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At the age of multi-media, portable electronic devices such as mobile phones, personal digital assistant and handheld gaming systems have increased the demand for high performance displays with low cost production. Inkjet printing color optical filters (COF) for LCD applications seem to be an interesting alternative to decrease the production costs. The advantage of inkjet printing technology is to be fast, accurate, easy to run and cheaper than other technologies. In this master thesis work, we used various disciplines such as optical microscopy, rheology, inkjet printing, profilometering and colorimetry. The specific aim of the thesis was to investigate the feasibility of using company-A pigment formulation in inkjet production of COF for active matrix LCD applications. Ideal viscosity parameters were determined from 10 to 20mPa·s for easy inkjet printing at room temperature. The red pigments used are fully dispersed into the solvent and present an excellent homogenous repartition after printing. Thickness investigations revealed that the printed COF were equal or slightly thicker than typically manufactured ones. The colorimetry investigations demonstrated color coordinates very close to the NTSC red standard. LED backlighting seems to be a valuable solution to combine with the printed COF regarding to the spectrum and color analysis. The results on this thesis will increase the understanding of inkjet printing company-A pigments to produce COF for LCD applications.

The purpose of this thesis is to develop a working methodology to color a grey scale image. This thesis is based on approach of using a colored reference image. Coloring grey scale images has no exact solution till date and all available methods are based on approximation. This technique of using a color reference image for approximating color information in grey scale image is among most modern techniques.Method developed here in this paper is better than existing methods of approximation of color information addition in grey scale images in brightness, sharpness, color shade gradients and distribution of colors over objects.Color and grey scale images are analyzed for statistical and textural features. This analysis is done only on basis of luminance value in images. These features are then segmented and segments of color and grey scale images are mapped on basis of distances of segments from origin. Then chromatic values are transferred between these matched segments from color image to grey scale image.Technique proposed in this paper uses better mechanism of mapping clusters and mapping colors between segments, resulting in notable improvement in existing techniques in this category.

Thesis (M.A.)--York University, 2008. Graduate Programme in Higher Education. / Typescript. Includes bibliographical references (leaves 42-50). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR45962

The car has increasingly become more and more intelligent throughout the years. Today's radar and vision based safety systems can warn a driver and brake the vehicle automatically if obstacles are detected. Research projects such as the Google Car have even succeeded in creating fully autonomous cars. The demands to obtain the highest rating in safety tests such as Euro NCAP are also steadily increasing, and as a result, the development of these systems have become more attractive for car manufacturers. In the near future, a car must have a system for detecting, and performing automatic braking for pedestrians to receive the highest safety rating of five stars. The prospect is that the volume of active safety system will increase drastically when the car manufacturers start installing them in not only luxury cars, but also in the regularly priced ones. The use of automatic braking comes with a high demand on the performance of active safety systems, false positives must be avoided at all costs. Dollar et al. [2014] introduced Aggregated Channel Features (ACF) which is based on a 10-channel LUV+HOG feature map. The method uses decision trees learned from boosting and has been shown to outperform previous algorithms in object detection tasks. The rediscovery of neural networks, and especially Convolutional Neural Networks (CNN) has increased the performance in almost every field of machine learning, including pedestrian detection. Recently Yang et al.[2015] combined the two approaches by using the the feature maps from a CNN as input to a decision tree based boosting framework. This resulted in state of the art performance on the challenging Caltech pedestrian data set. This thesis presents an approach to improve the performance of a cascade of boosted classifiers by investigating the impact of using color information for pedestrian detection. The color self similarity feature introduced by Walk et al.[2010] was used to create a version better adapted for boosting. This feature is then used in combination with a gradient based feature at the last step of a cascade. The presented feature increases the performance compared to currently used classifiers at Autoliv, on data recorded by Autoliv and on the benchmark Caltech pedestrian data set. / Bilen har genom åren kommit att bli mer och mer intelligent. Dagens radar- och kamerabaserade säkerhetssystem kan varna och bromsa bilen automatiskt om hider detekteras. Forskningsprojekt såsom Google Car har t.o.m lyckats köra bilar helt autonomt. Kraven för att uppnå den högsta säkerhetsklassningen i t.ex. Euro NCAP blir allt strängare i takt med att dessa system utvecklas och som följd har dessa system blivit attraktivare för biltillverkare. Inom en snart framtid kommer det att krävas att en bil har ett system för att upptäcka och att bromsa automatiskt för fotgängare för att uppnå den högsta klassen, fem stjärnor. Förutsikterna är att produktionsvolymer för aktiva säkerhetsytem kommer att öka drastiskt när biltillverkarna börjar utrusta vanliga bilar och inte enbart lyxmodeller med dessa system. Användningen av aktiv bromsning ställer höga krav på prestanda, felakting aktivering av system måste i högsta grad undvikas. Dollar et al. [2014] presenterade Aggregated Channel Features (ACF) som baseras på en tiokanalig LUV+HOG särdragskarta. Metoden använder beslutsträd på pixelnivå som tas fram genom boosting och överträffade tidigare algoritmer för objektigenkänning. Återupptäkten av neurala nätverker och i synnerlighet Convolutional Neural Networks (CNN) har medfört en ökning i prestanda inom nästan alla fält av maskininlärning, inklusive fotgängardetektion. Nyligen kombinerades dessa två metoder av Yang et al.[2015] genom att särdragskartan från ett CNN användes som insignal till ett beslutsträdsbaserat boostingramverk. Detta ledde till det hittills bästa resultatet på det utmanande Caltech pedestrian dataset. I det här examensarbetet presenteras en metod som kan öka prestandan för en kaskad av boostingklassificerare ämnad för fotgängardetektion. Det färgbaserad särdraget color self similarity, Walk et al.[2010], används för att skapa en version som är bättre lämpad för boosting. Det presenterade särdraget ökade prestandan jämfört med befintliga klassificerare som används av Autoliv på både data inspelat av Autoliv och på Caltech pedestrian dataset.

Optical see-through display (OSTD) is a transparent digital display which simultaneously gives access to the digital contents and the real world objects behind it. Additive optical see-though display is a hardware subtype of OSTD which has its own light source to create the digital contents. In Additive OSTD, light coming from background objects mixes with the light originating from the display causing what is known as the color blending problem. The work in this thesis provides a solution to the color blending problem. In order to understand the problem, this thesis first presents a new color blending model for additive OSTD based on two display induced distortions: the Render distortion and the Material distortion. A new method called Binned Profile (BP) method which accounts for the render distortion is developed to predict the blended color, when applied on the color blending model. BP method is validated with other known methods and is shown to be the most accurate in predicting the color blends with 9 just noticeable differences (JND) in worst case. Based on the BP method, a new color correction algorithm called BP color correction is created to solve the color blending problem. BP-color correction finds the alternative digital color to counter balance the blending. The correction capacity of various digital colors were analysed using the BP color correction approach. BP color correction is also compared and proven to be better than the existing solution. A quicker version of the correction called quick correction is also explored. The thesis concludes with an exploration of the material distortion, explains the limitations of BP-correction, provides design recommendations .

Queer people and people of color are two groups that are exposed to much stereotyping and discrimination in the United States. When these two identity labels coincide they sometimes conflict. In this study, five queer persons of color were interviewed on their identities and their life stories, to find out how they create their identities through narratives, negotiating and rewriting the meanings of social categories. Using Johnson's Quare term as inspiration,and analyzing the data with the use of Riessman's performative narrative analysis and Muñoz's Disidentifications, I find several common tropes of identity creation and performance as well as practices of resistance and disidentification. I then discuss the word Queer as used by respondents to label practices and attitudes that can be considered disidentifying.

Optical see-through display (OSTD) is a transparent digital display which simultaneously gives access to the digital contents and the real world objects behind it. Additive optical see-though display is a hardware subtype of OSTD which has its own light source to create the digital contents. In Additive OSTD, light coming from background objects mixes with the light originating from the display causing what is known as the color blending problem. The work in this thesis provides a solution to the color blending problem. In order to understand the problem, this thesis first presents a new color blending model for additive OSTD based on two display induced distortions: the Render distortion and the Material distortion. A new method called Binned Profile (BP) method which accounts for the render distortion is developed to predict the blended color, when applied on the color blending model. BP method is validated with other known methods and is shown to be the most accurate in predicting the color blends with 9 just noticeable differences (JND) in worst case. Based on the BP method, a new color correction algorithm called BP color correction is created to solve the color blending problem. BP-color correction finds the alternative digital color to counter balance the blending. The correction capacity of various digital colors were analysed using the BP color correction approach. BP color correction is also compared and proven to be better than the existing solution. A quicker version of the correction called quick correction is also explored. The thesis concludes with an exploration of the material distortion, explains the limitations of BP-correction, provides design recommendations .

Thesis (M.S.)--Rochester Institute of Technology, 1991. / Typescript. Includes bibliographical references (leaf 35).

Thesis (M.S.)--Rochester Institute of Technology, 1992. / Typescript. Includes bibliographical references (leaves 76-80).