As Machine Vision (MV) and Artificial Intelligence (AI) systems are incorporated to an ever-increasing range of imaging applications, there is a corresponding need for camera measurements that can accurately predict the performance of these systems. At the present time, the standard practice is to separately measure the two major factors, sharpness and noise (or Signal-to-Noise Ratio), along with several additional factors, then to estimate system performance based on a combination of these factors. This estimate is usually based on experience, and is often more of an art than a science. Camera information capacity (C), based on Claude Shannon's ground-breaking work on information theory, holds great promise as a figure of merit for a variety of imaging systems, but it has traditionally been difficult to measure. We describe a new method for measuring camera information capacity that uses the popular slanted-edge test pattern, specified by the ISO 12233:2014/2017 standard. Measuring information capacity requires no extra effort: it essentially comes for free with slanted-edge MTF measurements. C has units of bits per pixel or bits per image for a specified ISO speed and chart contrast, making it easy to compare very different cameras. The new measurement can be used to solve some important problems, such as finding a camera that meets information capacity requirements with a minimum number of pixels, important because fewer pixels mean faster processing as well as lower cost.
Optimizing exposure time for low light scenarios involves a trade-off between motion blur and signal to noise ratio. A method for defining the optimum exposure time for a given function has not been described in the literature. This paper presents the design of a simulation of motion blur and exposure time from the perspective of a real-world camera. The model incorporates characteristics of real-world cameras including the light level (quanta), shot noise and lens distortion. In our simulation, an image quality target chart called the Siemens Star chart will be used, and the simulation outputs a blurred image as if captured from a camera of set exposure and set movement speed. The resulting image is then processed in Imatest in which image quality readings will be extracted from the image and consequently the relationship between exposure time, motion blur and the image quality metrics can be evaluated.
Coating a printed surface with a smooth transparent layer can modify its color. This is due to light interreflections within the coating layer which produce a halo-shaped point spread function. The change of color is related to the coating thickness and the halftone screening used for printing. Thanks to an optical model able to predict the spectral reflectance of the coated print from the one of the non-coated print, we propose to study the impact of the halftone pattern (shape and profile) on the color change caused by the coating layer. It was found that line halftone patterns with a crenel profile induces the strongest changes of color. This is therefore the pattern that we use for an innovative application of this phenomenon: revealing a binary image by adding or removing a coating layer on the print that is originally uniform.
The COVID-19 virus induces infection in both the upper respiratory tract and the lungs. Chest X-ray are widely used to diagnose various lung diseases. Considering chest X-ray and CT images, we explore deep-learning-based models namely: AlexNet, VGG16, VGG19, Resnet50, and Resnet101v2 to classify images representing COVID-19 infection and normal health situation. We analyze and present the impact of transfer learning, normalization, resizing, augmentation, and shuffling on the performance of these models. We explored the vision transformer (ViT) model to classify the CXR images. The ViT model incorporates multi-headed attention to disclose more global information in constrast to CNN models at lower layers. This mechanism leads to quantitatively diverse features. The ViT model renders consolidated intermediate representations considering the training data. For experimental analysis, we use two standard datasets and exploit performance metrics: accuracy, precision, recall, and F1-score. The ViT model, driven by self-attention mechanism and longrange context learning, outperforms other models.
Passive stereo vision systems are useful for estimating 3D geometries from digital images similar to the human biological system. In general, two cameras are situated at a known distance from the object and simultaneously capture images of the same scene from different views. This paper presents a comparative evaluation of 3D geometries of scenes estimated by three disparity estimation algorithms, namely the hybrid stereo matching algorithm (HCS), factor graph-based stereo matching algorithm (FGS), and a multi-resolution FGS algorithm (MR-FGS). Comparative studies were conducted using our stereo imaging system as well as hand-held, consumer-market digital cameras and camera phones of a variety of makes/models. Based on our experimental results, the factor graph algorithm (FGS) and multi-resolution factor graph algorithm (MR-FGS) result in a higher level of 3D reconstruction accuracy than the HCS algorithm. When compared with the FGS algorithm, MR-FGS provides a significant improvement in the disparity contrast along the depth boundaries and minimal depth discontinuities.
