The purpose of this research was to study children's ability to recognize partially occluded images. Tasks were constructed which consisted of occluded images from video games, trademarks, and household objects. The tasks were administered to third and sixth grade students at two elementary schools in Arlington, Texas. The researcher discovered no significant differences between the scores of males and females except for the males' higher score on the video game task .

There has been much debate in the history of philosophy aimed at determining what it is, exactly, that makes a person who and what she is. Varying theories have offered a wide range of concepts in pursuit of the answer to this question. Some thinkers, such as B.F. Skinner, have claimed that it is observable behavior patterns that determine who and what a person is. Yet other thinkers, such as Carl Jung, have attributed unconscious motivators as being determinative in deciphering who and what a person is. Jung claims that it is the conscious and unconscious working together that determines who and what a person is. The purpose of this thesis is to discover evidence that supports or disproves the theory of self in which the unconscious and conscious work together to determine who and/or what a person is. This is done by semiotically analyzing the Visual Products (VP) of Visual Product Producers (VPP) who were or are afflicted with Bipolar Disorder. This thesis consists of the semiotic analysis of selected works by Jackson Pollock, Virginia Woolf, Vincent Van Gogh, and myself (Michael Main). Semiotic analysis studies how meanings are generated as opposed to what meanings are generated. It should be noted that semiotics was used strictly as a method of analysis and not as a guiding philosophy. In examining how the works of the selected VPPs generate meaning, it is hoped that evidence is produced that proves or disproves the theory of who or what a person is as determined by the interaction of the conscious and unconscious.

No description available.

No description available.

No description available.

No description available.

DNA Microarray is an innovative tool for gene studies in biomedical research. It is capable of testing and extracting the expression of large number of genes in parallel. Its applications can vary from cancer diagnosis to human identification. A DNA microarray experiment generates an image which has the genetic data embedded in it. Fast, accurate, and automatic routines for processing and compression of these images do not exist. For processing and modelling of micoarray images, we introduce a new, fast and accurate approach in this thesis. A new lossless compression method for microarray images is introduced that provides an average compression ratio of 1.89:1, and that outperforms other lossless compression schemes and the work of other researchers in this field. For the lossy compression, our new method has overcome the rate-distortion curve of JPEG. A new scanning method called spiral path, and a new spatial transform called C2S are introduced in this thesis for lossless and lossy compression of microarray images. / Thesis / Master of Applied Science (MASc)

Interactive deep learning frameworks are crucial for effectively exploring and analyzing complex image datasets in visual analytics. However, existing approaches often face challenges related to inference accuracy and adaptability. To address these issues, we propose ImageSI, a framework integrating deep learning models with semantic interaction techniques for interactive image data analysis. Unlike traditional methods, ImageSI directly incorporates user feedback into the image model, updating underlying embeddings through customized loss functions, thereby enhancing the performance of dimension reduction tasks. We introduce three variations of ImageSI, ImageSI$_{text{MDS}^{-1}}$, prioritizing explicit pairwise relationships from user interaction, and ImageSI$_{text{DRTriplet}}$ and ImageSI$_{text{PHTriplet}}$, emphasizing clustering by defining groups of images based on user input. Through usage scenarios and quantitative analyses centered on algorithms, we demonstrate the superior performance of ImageSI$_{text{DRTriplet}}$ and ImageSI$_{text{MDS}^{-1}}$ in terms of inference accuracy and interaction efficiency. Moreover, ImageSI$_{text{PHTriplet}}$ shows competitive results. The baseline model, WMDS$^{-1}$, generally exhibits lower performance metrics. / Master of Science / Interactive deep learning frameworks are crucial for effectively exploring and analyzing complex image datasets in visual analytics. However, existing approaches often face challenges related to inference accuracy and adaptability. To address these issues, we propose ImageSI, a framework integrating deep learning models with semantic interaction techniques for interactive image data analysis. Unlike traditional methods, ImageSI directly incorporates user feedback into the image model, updating underlying embeddings through customized loss functions, thereby enhancing the performance of dimension reduction tasks. We introduce three variations of ImageSI, ImageSI$_{text{MDS}^{-1}}$, prioritizing explicit pairwise relationships from user interaction, and ImageSI$_{text{DRTriplet}}$ and ImageSI$_{text{PHTriplet}}$, emphasizing clustering by defining groups of images based on user input. Through usage scenarios and quantitative analyses centered on algorithms, we demonstrate the superior performance of ImageSI$_{text{DRTriplet}}$ and ImageSI$_{text{MDS}^{-1}}$ in terms of inference accuracy and interaction efficiency. Moreover, ImageSI$_{text{PHTriplet}}$ shows competitive results. The baseline model, WMDS$^{-1}$, generally exhibits lower performance metrics.

Medical images are extremely complicated to comprehend for a person without expertise. The limited number of practitioners across the globe often face the issue of fatigue due to the high number of cases. This fatigue, physical and mental, can induce human-errors during the diagnosis. In such scenarios, having an additional opinion can be helpful in boosting the confidence of the decision-maker. Thus, it becomes crucial to have a reliable Visual Question Answering (VQA) system which can provide a "second opinion" on medical cases. However, most of the VQA systems that work today cater to real-world problems and are not specifically tailored for handling medical images. Moreover, the VQA system for medical images needs to consider a limited amount of training data available in this domain. In this thesis, we develop a deep learning-based model for VQA on medical images taking the associated challenges into account. Our MedFuseNet system aims at maximizing the learning with minimal complexity by breaking the problem statement into simpler tasks and weaving everything together to predict the answer. We tackle two types of answer prediction - categorization and generation. We conduct an extensive set of both quantitative and qualitative analyses to evaluate the performance of MedFuseNet. Our results conclude that MedFuseNet outperforms other state-of-the-art methods available in the literature for these tasks. / Master of Science / Medical images are extremely complicated to comprehend for a person without expertise. The limited number of practitioners across the globe often face the issue of fatigue due to the high number of cases. This fatigue, physical and mental, can induce human-errors during the diagnosis. In such scenarios, having an additional opinion can be helpful in boosting the confidence of the decision-maker. Thus, it becomes crucial to have a reliable Visual Question Answering (VQA) system which can provide a "second opinion" on medical cases. However, most of the VQA systems that work today cater to real-world problems and are not specifically tailored for handling medical images. In this thesis, we propose an end-to-end deep learning-based system, MedFuseNet, for predicting the answer for the input query associated with the image. We cater to close-ended as well as open-ended type question-answer pairs. We conduct an extensive analysis to evaluate the performance of MedFuseNet. Our results conclude that MedFuseNet outperforms other state-of-the-art methods available in the literature for these tasks.

Many older documents are of poor quality and are deteriorating with the passage of time. Furthermore, many of these documents are currently stored in the form of paper or photographic film, and therefore storage space, accessibility, and security are serious problems. Because of these problems, many older documents are being restored and converted to digital form for archival. Image scanners which perform these tasks must be fast and must produce digital images that are of high quality. A Scan Optimizer was developed by Image Processing Technologies, Inc., to assist in the restoration and conversion processes. This device dramatically improves the throughput of image scanners by the use of high-speed complicated image processing. As originally designed, however, the IPT Scan Optimizer requires the manual adjustment of several parameters to obtain the best results for a given document. Because of this manual adjustment, human intervention was often required, and conversion speeds were drastically reduced. In order to remove the need for manual adjustments, an Automatic Parameter Setting (APS) algorithm was developed. This algorithm relies on histogram analysis over non overlapping image regions for the computation of scan parameters. This thesis describes a hardware realization of the APS algorithm. To achieve a real-time implementation that is low in cost, several compromises were necessary in the design. This two-board set is compatible with the AT bus, and is composed of one SPARC RISC chip, four Xilinx Programmable Logic Cell Arrays, ten PALs and many RAMs and supporting logic. This system has been designed, built, and successfully tested with many documents. / Master of Science