The doctoral dissertation represents the culmination of the entire graduate school experience. It is a snapshot of all that a student has accomplished and learned about their dissertation topics. While we could post these on our publications page, we feel that they deserve a page of their own. Incorporating boltzmann priors for semantic labeling in images and videos by andrew kae, may 2014. Pdf semantic labeling is the task of assigning category labels to regions in an image. For example, a scene may consist of regions corresponding to categories such as sky, water, and ground, or parts of a face such as eyes, nose, and mouth.

Semantic labeling is an important mid level vision task for grouping and organizing image regions into coherent parts. Labeling these regions allows us to better understand the scene itself as well as properties of the objects in the scene, such as their parts, location, and interaction within the scene. Typical approaches for this task include the conditional random field crf , which is well suited to modeling local interactions among adjacent image regions. However the crf is limited in dealing with complex, global long range interactions between regions in an image, and between frames in a video. This thesis presents approaches to modeling long range interactions within images and videos, for use in semantic labeling. Unsupervised joint alignment, clustering and feature learning by marwan mattar, may 2014. Pdf joint alignment is the process of transforming instances in a data set to make them more similar based on a pre defined measure of joint similarity.

This process has great utility and applicability in many scientific disciplines including radiology, psychology, linguistics, vision, and biology. First, they typically fail when presented with complex data sets arising from multiple modalities such as a data set of normal and abnormal heart signals. Second, they require hand picking appropriate feature representations for each data set, which may be time consuming and ineffective, or outside the domain of expertise for practitioners. In the first part, we present an efficient curve alignment algorithm derived from the congealing framework that is effective on many synthetic and real data sets. We show that using the byproducts of joint alignment, the aligned data and transformation parameters, can dramatically improve classification performance. In the second part, we incorporate unsupervised feature learning based on convolutional restricted boltzmann machines to learn a representation that is tuned to the statistics of the data set.

We show how these features can be used to improve both the alignment quality and classification performance. In the third part, we present a nonparametric bayesian joint alignment and clustering model which handles data sets arising from multiple modes. We apply this model to synthetic, curve and image data sets and show that by simultaneously aligning and clustering, it can perform significantly better than performing these operations sequentially. It also has the added advantage that it easily lends itself to semi supervised, online, and distributed implementations. Overall this thesis takes steps towards developing an unsupervised data processing pipeline that includes alignment, clustering and feature learning. While clustering and feature learning serve as auxiliary information to improve alignment, they are important byproducts. Furthermore, we present a software implementation of all the models described in this thesis.

This will enable practitioners from different scientific disciplines to utilize our work, as well as encourage contributions and extensions, and promote reproducible research. Pdf the area of scene text recognition focuses on the problem of recognizing arbitrary text in images of natural scenes. Examples of scene text include street signs, business signs, grocery item labels, and license plates.

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With the increased use of smartphones and digital cameras, the ability to accurately recognize text in images is becoming increasingly useful and many people will benefit from advances in this area. The goal of this thesis is to develop methods for improving scene text recognition. We do this by incorporating new types of information into models and by exploring how to compose simple components into highly effective systems. We focus on three areas of scene text recognition, each with a decreasing number of prior assumptions. First, we introduce two techniques for character recognition, where word and character bounding boxes are assumed. We describe a character recognition system that incorporates similarity information in a novel way and a new language model that models syllables in a word to produce word labels that can be pronounced in english.