Based on the author's thesis (Ph. D.)--State University of New York at Stony Brook, 1999. / DatabaseACLS Humanities E-Book. This volume is made possible by a grant from the Andrew W. Mellon Foundation. Caption title; description based on screen of 2008-02-03. Originally published by Gutenberg-e: www.gutenberg-e.org.

Based on the author's thesis (Ph. D.)--State University of New York at Stony Brook, 1999. / Caption title; description based on screen of 2008-02-03. Originally published by Gutenberg-e: www.gutenberg-e.org. Includes bibliographical references.

Based on the author's thesis (Ph. D.)--State University of New York at Stony Brook, 1999. / Title from title screen (viewed October 6, 2006). "Gutenberg-e is a series of award-winning digital monographs in history, selected by the American Historical Association and published by Columbia University Press." Includes bibliographical references.

Based on the author's thesis (Ph. D.)--State University of New York at Stony Brook, 1999. / Title from title screen (viewed October 6, 2006). "Gutenberg-e is a series of award-winning digital monographs in history, selected by the American Historical Association and published by Columbia University Press." Includes bibliographical references.

Based on the author's thesis (Ph. D.)--State University of New York at Stony Brook, 1999. / Caption title; description based on screen of 2008-02-03. Originally published by Gutenberg-e: www.gutenberg-e.org. Includes bibliographical references.

<div>Machine learning has been revolutionizing our approach to image understanding problems. However, due to the unique nature of the problem, finding suitable data or learning from limited data properly is a constant challenge. In this work, we focus on building machine learning pipelines for fashion and inkjet image analysis with limited data. </div><div><br></div><div>We first look into the dire issue of missing and incorrect information on online fashion marketplace. Unlike professional online fashion retailers, sellers on P2P marketplaces tend not to provide correct color categorical information, which is pivotal for fashion shopping. Therefore, to assist users to provide correct color information, we aim to build an image understanding pipeline that can extract garment region in the fashion image and match the color of the fashion item to a pre-defined color categories on the fashion marketplace. To cope with the challenges of lack of suitable data, we propose an autonomous garment color extraction system that uses both clustering and semantic segmentation algorithm to extract the identify fashion garments in the image. In addition, a psychophysical experiment is designed to collect human subjects' color naming schema, and a random forest classifier is trained to given close prediction of color label for the fashion item. Our system is able to perform pixel level segmentation on fashion product portraits and parse human body parts and various fashion categories with human presence. </div><div><br></div><div>We also develop an inkjet printing analysis pipeline using pre-trained neural network. Our pipeline is able to learn to perceive print quality, namely high frequency noise level, of the test targets, without intense training. Our research also suggests that in spite of being trained on large scale dataset for object recognition, features generated from neural networks reacts to textural component of the image without any localized features. In addition, we expand our pipeline to printer forensics, and the pipeline is able to identify the printer model by examining the inkjet dot pattern at a microscopic level. Overall, the data-driven computer vision approach presents great value and potential to improve future inkjet imaging technology, even when the data source is limited.</div>

No description available.

Stereocomplex poly(methyl methacrylate) (PMMA) fibers for the purpose of reinforcing PMMA materials were developed. These kinds of composites are known as "self-reinforced" composites. We were successful in producing stereocomplex PMMA fibers with three different methods - wet spinning, gel spinning and electrospinning. Gel spinning and electrospinning produced the most crystalline fibers. Steroecomplex PMMA fibers were further shown to be resistant to high temperature and also to hot monomer solvent during bulk polymerization. We further describe our efforts in characterization, replication and mimicry of structural color features of butterflies and beetles. We have developed a simple method of characterizing the bidirectional reflectance distribution function of microscopic objects such as butterfly wing scales. We used this method to characterize nanometer sized structural color features resulting from the replication of butterfly Morpho rhetenor, mimickry of butterfly Papilio palinurus and also the native structural color features of iridescent beetle Chrysina gloriosa, which were shown to be cholesteric focal conic defects lined on the surface.

Hear Color and See Sound - What happens when I combine flamenco guitar and film in a live performance? This master thesis explores the combination of two art forms. The purpose is to use live projections to enhance my guitar composition with movie. My research question is: what happens when I combine flamenco guitar and film in a live performance? I explore if visual qualities add a deeper meaning to my instrumental music by experimenting with color science and video telling/editing techniques. I’ve tested through surveys and live performances if the audience perceive my music differently, by showing contrasting footage or footage that synchronize with my musical compositions. Resulting in an exam concert where I control the prerecorded video compositions with a touch screen interface through the video-jockey software Resolume, while also performing the music (which have been timed for the videos) live together with a flamenco singer and dancer.

本研究提出一創新的旅遊推薦服務系統，以意象模型作為旅客意象(包含自我意象和情感需求)、景點意象、以及中小企業所提供服務之意象在系統裡的一致性表達。以上所提及之利益關係人的意象會經由數個系統模組進行建立與管理，並演化以反映出意象擁有者在真實世界的狀態。除此之外，本系統為動態運行，強調旅遊產業裡各個利益關係人角色之間的互動關係。每當互動發生，相關意象模型會進行混合，演繹出額外的意象屬性，以進行意象模型之調整。另外，基於顏色與情緒可相互對應的相關研究，我們將色彩理論運用於意象媒合與意象混合模組之中，藉此為旅客推薦符合其情感需求的旅遊景點或服務。本研究所提出一系列基於意象衍伸的系統化方法，可被應用於各種不同的領域。我們相信本研究可以為其它領域之實務應用與學術探討帶來顯著的貢獻。 / This research presents a recommendation service system that considers the image as a uniform representation of tourist images (include self-image and emotional needs), destinations, and local SMEs. Images carried by each stakeholder roles are modeled and managed by several system modules, and they also evolve to reflect the real time situations of each entity. In addition, the system is dynamic in terms of its emphasis on the relationships among these roles. When interactions occur, image mixing will be conducted to derive extra image attributes for the adjustments of the images. Besides, since colors can be mapped onto emotions, we use colors to operate the image matching and mixing process to find good matches of destinations for the recommendation. This image related approach we proposed is domain-independent. We believe our method could contribute to other areas of practical applications and academic studies.