from Pictorial Natural Scenes

Camera system performance is a prominent part of many aspects of imaging science and computer vision. There are many aspects to camera performance that determines how accurately the image represents the scene, including measurements of colour accuracy, tone reproduction, geometric distortions, and image noise evaluation. The research conducted in this thesis focuses on the Modulation Transfer Function (MTF), a widely used camera performance measurement employed to describe resolution and sharpness. Traditionally measured under controlled conditions with characterised test charts, the MTF is a measurement restricted to laboratory settings. The MTF is based on linear system theory, meaning the input to output must follow a straightforward correlation. Established methods for measuring the camera system MTF include the ISO12233:2017 for measuring the edge-based Spatial Frequency Response (e-SFR), a sister measure of the MTF designed for measuring discrete systems. Many modern camera systems incorporate non-linear, highly adaptive image signal processing (ISP) to improve image quality. As a result, system performance becomes scene and processing dependant, adapting to the scene contents captured by the camera. Established test chart based MTF/SFR methods do not describe this adaptive nature; they only provide the response of the camera to a test chart signal. Further, with the increased use of Deep Neural Networks (DNN) for image recognition tasks and autonomous vision systems, there is an increased need for monitoring system performance outside laboratory conditions in real-time, i.e. live-MTF. Such measurements would assist in monitoring the camera systems to ensure they are fully operational for decision critical tasks. This thesis presents research conducted to develop a novel automated methodology that estimates the standard e-SFR directly from pictorial natural scenes. This methodology has the potential to produce scene dependant and real-time camera system performance measurements, opening new possibilities in imaging science and allowing live monitoring/calibration of systems for autonomous computer vision applications. The proposed methodology incorporates many well-established image processes, as well as others developed for specific purposes. It is presented in two parts. Firstly, the Natural Scene derived SFR (NS-SFR) are obtained from isolated captured scene step-edges, after verifying that these edges have the correct profile for implementing into the slanted-edge algorithm. The resulting NS-SFRs are shown to be a function of both camera system performance and scene contents. The second part of the methodology uses a series of derived NS-SFRs to estimate the system e-SFR, as per the ISO12233 standard. This is achieved by applying a sequence of thresholds to segment the most likely data corresponding to the system performance. These thresholds a) group the expected optical performance variation across the imaging circle within radial distance segments, b) obtain the highest performance NS-SFRs per segment and c) select the NS-SFRs with input edge and region of interest (ROI) parameter ranges shown to introduce minimal e-SFR variation. The selected NS-SFRs are averaged per radial segment to estimate system e-SFRs across the field of view. A weighted average of these estimates provides an overall system performance estimation. This methodology is implemented for e-SFR estimation of three characterised camera systems, two near-linear and one highly non-linear. Investigations are conducted using large, diverse image datasets as well as restricting scene content and the number of images used for the estimation. The resulting estimates are comparable to ISO12233 e-SFRs derived from test chart inputs for the near-linear systems. Overall estimate stays within one standard deviation of the equivalent test chart measurement. Results from the highly non-linear system indicate scene and processing dependency, potentially leading to a more representative SFR measure than the current chart-based approaches for such systems. These results suggest that the proposed method is a viable alternative to the ISO technique

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

A tool for deriving camera spatial frequency response from natural scenes (NS-SFR)

Recent research on digital camera performance evaluation introduced the Natural Scene Spatial Frequency Response (NS-SFR) framework, shown to provide a comparable measure to the ISO12233 edge SFR (e-SFR) but derived outside laboratory conditions. The framework extracts step-edges captured from pictorial natural scenes to evaluate the camera SFR. It is in 2-parts. The first utilizes the ISO12233 slanted-edge algorithm to produce an ‘envelope’ of NS-SFRs. The second estimates the system e-SFR from this NS-SFR data. One drawback of this proposed methodology has been the computation time. The process was not optimized, as it first derived NS-SFRs from all suitable step-edges and then further validated and statistically treated the results to estimate the e-SFR. This paper presents changes to the framework processes, aiming to optimize the computation time so that it is practical for real-world implementation. The developments include an improved framework structure, a pixel-stretching filter alternative, and the capability to utilize Graphics Processing Unit (GPU) acceleration. In addition, the methodology was updated to utilize the latest e-SFR algorithm implementation. The resulting code has been incorporated into a self-executable user interface prototype, available in GitHub. Future goals include making it an open-access, cloud-based solution to be used by scientists, camera evaluation labs and the general public

A framework for the metrification of input image quality in deep networks

Deep Neural Networks (DNNs) are critical for real-time imaging applications including autonomous vehicles. DNNs are often trained and validated with images that originate from a limited number of cameras, each of which has its own hardware and image signal processing (ISP) characteristics. However, in most real-time embedded systems, the input images come from a variety of cameras with different ISP pipelines, and often include perturbations due to a variety of scene conditions. Data augmentation methods are commonly exploited to enhance the robustness of such systems. Alternatively, methods are employed to detect input images that are unfamiliar to the trained networks, including out of distribution detection. Despite these efforts DNNs remain widely systems with operational boundaries that cannot be easily defined. One reason is that, while training and benchmark image datasets include samples with a variety of perturbations, there is a lack of research in the areas of metrification of input image quality suitable to DNNs and a universal method to relate quality to DNN performance using meaningful quality metrics. This paper addresses this lack of metrification specific to DNNs systems and introduces a framework that uses systematic modification of image quality attributes and relate input image quality to DNN performance

Edge Detection Techniques for Quantifying Spatial Imaging System Performance and Image Quality

Measuring camera system performance and associating it directly to image quality is very relevant, whether images are aimed for viewing, or as input to machine learning and automated recognition algorithms. The Modulation Transfer Function (MTF) is a well- established measure for evaluating this performance. This study proposes a novel methodology for measuring system MTFs directly from natural scenes, by adapting the standardized Slanted Edge Method (ISO 12233). The method involves edge detection techniques, to select and extract suitable step edges from pictorial images. The scene MTF aims to account for camera non-linear scene dependent processes. This measure is more relevant to image quality modelling than the traditionally measured MTFs. Preliminary research results indicate that the proposed method can provide reliable MTFs, following the trends of the ISO 12233. Further development and validation are required before it is proposed as a universal camera measuring technique

Natural Scene Derived Camera Edge Spatial Frequency Response for Autonomous Vision Systems

The edge Spatial Frequency Response (eSFR) is an established measure for camera system quality performance, traditionally measured under laboratory conditions. With the increasing use of Deep Neural Networks (DNNs) in autonomous vision systems, the input signal quality becomes crucial for optimal operation. This paper proposes a method to estimate the system eSFR (sys-SFR) from pictorial natural scene derived SFRs (NS-SFRs) as previously presented, laying the foundation for adapting the traditional method to a real-time measure. In this study, the NS-SFR input parameter variations are first investigated to establish suitable ranges that give a stable estimate. Using the NS-SFR framework with the established parameter ranges, the system eSFR, as per ISO 12233, is estimated. Initial validation of results is obtained from implementing the measuring framework with images from a linear and a non-linear camera system. For the linear system, results closely approximate the ISO 12233 eSFR measurement. Non-linear system measurements exhibit scene dependant characteristics expected from edge-based methods. The requirements to implement this method in real-time for autonomous systems are then discussed

Impact of DOTS expansion on tuberculosis related outcomes and costs in Haiti

BACKGROUND: Implementation of the World Health Organization's DOTS strategy (Directly Observed Treatment Short-course therapy) can result in significant reduction in tuberculosis incidence. We estimated potential costs and benefits of DOTS expansion in Haiti from the government, and societal perspectives. METHODS: Using decision analysis incorporating multiple Markov processes (Markov modelling), we compared expected tuberculosis morbidity, mortality and costs in Haiti with DOTS expansion to reach all of the country, and achieve WHO benchmarks, or if the current situation did not change. Probabilities of tuberculosis related outcomes were derived from the published literature. Government health expenditures, patient and family costs were measured in direct surveys in Haiti and expressed in 2003 US$. RESULTS: Starting in 2003, DOTS expansion in Haiti is anticipated to cost$4.2 million and result in 63,080 fewer tuberculosis cases, 53,120 fewer tuberculosis deaths, and net societal savings of $131 million, over 20 years. Current government spending for tuberculosis is high, relative to the per capita income, and would be only slightly lower with DOTS. Societal savings would begin within 4 years, and would be substantial in all scenarios considered, including higher HIV seroprevalence or drug resistance, unchanged incidence following DOTS expansion, or doubling of initial and ongoing costs for DOTS expansion. CONCLUSION: A modest investment for DOTS expansion in Haiti would provide considerable humanitarian benefit by reducing tuberculosis-related morbidity, mortality and costs for patients and their families. These benefits, together with projected minimal Haitian government savings, argue strongly for donor support for DOTS expansion

Prader-Willi syndrome: A primer for clinicians

The advent of sensitive genetic testing modalities for the diagnosis of Prader-Willi syndrome has helped to define not only the phenotypic features of the syndrome associated with the various genotypes but also to anticipate clinical and psychological problems that occur at each stage during the life span. With advances in hormone replacement therapy, particularly growth hormone children born in circumstances where therapy is available are expected to have an improved quality of life as compared to those born prior to growth hormone

A Framework for the Precautionary Governance of Food Safety: integrating science and participation in the social appraisal of risk

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