A Type System For Safe SN Resource Allocation

snBench is a platform on which novice users compose and deploy distributed Sense and Respond programs for simultaneous execution on a shared, distributed infrastructure. It is a natural imperative that we have the ability to (1) verify the safety/correctness of newly submitted tasks and (2) derive the resource requirements for these tasks such that correct allocation may occur. To achieve these goals we have established a multi-dimensional sized type system for our functional-style Domain Specific Language (DSL) called Sensor Task Execution Plan (STEP). In such a type system data types are annotated with a vector of size attributes (e.g., upper and lower size bounds). Tracking multiple size aspects proves essential in a system in which Images are manipulated as a first class data type, as image manipulation functions may have specific minimum and/or maximum resolution restrictions on the input they can correctly process. Through static analysis of STEP instances we not only verify basic type safety and establish upper computational resource bounds (i.e., time and space), but we also derive and solve data and resource sizing constraints (e.g., Image resolution, camera capabilities) from the implicit constraints embedded in program instances. In fact, the static methods presented here have benefit beyond their application to Image data, and may be extended to other data types that require tracking multiple dimensions (e.g., image "quality", video frame-rate or aspect ratio, audio sampling rate). In this paper we present the syntax and semantics of our functional language, our type system that builds costs and resource/data constraints, and (through both formalism and specific details of our implementation) provide concrete examples of how the constraints and sizing information are used in practice.

Active Voodoo Dolls: A Vision Based Input Device for Nonrigid Control

A vision based technique for non-rigid control is presented that can be used for animation and video game applications. The user grasps a soft, squishable object in front of a camera that can be moved and deformed in order to specify motion. Active Blobs, a non-rigid tracking technique is used to recover the position, rotation and non-rigid deformations of the object. The resulting transformations can be applied to a texture mapped mesh, thus allowing the user to control it interactively. Our use of texture mapping hardware allows us to make the system responsive enough for interactive animation and video game character control.

SymbolDesign: A User-centered Method to Design Pen-based Interfaces and Extend the Functionality of Pointer Input Devices

A method called "SymbolDesign" is proposed that can be used to design user-centered interfaces for pen-based input devices. It can also extend the functionality of pointer input devices such as the traditional computer mouse or the Camera Mouse, a camera-based computer interface. Users can create their own interfaces by choosing single-stroke movement patterns that are convenient to draw with the selected input device and by mapping them to a desired set of commands. A pattern could be the trace of a moving finger detected with the Camera Mouse or a symbol drawn with an optical pen. The core of the SymbolDesign system is a dynamically created classifier, in the current implementation an artificial neural network. The architecture of the neural network automatically adjusts according to the complexity of the classification task. In experiments, subjects used the SymbolDesign method to design and test the interfaces they created, for example, to browse the web. The experiments demonstrated good recognition accuracy and responsiveness of the user interfaces. The method provided an easily-designed and easily-used computer input mechanism for people without physical limitations, and, with some modifications, has the potential to become a computer access tool for people with severe paralysis.

Spatiotemporal Gesture Segmentation

Spotting patterns of interest in an input signal is a very useful task in many different fields including medicine, bioinformatics, economics, speech recognition and computer vision. Example instances of this problem include spotting an object of interest in an image (e.g., a tumor), a pattern of interest in a time-varying signal (e.g., audio analysis), or an object of interest moving in a specific way (e.g., a human's body gesture). Traditional spotting methods, which are based on Dynamic Time Warping or hidden Markov models, use some variant of dynamic programming to register the pattern and the input while accounting for temporal variation between them. At the same time, those methods often suffer from several shortcomings: they may give meaningless solutions when input observations are unreliable or ambiguous, they require a high complexity search across the whole input signal, and they may give incorrect solutions if some patterns appear as smaller parts within other patterns. In this thesis, we develop a framework that addresses these three problems, and evaluate the framework's performance in spotting and recognizing hand gestures in video. The first contribution is a spatiotemporal matching algorithm that extends the dynamic programming formulation to accommodate multiple candidate hand detections in every video frame. The algorithm finds the best alignment between the gesture model and the input, and simultaneously locates the best candidate hand detection in every frame. This allows for a gesture to be recognized even when the hand location is highly ambiguous. The second contribution is a pruning method that uses model-specific classifiers to reject dynamic programming hypotheses with a poor match between the input and model. Pruning improves the efficiency of the spatiotemporal matching algorithm, and in some cases may improve the recognition accuracy. The pruning classifiers are learned from training data, and cross-validation is used to reduce the chance of overpruning. The third contribution is a subgesture reasoning process that models the fact that some gesture models can falsely match parts of other, longer gestures. By integrating subgesture reasoning the spotting algorithm can avoid the premature detection of a subgesture when the longer gesture is actually being performed. Subgesture relations between pairs of gestures are automatically learned from training data. The performance of the approach is evaluated on two challenging video datasets: hand-signed digits gestured by users wearing short sleeved shirts, in front of a cluttered background, and American Sign Language (ASL) utterances gestured by ASL native signers. The experiments demonstrate that the proposed method is more accurate and efficient than competing approaches. The proposed approach can be generally applied to alignment or search problems with multiple input observations, that use dynamic programming to find a solution.

Neural Pattern Recognition on Multichannel Input Representation

This article presents a new neural pattern recognition architecture on multichannel data representation. The architecture emploies generalized ART modules as building blocks to construct a supervised learning system generating recognition codes on channels dynamically selected in context using serial and parallel match trackings led by inter-ART vigilance signals.