This paper proposes a machine learning approach to grouping problems in ink parsing. Starting from an initial segmentation, hypotheses are generated by perturbing local configurations and processed in a high-confidence-first fashion, where the confidence of each hypothesis is produced by a data-driven AdaBoost decision-tree classifier with a set of intuitive features. This framework has successfully applied to grouping text lines and regions in complex freeform digital ink notes from real TabletPC users. It holds great potential in solving many other grouping problems in the ink parsing and document image analysis domains.

Handwritten text lines are prominent structures in freeform digital ink notes and their reliable detection is the foundation to a natural and intelligent interface for note editing and repurposing. This paper presents an optimization method for text line grouping. The global'cost function is designed to find the simplest stroke partitioning to maximize the likelihood of the resulting lines and the consistency of their configuration. A dynamic programming algorithm provides an initial segmentation of the time-ordered stroke sequence. Then a local gradient-descent algorithm iteratively evaluates splitting and merging hypotheses to minimize the global cost function. On average, the proposed technique processes each note page in less than a second at 90% accuracy.

Most current robotic systems treat motion planning and control as two independent subsystems where the motion planner determines a collision-free path for achieving a goal, and the control system attempts to follow the planned path using sensing. This may yield undesirable motion plans that do not utilize the sensing effectively or consider sensor constraints. In this paper we consider an approach for motion planning that incorporates visual servoing constraints into the computation of the motion plans. The approach extends the notion of configuration space to include the corresponding sensor values. We address the issue of representing the extended planning space and practical computational schemes for motion planning with visual servoing constraints. We propose a hierarchical representation of the high dimensional planning space involved, and a multi-strategic heuristics search. This results in a practical motion planning scheme that is proven to be resolution-complete.

The calibration requirements for visual servoing can make it difficult to apply in many real-world situations. One approach to image-based visual servoing without calibration is to dynamically estimate the image Jacobian and use it as the basis for control. However, with the normal motion of a robot toward the goal, the estimation of the image Jacobian deteriorates over time. The authors propose the use of additional exploratory motion to considerably improve the estimation of the image Jacobian. They study the role of such exploratory motion in a visual servoing task. Simulations and experiments with a 6-DOF robot are used to verify the practical feasibility of the approach.

Visual feedback can play a crucial role in a dynamic robotic task such as the interception of a moving target. To utilize the feedback effectively, there is a need to develop robot motion planning techniques that also take into account properties of the sensed data. We propose a motion planning framework that achieves this with the help of a space called the perceptual control manifold (PCM) defined on the product of the robot configuration space and an image-based feature space. We show how the task of intercepting a moving target can be mapped to the PCM, using image feature trajectories of the robot end-effector and the moving target. This leads to the generation of motion plans that satisfy various constraints and optimality criteria derived from the robot kinematics, the control system, and the sensing mechanism. Specific interception tasks are analyzed to illustrate this vision-based planning technique.

Visual feedback can play a crucial role in a dynamic robotic task such as the interception of a moving target. To utilize the feedback effectively, there is a need to develop robot motion planning techniques that also take into account properties of the sensed data. We propose a motion planning framework that achieves this with the help of a space called the perceptual control manifold (PCM) defined on the product of the robot configuration space and an image-based feature space. We show how the task of intercepting a moving target can be mopped to the PCM. This leads to the generation of motion plans that satisfy various constraints and optimality criteria derived from the robot kinematics, the control system, and the sensing mechanism.

This paper describes two case studies of motion planning that uses a space called Perceptual Control Surface (PCS) introduced previously as a basis for visually controlling a robot manipulator. The motion planning problems discussed here are limited to dynamic robot hand positioning for interception tasks. Using this approach, a motion planning problem can be regarded as finding a path in robot's PCS which satisfies certain criteria. Qualitatively different tasks correspond to different type of paths in the PCS. These correspondences are discussed and techniques to find these different paths are outlined.>