Browsing by Subject "Recognition"

Now showing 1 - 3 of 3
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    Functional neuroanatomical correlates of episodic memory impairment in early phase psychosis
    (SpringerNature, 2016-03) Francis, Michael Matthew; Hummer, Tom A.; Vohs, Jenifer L.; Yung, Matthew G.; Liffick, Emily; Mehdiyoun, Nicole F.; Radnovich, Alexander J.; McDonald, Brenna C.; Saykin, Andrew J.; Breier, Alan; Department of Psychiatry, IU School of Medicine
    Studies have demonstrated that episodic memory (EM) is often preferentially disrupted in schizophrenia. The neural substrates that mediate EM impairment in this illness are not fully understood. Several functional magnetic resonance imaging (fMRI) studies have employed EM probe tasks to elucidate the neural underpinnings of impairment, though results have been inconsistent. The majority of EM imaging studies have been conducted in chronic forms of schizophrenia with relatively few studies in early phase patients. Early phase schizophrenia studies are important because they may provide information regarding when EM deficits occur and address potential confounds more frequently observed in chronic populations. In this study, we assessed brain activation during the performance of visual scene encoding and recognition fMRI tasks in patients with earlyphase psychosis (n = 35) and age, sex, and race matched healthy control subjects (n = 20). Patients demonstrated significantly lower activation than controls in the right hippocampus and left fusiform gyrus during scene encoding and lower activation in the posterior cingulate, precuneus, and left middle temporal cortex during recognition of target scenes. Symptom levels were not related to the imaging findings, though better cognitive performance in patients was associated with greater right hippocampal activation during encoding. These results provide evidence of altered function in neuroanatomical circuitry subserving EM early in the course of psychotic illness, which may have implications for pathophysiological models of this illness.
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    Machine Vision Assisted In Situ Ichthyoplankton Imaging System
    (2013-07-12) Iyer, Neeraj; Tsechpenakis, Gavriil; Raje, Rajeev; Tuceryan, Mihran; Fang, Shiaofen
    Recently there has been a lot of effort in developing systems for sampling and automatically classifying plankton from the oceans. Existing methods assume the specimens have already been precisely segmented, or aim at analyzing images containing single specimen (extraction of their features and/or recognition of specimens as single targets in-focus in small images). The resolution in the existing systems is limiting. Our goal is to develop automated, very high resolution image sensing of critically important, yet under-sampled, components of the planktonic community by addressing both the physical sensing system (e.g. camera, lighting, depth of field), as well as crucial image extraction and recognition routines. The objective of this thesis is to develop a framework that aims at (i) the detection and segmentation of all organisms of interest automatically, directly from the raw data, while filtering out the noise and out-of-focus instances, (ii) extract the best features from images and (iii) identify and classify the plankton species. Our approach focusses on utilizing the full computational power of a multicore system by implementing a parallel programming approach that can process large volumes of high resolution plankton images obtained from our newly designed imaging system (In Situ Ichthyoplankton Imaging System (ISIIS)). We compare some of the widely used segmentation methods with emphasis on accuracy and speed to find the one that works best on our data. We design a robust, scalable, fully automated system for high-throughput processing of the ISIIS imagery.
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    Protein Fold Recognition Using Adaboost Learning Strategy
    Su, Yijing
    Protein structure prediction is one of the most important and difficult problems in computational molecular biology. Unlike sequence-only comparison, protein fold recognition based on machine learning algorithms attempts to detect similarities between protein structures which might not be accompanied with any significant sequence similarity. It takes advantage of the information from structural and physic properties beyond sequence information. In this thesis, we present a novel classifier on protein fold recognition, using AdaBoost algorithm that hybrids to k Nearest Neighbor classifier. The experiment framework consists of two tasks: (i) carry out cross validation within the training dataset, and (ii) test on unseen validation dataset, in which 90% of the proteins have less than 25% sequence identity in training samples. Our result yields 64.7% successful rate in classifying independent validation dataset into 27 types of protein folds. Our experiments on the task of protein folding recognition prove the merit of this approach, as it shows that AdaBoost strategy coupling with weak learning classifiers lead to improved and robust performance of 64.7% accuracy versus 61.2% accuracy in published literatures using identical sample sets, feature representation, and class labels.