ChemInk: A Natural Real-Time Recognition System for Chemical Drawings

We describe a new sketch recognition framework for chemical structure drawings that combines multiple levels of visual features using a jointly trained conditional random field. This joint model of appearance at different levels of detail makes our framework less sensitive to noise and drawing variations, improving accuracy and robustness. In addition, we present a novel learning-based approach to corner detection that achieves nearly perfect accuracy in our domain. The result is a recognizer that is better able to handle the wide range of drawing styles found in messy freehand sketches. Our system handles both graphics and text, producing a complete molecular structure as output. It works in real time, providing visual feedback about the recognition progress. On a dataset of chemical drawings our system achieved an accuracy rate of 97.4%, an improvement over the best reported results in literature. A preliminary user study also showed that participants were on average over twice as fast using our sketch-based system compared to ChemDraw, a popular CAD-based tool for authoring chemical diagrams. This was the case even though most of the users had years of experience using ChemDraw and little or no experience using Tablet PCs.National Science Foundation (U.S.) (Grant 0729422)United States. Dept. of Homeland Security (Graduate Research Fellowship)Pfizer Inc

Combining appearance and context for multi-domain sketch recognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 99-102).As our interaction with computing shifts away from the traditional desktop model (e.g., towards smartphones, tablets, touch-enabled displays), the technology that drives this interaction needs to evolve as well. Wouldn't it be great if we could talk, write, and draw to a computer just like we do with each other? This thesis addresses the drawing aspect of that vision: enabling computers to understand the meaning and semantics of free-hand diagrams. We present a novel framework for sketch recognition that seamlessly combines a rich representation of local visual appearance with a probabilistic graphical model for capturing higher level relationships. This joint model makes our system less sensitive to noise and drawing variations, improving accuracy and robustness. The result is a recognizer that is better able to handle the wide range of drawing styles found in messy freehand sketches. To preserve the fluid process of sketching on paper, our interface allows users to draw diagrams just as they would on paper, using the same notations and conventions. For the isolated symbol recognition task our method exceeds state-of-the-art performance in three domains: handwritten digits, PowerPoint shapes, and electrical circuit symbols. For the complete diagram recognition task it was able to achieve excellent performance on both chemistry and circuit diagrams, improving on the best previous results. Furthermore, in an on-line study our new interface was on average over twice as fast as the existing CAD-based method for authoring chemical diagrams, even for novice users who had little or no experience using a tablet. This is one of the first direct comparisons that shows a sketch recognition interface significantly outperforming a professional industry-standard CAD-based tool.by Tom Yu Ouyang.Ph.D

Learning discriminative models with incomplete data

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 115-121).Many practical problems in pattern recognition require making inferences using multiple modalities, e.g. sensor data from video, audio, physiological changes etc. Often in real-world scenarios there can be incompleteness in the training data. There can be missing channels due to sensor failures in multi-sensory data and many data points in the training set might be unlabeled. Further, instead of having exact labels we might have easy to obtain coarse labels that correlate with the task. Also, there can be labeling errors, for example human annotation can lead to incorrect labels in the training data. The discriminative paradigm of classification aims to model the classification boundary directly by conditioning on the data points; however, discriminative models cannot easily handle incompleteness since the distribution of the observations is never explicitly modeled. We present a unified Bayesian framework that extends the discriminative paradigm to handle four different kinds of incompleteness. First, a solution based on a mixture of Gaussian processes is proposed for achieving sensor fusion under the problematic conditions of missing channels. Second, the framework addresses incompleteness resulting from partially labeled data using input dependent regularization.(cont.) Third, we introduce the located hidden random field (LHRF) that learns finer level labels when only some easy to obtain coarse information is available. Finally the proposed framework can handle incorrect labels, the fourth case of incompleteness. One of the advantages of the framework is that we can use different models for different kinds of label errors, providing a way to encode prior knowledge about the process. The proposed extensions are built on top of Gaussian process classification and result in a modular framework where each component is capable of handling different kinds of incompleteness. These modules can be combined in many different ways, resulting in many different algorithms within one unified framework. We demonstrate the effectiveness of the framework on a variety of problems such as multi-sensor affect recognition, image classification and object detection and segmentation.by Ashish Kapoor.Ph.D

Hand gesture spotting and recognition using HMMs and CRFs in color image sequences

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2010von Mahmoud Othman Selim Mahmoud Elmezai