Daniel D. Morris

Singularity analysis for articulated object tracking

We analyze the use of kinematic constraints for articulated object tracking. Conditions for the occurrence of singularities in 3-D models are presented and their effects on tracking are characterized We describe a novel 2-D Scaled Prismatic Model (SPM) for figure registration. In contrast to 3-D kinematic models, the SPM has fewer singularity problems and does not require detailed knowledge of the 3-D kinematics. We fully characterize the singularities in the SPM and illustrate tracking through singularities using synthetic and real examples with 3-D and 2-D models. Our results demonstrate the significant benefits of the SPM in tracking with a single source of video.

A unified factorization algorithm for points, line segments and planes with uncertainty models

In this paper we present a unified factorization algorithm for recovering structure and motion from image sequences by using point features, line segments and planes. This new formulation is based on directional uncertainty model for features. Points and line segments are both described by the same probabilistic models and so can be recovered in the same way. Prior information on the coplanarity of features is shown to fit naturally into the new factorization formulation and provides additional constraints for the shape recovery. This formulation leads to a weighted least squares motion and shape recovery problem which is solved by an efficient quasi-linear algorithm. The statistical uncertainty model also enables us to recover uncertainty estimates for the reconstructed three dimensional feature locations.

Image-consistent surface triangulation

Given a set of 3D points that we know lie on the surface of an object, we can define many possible surfaces that pass through all of these points. Even when we consider only surface triangulations, there are still an exponential number of valid triangulations that all fit the data. Each triangulation will produce a different faceted surface connecting the points. Our goal is to overcome this ambiguity and find the particular surface that is closest to the true object surface. We do not know the true surface but instead we assume that we have a set of images of the object. We propose selecting a triangulation based on its consistency with this set of images of the object. We present an algorithm that starts with an initial rough triangulation and refines the triangulation until it obtains a surface that best accounts for the images of the object. Our method is thus able to overcome the surface ambiguity problem and at the same time capture sharp corners and handle concave regions and occlusions. We show results for a few real objects.

Factorization methods for structure from motion

In this article we present an overview of factorization methods for recovering structure and motion from image sequences. We distinguish these methods from general nonlinear algorithms primarily by their bilinear formulation in motion and shape parameters. The bilinear formulation makes possible powerful and efficient solution techniques including singular value decomposition. We show how factorization methods apply under various affine camera models and under the perspective camera model, and then we review factorization methods for various features including points, lines, directional point features and line segments. An extension to these methods enables them to segment and recover motion and shape for multiple independently moving objects. Finally we illustrate the generality of the factorization methods with two applications outside structure from motion.

mLogic: ultra-low voltage non-volatile logic circuits using STT-MTJ devices

This paper introduces the design of logic circuits based exclusively on novel magnetoelectronic devices. Current signals are steered by 2× resistance change switching while operating with sub-100 mV voltage pulses for power and synchronization. The inherent memory of the devices results in fully pipelined nonvolatile logic. We demonstrate that co-optimization of the devices, circuits and logic can achieve ultra-low energy-per-operation for design examples.

Visual classification of coarse vehicle orientation using Histogram of Oriented Gradients features

For an autonomous vehicle, detecting and tracking other vehicles is a critical task. Determining the orientation of a detected vehicle is necessary for assessing whether the vehicle is a potential hazard. If a detected vehicle is moving, the orientation can be inferred from its trajectory, but if the vehicle is stationary, the orientation must be determined directly. In this paper, we focus on vision-based algorithms for determining vehicle orientation of vehicles in images. We train a set of Histogram of Oriented Gradients (HOG) classifiers to recognize different orientations of vehicles detected in imagery. We find that these orientation-specific classifiers perform well, achieving a 88% classification accuracy on a test database of 284 images. We also investigate how combinations of orientation-specific classifiers can be employed to distinguish subsets of orientations, such as drivers side versus passengers side views. Finally, we compare a vehicle detector formed from orientation-specific classifiers to an orientation-independent classifier and find that, counter-intuitively, the orientation-independent classifier outperforms the set of orientation-specific classifiers.

Ambiguities in Visual Tracking of Articulated Objects Using Two- and Three-Dimensional Models

Three-dimensional (3D) kinematic models are widely-used in video-based figure tracking. We show that these models can suffer from singularities when motion is directed along the viewing axis of a single camera. The single camera case is important because it arises in many interesting applications, such as motion capture from movie footage, video surveillance, and vision-based user-interfaces. We describe a novel two-dimensional scaled prismatic model (SPM) for figure registration. In contrast to 3D kinematic models, the SPM has fewer singularity problems and does not require detailed knowledge of the 3D kinematics. We fully characterize the singularities in the SPM and demonstrate tracking through singularities using synthetic and real examples. We demonstrate the application of our model to motion capture from movies. Fred Astaire is tracked in a clip from the film “Shall We Dance”. We also present the use of monocular hand tracking in a 3D user-interface. These results demonstrate the benefits of the SPM in tracking with a single source of video.

Autonomous river navigation

Existing maritime navigation and reconnaissance systems require man-in-the-loop situation awareness for obstacle avoidance, area survey analysis, threat assessment, and mission re-planning. We have developed a boat with fully autonomous navigation, surveillance, and reactive behaviors. Autonomous water navigation is achieved with no prior maps or other data − the water surface, riverbanks obstacles, movers and salient objects are discovered and mapped in real-time using a circular array of cameras along with a self-directed pan-tilt camera. The autonomous boat has been tested on harbor and river domains. Results of the detection, tracking, mapping and navigation will be presented.

Gauges and gauge transformations for uncertainty description of geometric structure with indeterminacy

This paper presents a consistent theory for describing indeterminacy and uncertainty of three-dimensional (3-D) reconstruction from a sequence of images. First, we give a group-theoretical analysis of gauges and gauge transformations. We then discuss how to evaluate the reliability of the solution that has indeterminacy and extend the Cramer-Rao lower bound to incorporate internal indeterminacy. We also introduce the free-gauge approach and define the normal form of a covariance matrix that is independent of particular gauges. Finally, we show simulated and real-image examples to illustrate the effect of gauge freedom on uncertainty description.

Gauge fixing for accurate 3D estimation

Computer vision techniques can estimate 3D shape from images, but usually only up to a scale factor. The scale factor must be obtained by a physical measurement of the scene or the camera motion. Using gauge theory, we show that how this scale factor is determined can significantly affect the accuracy of the estimated shape. And yet these considerations have been ignored in previous works where 3D shape accuracy is optimized. We investigate how scale fixing influences the accuracy of 3D reconstruction and determine what measurement should be made to maximize the shape accuracy.