Browsing by Thesis Advisor "Zare, Alina"
Now showing items 1-12 of 12
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Accounting for spectral variability in hyperspectral unmixing using beta endmember distributions
(University of Missouri--Columbia, 2013)Hyperspectral imaging is widely used in the field of remote sensing (Goetz, et al., 1985; Green, et al., 1998). In a hyperspectral imaging system, sensors collect radiance/reflectance values over an area (or a scene) across ... -
Hyperspectral unmixing and band weighting for multiple endmember sets
(University of Missouri--Columbia, 2014)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Imaging spectrometers measure the response from materials across the electromagnetic spectrum. Often, in remote sensing applications, the imaging ... -
Landmine classification using possibilistic K-nearest neighbors with wideband electromagnetic induction data
(University of Missouri--Columbia, 2012)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] In this thesis, a possibilistic K-nearest neighbor classifier is presented to distinguish between and classify mine and non-mine targets on data ... -
Map-guided hyperspectral image superpixel segmentation using semi-supervised partial membership latent Dirichlet allocation
(University of Missouri--Columbia, 2016)Many superpixel segmentation algorithms which are suitable for the regular color images like images with three channels: red, green and blue (RGB images) have been developed in the literature. However, because of the high ... -
Multi-camera high-throughput plant root phenotyping system
(University of Missouri--Columbia, 2016)Plant root phenotyping is a key component in plant breeding and selection for desireable root properties. Preferable root traits can not only help a plant to grow faster but also allow for more dense and deep root system ... -
Multiple Instance Choquet Integral for multiresolution sensor fusion
(University of Missouri--Columbia, 2017)Imagine you are traveling to Columbia, MO for the first time. On your flight to Columbia, the woman sitting next to you recommended a bakery by a large park with a big yellow umbrella outside. After you land, you need ... -
Partial membership latent Dirichlet allocation
(University of Missouri--Columbia, 2016)For many years, topic models (e.g., pLSA, LDA, SLDA) have been widely used for segmenting and recognizing objects in imagery simultaneously. However, these models are confined to the analysis of categorical data, forcing ... -
Semi-supervised interactive unmixing for hyperspectral image analysis
(University of Missouri--Columbia, 2016)In the past several decades, hyperspectral imaging has drawn a lot of attention in the field of remote sensing. Yet, due to low spatial resolutions of hyperspectral imagers, often the response from more than one surface ... -
Target concept learning from ambiguously labeled data
(University of Missouri--Columbia, 2017)The multiple instance learning problem addresses the case where training data comes with label ambiguity, i.e., the learner has access only to inaccurately labeled data. For example, in target detection from remotely sensed ... -
Task driven extended functions of multiple instances (TD-eFUMI)
(University of Missouri--Columbia, 2015)Dictionary learning techniques have proven to be a powerful method in the pattern recognition literature. Recently supervised dictionary learning has been used to achieve very good results on a number of different data ... -
Three dimensional reconstruction of plant roots via low energy x-ray computed tomography
(University of Missouri--Columbia, 2018)Plant roots are vital organs for water and nutrient uptake. The structure and spatial distribution of plant roots in the soil affects a plant's physiological functions such as soil-based resource acquisition, yield and its ... -
Vehicle detection using morphological shared-weight neural network in the multiple instance learning framework
(University of Missouri--Columbia, 2017)In this thesis, we design and implement an algorithm for object detection in aerial images based on the morphological shared-weight neural network (MSNN). The multiple instance learning (MIL) framework is used to avoid the ...