Iterative Refinement of Uniformly Focused Image Set for Accurate Depth from Focus

Estimating the 3D shape of a scene from differently focused set of images has been a practical approach for 3D reconstruction with color cameras. However, reconstructed depth with existing depth from focus (DFF) methods still suffer from poor quality with textureless and object boundary regions. In this paper, we propose an improved depth estimation based on depth from focus iteratively refining 3D shape from uniformly focused image set (UFIS). We investigated the appearance changes in spatial and frequency domains in iterative manner. In order to achieve sub-frame accuracy in depth estimation, optimal location of focused frame in DFF is estimated by fitting a polynomial curve on the dissimilarity measurements. In order to avoid wrong depth values on texture-less regions we propose to build a confidence map and use it to identify erroneous depth estimations. We evaluated our method on public and our own datasets obtained from different types of devices, such as smartphones, medical, and normal color cameras. Quantitative and qualitative evaluations on various test image sets show promising performance of the proposed method in depth estimation.

Color Sensor Accuracy Index Utilizing Metamer Mismatch Radii

A novel method is described for evaluating the colorimetric accuracy of digital color cameras based on a new measure of the metamer mismatch body (MMB) that is induced by the change from the camera as an ‘observer’ to the human standard observer. In comparison to the majority of existing methods for evaluating colorimetric accuracy, the advantage of using the MMB is that it is based on the theory of metamer mismatching and, therefore, shows how much color error can arise in principle. A new measure of colorimetric accuracy based on the shape of the camera-induced MMB is proposed and tested. MMB shape is measured in terms of the moments of inertia of the MMB treated as a mass of uniform density. Since colorimetric accuracy is independent of any linear transformation of the sensor space, the MMB measure needs to be as well. Normalization by the moments of inertia of the object color solid is introduced to provide this independence.

A Plastic Contamination Image Dataset for Deep Learning Model Development and Training

The removal of plastic contamination in cotton lint is an issue of top priority for the U.S. cotton industry. One of the main sources of plastic contamination appearing in marketable cotton bales is plastic used to wrap cotton modules on cotton harvesters. To help mitigate plastic contamination at the gin, automatic inspection systems are needed to detect and control removal systems. Due to significant cost constraints in the U.S. cotton ginning industry, the use of low-cost color cameras for detection of plastic contamination has been successfully adopted. However, some plastics of similar color to background are difficult to detect when utilizing traditional machine learning algorithms. Hence, current detection/removal system designs are not able to remove all plastics and there is still a need for better detection methods. Recent advances in deep learning convolutional neural networks (CNNs) show promise for enabling the use of low-cost color cameras for detection of objects of interest when placed against a background of similar color. They do this by mimicking the human visual detection system, focusing on differences in texture rather than color as the primary detection paradigm. The key to leveraging the CNNs is the development of extensive image datasets required for training. One of the impediments to this methodology is the need for large image datasets where each image must be annotated with bounding boxes that surround each object of interest. As this requirement is labor-intensive, there is significant value in these image datasets. This report details the included image dataset as well as the system design used to collect the images. For acquisition of the image dataset, a prototype detection system was developed and deployed into a commercial cotton gin where images were collected for the duration of the 2018–2019 ginning season. A discussion of the observational impact that the system had on reduction of plastic contamination at the commercial gin, utilizing traditional color-based machine learning algorithms, is also included.

Machine-Readable Pattern for Colorimetric Sensor Interrogation

We present a systematic methodology to generate machine-readable patterns embodying all the elements needed to carry out colorimetric measurements with conventional color cameras in an automated, robust and accurate manner. Our approach relies on the well-stablished machine-readable features of the QR Codes, to detect the pattern, identify the color reference elements and the colorimetric spots, to calibrate the color of the image and to conclude a quantitative measurement. We illustrate our approach with a NH3 colorimetric indicator operating at distinct color temperature ambient lights, demonstrating that with our design, consistent measurements can be achieved, with independence on the illumination conditions.