Color imaging has historically been treated as a phenomenon sufficiently described by three independent parameters. Recent advances in computational resources and in the understanding of the human aspects are leading to new approaches that extend the purely metrological view of color towards a perceptual approach in documents and displays. Part of this perceptual view is the incorporation of spatial aspects, adaptive color processing based on image content, and the automation of color tasks, to name a few. This dynamic nature applies to all output modalities, including hardcopy devices, but to an even larger extent to soft-copy displays with their even larger options of dynamic processing. Spatially adaptive gamut and tone mapping, dynamic contrast, and color management continue to support the unprecedented development of display hardware covering everything from mobile displays to standard monitors, and all the way to large size screens and emerging technologies. This conference provides an opportunity to present, to interact, and to learn about the most recent developments in color imaging researches, technologies and applications. Focus of the conference is on color basic research and testing, color image input, dynamic color image output and rendering, color image automation, emphasizing color in context and color in images, and reproduction of images across local and remote devices. The conference covers also software, media, and systems related to color. Special attention is given to applications and requirements created by and for multidisciplinary fields involving color and/or vision.
The detection of the contaminants in daily food and drinking water is crucial for global public health. For heavy metals detection of Mercury (Hg) and Arsenic (As), our group has proposed a novel paper-based and microfluidic device integrated with a mobile phone and an image analysis pipeline to capture and analyze the sensor images on-site. Still, the detection of lower contamination levels remains challenging due to the small number of available data samples and large intra-class variance of our application. To overcome this challenge, we explore traditional data augmentation and GAN-based augmentation techniques for synthesizing realistic colorimetric images; and we propose a CNN classifier for five-contamination-levels classification. Our proposed system is trained and evaluated on a limited dataset of 126 phone captured images of five contamination levels. Our system yields 88.1% classification accuracy and 91.92% precision, demonstrating the feasibility of this approach. We believe that this approach of training deep learning models on limited detection images datasets presents a clear path toward phone-based contamination-levels detection.
In this paper we investigate applying two deep generative models to digital halftoning with the aim of generating halftones with comparable quality to those generated with the direct binary search (DBS) algorithm. For the first framework, we apply conditional generative adversarial networks (cGANs) using two discriminators with different receptive field size and a generator consisting of densely connected blocks. For the second framework, deep autoregressive (AR) models, we propose mapping input images into a feature space using a single forward pass of a deep neural network and then applying a shallow autoregressive model at the end output. Our methods show promising results; halftones generated with our algorithms are less noisy than those generated with DBS screen and do not contain artifacts commonly associated with error diffusion type algorithms.
A correlation between thin-film nitrate sensor performance and sensor surface texture was hypothesized. Based on this hypothesis, we began research on the application of machine learning methods on thin-film nitrate sensor surface images to predict its performance. This technology would enable real-time optimization adjustments to be made during production to greatly increase the quality of the sensors while reducing costs associated with testing and defective sensors. Recently, we have made progress in the addition of new texture features, repeated crossvalidation methods, and auto-tuning of hyperparameters.
Colorant fading defects in raster region of interest (ROI) are among the most common printing issues in electrophotographic printers. Colorant fading manifests as faint print or customer content and is usually caused by low-level ink/cartridge. This paper presents an accurate method to detect the colorant fading defects and classify the defects based on their severity. There are two modules for this method. The first module uses the SLIC super-pixel method to separate the raster ROI and extract the smooth super-pixels. We design a novel unsupervised clustering algorithm to automatically extract the three or four main colors from the smooth super-pixels. Unsupervised data clustering is an important problem that arises in many image processing applications. We propose a new approach to unsupervised data clustering called Data Inter-Distance MEdiated Clustering (DIDMEC). Our new method is based on analyzing the matrix of Euclidean distances between each pair of points in the data set. Based on three simple properties, we devise an approach that effectively yields the same accuracy as the K-Means algorithm but at a much lower computational cost for a moderate number of sample points. The second module extracts feature vectors for each main color clustered by DIDMEC to classify the colorant fading defects based on their severity.
In our previous paper presented in the last year, we mostly focused on the color saturation problem in our inkjet printer. However, our partner reported that there are some boundary noise pixels on the background, which are quite visible when the background is white. By checking the pipeline of our printing procedure, we realized that the noise stray dots are generated during the halftoning procedure. This paper is dedicated to separate the white background from the foreground, which enables us to constrain the error diffusion process inside the white background. The main idea is to apply image segmentation, which could help us to precisely extract the background.
Gamut mapping algorithms (GMAs) map all the colors within the input image to colors reproducible with a printer. In this paper we discuss a gamut mapping algorithm we’ve developed for a novel inkjet nail printer. The algorithm we used previously for this suffers from visible desaturation since it only exploits the part of the printer gamut that overlaps with the sRGB gamut. To solve this issue, we add a step we call gamut alignment, which enables the printer to fully exploit the entire printer gamut. We show digitally simulated gamut mapped images to illustrate that the proposed GMA indeed produced better saturated gamut mapped images.
Print margin and skew describe an image placed crookedly on the printed page. It is one of the most common defects in electrophotographic printers and dramatically affects print quality. It primarily might occur when using a two-sided printing module on the printer. To solve or correct the print margin and skew error, we should first accurately detect the print margin and skew on the printed page. This paper proposes a method to accurately detect the print margin and skew based on the Hough Lines Detection algorithm. There are three steps of this print margin and skew detection method. We first project the digital master images into the scanned test image with master image edges. The second step is the most challenging part of designing this method because all of our scanned test pages had only two or three and barely visible edges of the printed paper to the naked eye. We use an image processing method and Hough Lines Detection algorithm to extract the paper edges. The third step uses the projected master images edge and the extracted paper edges to calculate the print margin and skew result. Our algorithm is an efficient and accurate method to detect print margin and skew errors based on factual scanned image verification.
Print defect detection and measurement are critical for designing and improving high-quality printing systems. Color plane misregistration is considered to be among the most serious and common defects affecting the overall image quality within the printing industry. There has been some research on it, but mostly focused on measuring the defect on uniform color pages or specific test images. In this paper, we designed and developed a processing pipeline, and two measurement strategies for measuring color plane misregistration. The processing pipeline can automatically determine the direction, magnitude, and identity of the misregistered color plane for a scanned printed customer content image. The entire design pipeline can be directly used on the printed output without prior information on image content, allowing it to be widely used for printer troubleshooting.
In this paper, we introduce a paper-based microfluidic device design that allows liquids to flow at a constant rate through the channels. The device pattern is designed based on a flow rate control test. Our proposed device can be easily manufactured by a wax printer and be printed on filter paper. The primary function of the paper device is to measure the concentration of hazardous chemicals such as heavy metals and bacteria in liquids. We also propose two new image analysis metrics, hue difference and chroma magnitude difference, for analyzing the color of images as a means of identifying the concentration of heavy metals in solutions. In addition, we discuss in detail the image processing pipeline for analyzing the devices from the initial image capturing to segmentation and analysis. This paper also discusses future goals and possible directions to take, such as handling the device from different viewpoints.