Practical photoquantity measurement using a camera

International audienceAn image output by a camera is generally not a faithful representation of the real scene, because it undergoes a series of radiometric disturbances during the imaging process. This paper proposes a method for obtaining a more accurate measure of the light seen by a camera. Our method requires no specific calibration apparatus and only minimal supervision. Nevertheless, it is quite comprehensive, since it accounts for response function, exposure, vignetting, spatial non-uniformity of the sensor and colour balancing. Our method works in two steps. First, the camera is calibrated off-line, in a photoquantity sense. Then, the photoquantity of any scene can be estimated in-line. Our method is therefore geared to a wide range of computer vision applications where a camera is expected to give a measurement of the visible light. The paper starts by presenting a photoquantity model of the camera imaging process. It then describes the key steps of calibration and correction method. Finally, results are given and analyzed to evaluate the relevance of our approach

Analysis of lesion border segmentation using watershed algorithm

Automatic lesion segmentation is an important part of computer-based skin cancer detection. A watershed algorithm was introduced and tested on benign and melanoma images. The average of three dermatologists\u27 manually drawn borders was compared as the benchmark. Hair removing, black border removing and vignette removing methods were introduced in preprocessing steps. A new lesion ratio estimate was added to the merging method, which was determined by the outer bounding box ratio. In postprocessing, small blob removing and border smoothing using a peninsula removing method as well as a second order B-Spline smoothing method were included. A novel threshold was developed for removing large light areas near the lesion boundary. A supervised neural network was applied to cluster results and improve the accuracy, classifying images into three clusters: proper estimate, over-estimate and under-estimate. Comparing to the manually drawn average border, an overall of 11.12% error was achieved. Future work will involve reducing peninsula-shaped noise and looking for other reliable features for the classifier --Abstract, page iii

Standardized spectral and radiometric calibration of consumer cameras

Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database

Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry

Quick mass production of homogeneous thin film material is required in paper, plastic, fabric, and thin film industries. Due to the high feed rates and small thicknesses, machine vision and other nondestructive evaluation techniques are used to ensure consistent, defect-free material by continuously assessing post-production quality. One of the fastest growing inspection areas is for 0.5-500 micrometer thick thin films, which are used for semiconductor wafers, amorphous photovoltaics, optical films, plastics, and organic and inorganic membranes. As a demonstration application, a prototype roll-feed imaging system has been designed to inspect high-temperature polymer electrolyte membrane (PEM), used for fuel cells, after being die cast onto a moving transparent substrate. The inspection system continuously detects thin film defects and classifies them with a neural network into categories of holes, bubbles, thinning, and gels, with a 1.2% false alarm rate, 7.1% escape rate, and classification accuracy of 96.1%. In slot die casting processes, defect types are indicative of a misbalance in the mass flow rate and web speed; so, based on the classified defects, the inspection system informs the operator of corrective adjustments to these manufacturing parameters. Thickness uniformity is also critical to membrane functionality, so a real-time, full-field transmission densitometer has been created to measure the bi-directional thickness profile of the semi-transparent PEM between 25-400 micrometers. The local thickness of the 75 mm x 100 mm imaged area is determined by converting the optical density of the sample to thickness with the Beer-Lambert law. The PEM extinction coefficient is determined to be 1.4 D/mm and the average thickness error is found to be 4.7%. Finally, the defect inspection and thickness profilometry systems are compiled into a specially-designed graphical user interface for intuitive real-time operation and visualization.M.S.Committee Chair: Tequila Harris; Committee Member: Levent Degertekin; Committee Member: Wayne Dale

Radiometric calibration methods from image sequences

In many computer vision systems, an image of a scene is assumed to directly reflect the scene radiance. However, this is not the case for most cameras as the radiometric response function which is a mapping from the scene radiance to the image brightness is nonlinear. In addition, the exposure settings of the camera are adjusted (often in the auto-exposure mode) according to the dynamic range of the scene changing the appearance of the scene in the images. Vignetting effect which refers to the gradual fading-out of an image at points near its periphery also contributes in changing the scene appearance in images. In this dissertation, I present several algorithms to compute the radiometric properties of a camera which enable us to find the relationship between the image brightness and the scene radiance. First, I introduce an algorithm to compute the vignetting function, the response function, and the exposure values that fully explain the radiometric image formation process from a set of images of a scene taken with different and unknown exposure values. One of the key features of the proposed method is that the movement of the camera is not limited when taking the pictures whereas most existing methods limit the motion of the camera. Then I present a joint feature tracking and radiometric calibration scheme which performs an integrated radiometric calibration in contrast to previous radiometric calibration techniques which require the correspondences as an input which leads to a chicken-and-egg problem as precise tracking requires accurate radiometric calibration. By combining both into an integrated approach we solve this chicken-and-egg problem. Finally, I propose a radiometric calibration method suited for a set of images of an outdoor scene taken at a regular interval over a period of time. This type of data is a challenging problem because the illumination for each image is changing causing the exposure of the camera to change and the conventional radiometric calibration framework cannot be used for this type of data. The proposed methods are applied to radiometrically align images for seamless mosaics and 3D model textures, to create high dynamic range mosaics, and to build an adaptive stereo system