David M. Chelberg

Automatic gradient threshold determination for edge detection

We describe a method to automatically find gradient thresholds to separate edge from nonedge pixels. A statistical model that is the weighted sum of two gamma densities corresponding to edge and nonedge pixels is used to identify a threshold. Results closely match human perceptual thresholds even under low signal-to-noise ratio (SNR) levels.

Model-Based 3D Object Recognition Using Bayesian Indexing

This research features the rapid recognition of three-dimensional objects, focusing on efficient indexing. A major concern in practical vision systems is how to retrieve the best matched models without exploring all possible object matches. We have employed a Bayesian framework to achieve efficient indexing of model objects. A decision-theoretic measure of the discriminatory power of a feature for a model object is defined in terms of posterior probability. Domain-specific knowledge compiled off-line from CAD model data is used in order to estimate posterior probabilities that define the discriminatory power of features for model objects. In order to speed up the indexing or selection of correct objects, we generate and verify the object hypotheses for features detected in a scene in the order of the discriminatory power of these features for model objects. Based on the principles described above, we have implemented a working prototype vision system using a feature structure called an LSG (local surface group) for generating object hypotheses. Our object recognition system can employ a wide class of features for generation of object hypotheses. In order to verify an object hypothesis, we estimate the view of the hypothesized model object and render the model object for the computed view. The object hypothesis is then verified by finding additional features in the scene that match those present in the rendered image. Experimental results on synthetic and real range images show the effectiveness of the indexing scheme.

Content-Based Image Retrieval Based on ROI Detection and Relevance Feedback

Content-based image retrieval is an important research topic in computer vision. We present a new method that combines region of interest (ROI) detection and relevance feedback. The ROI based approach is more accurate in describing the image content than using global features, and the relevance feedback makes the system to be adaptive to subjective human perception. The feedback information is utilized to discover the subjective ROI perception of a particular user, and it is further employed to recompute the features associated with ROIs with the updated personalized ROI preference. A fast computation technique is proposed to avoid repeating the ROI detection for images in the database. It directly estimates the features of the ROIs, which makes the query process fast and efficient. For illustration of the overall approach, we use the color saliency and wavelet feature saliency to determine the ROIs. Normalized projections are selected to represent the shape features associated with the ROIs. Experimental results show that the proposed system has better performance than the global features based approaches and region based techniques without feedback.

Issues in the design of studies to test the effectiveness of stereo imaging

Recently, there has been a great increase in interest in using three dimensional stereoscopic displays to provide viewers with realistic 3D views of objects of interest. Some applications where stereoscopic displays are becoming popular include medical visualization, visualization of meteorological data, and various virtual reality applications. To quantify the effectiveness of stereoscopic systems over conventional monoscopic systems, well-designed experiments and data analysis methods are necessary. This task requires the combined effort of application scientists and experts in experimental design. Lack of interdisciplinary collaboration is a primary weakness of many stereoscopic display studies, resulting in the neglect of many important but subtle experimental issues. In this paper, we discuss specific issues that arise in the design of studies to determine the effectiveness of digital stereo imagery. Issues concerning statistical analysis of the experimental data are also discussed. References to related literature from engineering, computer graphics, and psychophysics are given. The issues developed herein provide a guideline for the design of studies to compare observer performance when using different imaging modalities.

An optimization framework for dynamic, distributed real-time systems

The paper presents a model that is useful for developing resource allocation algorithms for distributed real-time systems that operate in dynamic environments. Interesting aspects of the model include dynamic environments, utility and service levels, which provide a means for graceful degradation in resource-constrained situations and support optimization of the allocation of resources. The paper also provides an allocation algorithm that illustrates how to use the model for producing feasible, optimal resource allocations.

An overview of the STEAMiE Educational game Engine

Today, there exists a significant disparity in the degree of technological development between commercial and educational games. The STEAMiE educational engine is a cutting-edge system used to create advanced, realistic, immersive learning environments. These environments have been shown to influence comprehension and retention of hard-to-teach science concepts. This paper will present an overview of the features of our STEAMiE educational engine and how to use it to create games/simulations capable of enhancing a userpsilas learning experience. The STEAMiE educational engine contains a rich feature set allowing for development of powerful modules within a short period of time. From a development point of view, the STEAMiE educational enginepsilas object oriented design is modular and can be easily extended to support new functionality in a timely manner. This significantly shortens development time and abstracts complexities from the developer allowing for clean and efficient implementations of games and simulations.

Uncertainty in interpretation of range imagery

A description is given of a method for automatically deriving a Bayesian probability network from CAD based 3D models. This network may be used to match 3D objects to features derived from range images in the presence of uncertainty. The network describes how evidence for subparts of an object should be combined to calculate the evidence for an object. A prototype model based range interpretation system using this method is also presented. Sample results for an object are presented. For this prototype system, sources of evidence include maximum likelihood estimate fits of circular cylinders, planes, and spheres, as well as boundary data (i.e. edges).<<ETX>>

Spatio-temporal modeling of facial expressions using Gabor-wavelets and hierarchical hidden Markov models

As one of the key techniques for futuristic man-machine interface, facial expression analysis has received much attention in recent years. This paper proposes a hierarchical approach to facial expression recognition in image sequences by exploiting both spatial and temporal characteristics within the framework of hierarchical hidden Markov models (HHMMs). Human faces are automatically detected in the maximum likelihood sense. Gabor-wavelet based features are extracted from image sequences to capture the subtle changes of facial expressions. Four prototype emotions; i.e. happiness, anger, fear and sadness, are investigated using the Cohn-Kanade database and an average of 90.98% person-independent recognition rate is achieved. We also demonstrate that HHMMs outperform HMMs for modeling image sequences with multilevel statistical structure.

Meta-reasoning for a distributed agent architecture

Agent based computing offers the ability to decentralize computing solutions by incorporating autonomy and intelligence into cooperating, distributed applications. It provides an effective medium for expressing solutions to problems that involve interaction with real-world environments and allows modelling of the world state and its dynamics. This model can be then used to determine how candidate actions affect the world and how to choose the best from a set of actions. Most agent paradigms overlook real-time requirements and computing resource constraints. We discuss the application of agent based computing to RoboCup and examine methods to improve it. In particular, we discuss the incorporation of a meta-level reasoning mechanism that handles individual agent organization, plan generation, task allocation, integration and plan execution. We also propose an architecture where a meta-agent is further enhanced by combining it with system-level resource allocation and optimization. The approach adopted by us unifies agent based computing with adaptive resource management for dynamic real-time systems. The goal is to build and implement a distributed, intelligent, agent based system for dynamic real-time applications.

Binocular Shape Reconstruction

A similarity structure (shape) of a 3-D object can be reconstructed from two perspective views of the object obtained by a calibrated camera. One of the best-known methods for performing such a reconstruction is the 8-point algorithm. To evaluate the psychological plausibility of this algorithm, we compared its performance to that of human subjects in a task of binocular shape reconstruction. Results show that the reconstructions produced by the 8-point algorithm are substantially less accurate than those produced by human subjects. We then modified the 8-point algorithm by incorporating natural constraints that are present in human binocular vision. Specifically, the new algorithm simulates a fixating system with no torsion. The performance of this algorithm is more robust in the presence of noise compared to that of the 8-point algorithm and is similar to the performance of human subjects.