Automatic segmentation of object region using Graph Cuts based on saliency maps and AdaBoost

Abstract—In conventional methods for region segmentation of objects, the best segmentation results have been obtained by semi-automatic or interactive methods that require a small amount of user input. In this study, we propose a new technique for automatically obtaining segmentation of a flower region by using visual attention (saliency maps) as the prior probability in Graph Cuts. First, AdaBoost determines an approximate flower location using a rectangular window in order to learn the object and background color information using two Gaussian mixture models. We then extract visual attention using saliency maps of the image, and used them as a prior probability of the object model (spatial information). Bayes ’ theorem gives a posterior probability using the prior probability and the likelihood from GMMs, and the posterior probability is used as t-link cost in Graph Cuts, where no manual labeling of image regions is required. The effectiveness of our approach is confirmed by experiments of region segmentation on flower images. I

Automated Quality Assessment of Printed Objects Using Subjective and Objective Methods Based on Imaging and Machine Learning Techniques.

Estimating the perceived quality of printed patterns is a complex task as quality is subjective. A study was conducted to evaluate how accurately a machine learning method can predict human judgment about printed pattern quality. The project was executed in two phases: a subjective test to evaluate the printed pattern quality and development of the machine learning classifier-based automated objective model. In the subjective experiment, human observers ranked overall visual quality. Object quality was compared based on a normalized scoring scale. There was a high correlation between subjective evaluation ratings of objects with similar defects. Observers found the contrast of the outer edge of the printed pattern to be the best distinguishing feature for determining the quality of object. In the second phase, the contrast of the outer print pattern was extracted by flat-fielding, cropping, segmentation, unwrapping and an affine transformation. Standard deviation and root mean square (RMS) metrics of the processed outer ring were selected as feature vectors to a Support Vector Machine classifier, which was then run with optimized parameters. The final objective model had an accuracy of 83%. The RMS metric was found to be more effective for object quality identification than the standard deviation. There was no appreciable difference in using RGB data of the pattern as a whole versus using red, green and blue separately in terms of classification accuracy. Although contrast of the printed patterns was found to be an important feature, other features may improve the prediction accuracy of the model. In addition, advanced deep learning techniques and larger subjective datasets may improve the accuracy of the current objective model

Doctor of Philosophy

dissertationInteractive editing and manipulation of digital media is a fundamental component in digital content creation. One media in particular, digital imagery, has seen a recent increase in popularity of its large or even massive image formats. Unfortunately, current systems and techniques are rarely concerned with scalability or usability with these large images. Moreover, processing massive (or even large) imagery is assumed to be an off-line, automatic process, although many problems associated with these datasets require human intervention for high quality results. This dissertation details how to design interactive image techniques that scale. In particular, massive imagery is typically constructed as a seamless mosaic of many smaller images. The focus of this work is the creation of new technologies to enable user interaction in the formation of these large mosaics. While an interactive system for all stages of the mosaic creation pipeline is a long-term research goal, this dissertation concentrates on the last phase of the mosaic creation pipeline - the composition of registered images into a seamless composite. The work detailed in this dissertation provides the technologies to fully realize interactive editing in mosaic composition on image collections ranging from the very small to massive in scale