Studies on Imaging System and Machine Learning: 3D Halftoning and Human Facial Landmark Localization

In this dissertation, studies on digital halftoning and human facial landmark localization will be discussed. 3D printing is becoming increasingly popular around the world today. By utilizing 3D printing technology, customized products can be manufactured much more quickly and efficiently with much less cost. However, 3D printing still suffers from low-quality surface reproduction compared with 2D printing. One approach to improve it is to develop an advanced halftoning algorithm for 3D printing. In this presentation, we will describe a novel method to 3D halftoning that can cooperate with 3D printing technology in order to generate a high-quality surface reproduction. In the second part of this report, a new method named direct element swap to create a threshold matrix for halftoning is proposed. This method directly swaps the elements in a threshold matrix to find the best element arrangement by minimizing a designated perceived error metric. Through experimental results, the new method yields halftone quality that is competitive with the conventional level-by-level matrix design method. Besides, by using direct element swap method, for the first time, threshold matrix can be designed through being trained with real images. In the second part of the dissertation, a novel facial landmark detection system is presented. Facial landmark detection plays a critical role in many face analysis tasks. However, it still remains a very challenging problem. The challenges come from the large variations of face appearance caused by different illuminations, different facial expressions, different yaw, pitch and roll angles of heads and different image qualities. To tackle this problem, a novel coarse-to-fine cascaded convolutional neural network system for robust facial landmark detection of faces in the wild is presented. The experiment result shows our method outperforms other state-of-the-art methods on public test datasets. Besides, a frontal and profile landmark localization system is proposed and designed. By using a frontal/profile face classifier, either frontal landmark configuration or profile landmark configuration is employed in the facial landmark prediction based on the input face yaw angle

Black-box printer models and their applications

In the electrophotographic printing process, the deposition of toner within the area of a given printer addressable pixel is strongly influenced by the values of its neighboring pixels. The interaction between neighboring pixels, which is commonly referred to as dot-gain, is complicated. The printer models which are developed according to a pre-designed test page can either be embedded in the halftoning algorithm, or used to predict the printed halftone image at the input to an algorithm being used to assess print quality. In our research, we examine the potential influence of a larger neighborhood (45?45) of the digital halftone image on the measured value of a printed pixel at the center of that neighborhood by introducing a feasible strategy for the contribution. We developed a series of six models with different accuracy and computational complexity to account for local neighborhood effects and the influence of a 45?45 neighborhood of pixels on the central printer-addressable pixel tone development. All these models are referred to as Black Box Model (BBM) since they are based solely on measuring what is on the printed page, and do not incorporate any information about the marking process itself. We developed two different types of printer models Standard Definition (SD) BBM and High Definition (HD) BBM with capture device Epson Expression 10000XL (Epson America, Inc., Long Beach, CA, USA) flatbed scanner operated at 2400 dpi under different analysis resolutions. The experiment results show that the larger neighborhood models yield a significant improvement in the accuracy of the prediction of the pixel values of the printed halftone image. The sample function generation black box model (SFG-BBM) is an extension of SD-BBM that adds the printing variation to the mean prediction to improve the prediction by more accurately matching the characteristics of the actual printed image. We also followed a structure similar to that used to develop our series of BBMs to develop a two-stage toner usage predictor for electrophotographic printers. We first obtained on a pixel-by-pixel basis, the predicted absorptance of printed and scanned page with the digital input using BBM. We then form a weighted sum of these predicted pixel values to predict overall toner usage on the printed page. Our two-stage predictor significantly outperforms existing method that is based on a simple pixel counting strategy, in terms of both accuracy and robustness of the prediction