A parking assistant system

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 111-114).Parking a car can be very cumbersome and difficult in many circumstances, especially when the rear view of the car is blocked. This thesis designs a prototype for an efficient parking assistant system that aims to facilitate the parking process by providing the drivers with distance information of obstacles in the scene. A video camera mounted on the rear window of a car is used to supply a sequence of input images to the system which then generates a 3-D depth map of the environment. The algorithm proposed in this thesis to reconstruct 3-D distance information from an image sequence involves several steps. First, feature points are extracted in each image using a Curvature Scale Space (CSS) based corner detector. Corresponding feature points are then matched between consecutive frames and tracked through the entire sequence. Using the motion parameters of the vehicle obtained from measuring the angular moment of the steering wheel and the distance information provided by the wheel encoders attached to the rear wheels of the vehicle, the distance of objects in the scene can be reasonably estimated. The algorithm is tested on several sets of real image sequences captured inside a parking lot. Some experimental results are presented and analyzed in detail.by Duangmanee Putthividhya.M.Eng

OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]

Extraction of missing attribute values is to find values describing an attribute of interest from a free text input. Most past related work on extraction of missing attribute values work with a closed world assumption with the possible set of values known beforehand, or use dictionaries of values and hand-crafted features. How can we discover new attribute values that we have never seen before? Can we do this with limited human annotation or supervision? We study this problem in the context of product catalogs that often have missing values for many attributes of interest. In this work, we leverage product profile information such as titles and descriptions to discover missing values of product attributes. We develop a novel deep tagging model OpenTag for this extraction problem with the following contributions: (1) we formalize the problem as a sequence tagging task, and propose a joint model exploiting recurrent neural networks (specifically, bidirectional LSTM) to capture context and semantics, and Conditional Random Fields (CRF) to enforce tagging consistency, (2) we develop a novel attention mechanism to provide interpretable explanation for our model's decisions, (3) we propose a novel sampling strategy exploring active learning to reduce the burden of human annotation. OpenTag does not use any dictionary or hand-crafted features as in prior works. Extensive experiments in real-life datasets in different domains show that OpenTag with our active learning strategy discovers new attribute values from as few as 150 annotated samples (reduction in 3.3x amount of annotation effort) with a high F-score of 83%, outperforming state-of-the-art models.Comment: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK, August 19-23, 201

Towards Effective Extraction and Linking of Software Mentions from User-Generated Support Tickets

Software support tickets contain short and noisy text from the customers. Software products are often represented by various surface forms and informal abbreviations. Automatically identifying software mentions from support tickets and determining the official names and versions are helpful for many downstream applications, \eg routing the support tickets to the right expert groups for support. In this work, we study the problem ofsoftware product name extraction andlinking from support tickets. We first annotate and analyze sampled tickets to understand the language patterns. Next, we design features using local, contextual, and external information sources, for extraction and linking models. In experiments, we show that linear models with the proposed features are able to deliver better and more consistent results, compared with the state-of-the-art baseline models, even on dataset with sparse labels

A family of statistical topic models for text and multimedia documents

In this thesis, we investigate several extensions of the basic Latent Dirichlet Allocation model for text and multimedia documents containing images and texts, video and texts, or audio-video and texts. For exploratory analysis of large-scale text document collections, we present Independent Factor Topic Models (IFTM) which captures topic correlations using linear latent variable models to directly uncover the hidden sources of correlations. Such a framework offers great flexibility in exploring different forms of source prior, and in this work we investigate 2 source distributions: Gaussian and Laplacian. When the sparse source prior is used, we can indeed visualize and give interpretation to the sources of correlations and construct a simple topic graph which can be used to navigate large-scale archives. In extending IFTM to learn correlations between latent topics of different data modalities in multimedia documents, we present a topic-regression multi-modal Latent Dirichlet Allocation (tr-mmLDA) which uses a linear regression module to learn the precise relationships between latent variables in different modalites. We employ tr-mmLDA in an image and video annotation task, where the goal is to learn statistical association between images and their corresponding captions, so that the caption data can be accurately inferred in the test set. When dealing with annotation data that act more similar to class labels, the assumption in tr-mmLDA which allows caption words in the same document to be generated from multiple hidden topics might be overly complex. For such annotation data, we propose a novel statistical topic model called sLDA-bin, which extends supervised Latent Dirichlet Allocation (sLDA) [BM07] model to handle a multi-variate binary response variable of the annotation data. We show superior image annotation and retrieval results comparing sLDA-bin with correspondence LDA [BJ03] on standard image datasets. We also extend the association model for the case of image -text and video-text to perform automatic annotation of multimedia documents containing audio and video, we find that unlike cLDA, tr-mmLDA and sLDA-bin can be straight- forwardly extended to include influence from additional data modalities in predicting annotation by incorporating the latent topics from the additional modality as another set of covariates into the linear and logistic regression module respectivel