Brian C. Becker

Micron: An Actively Stabilized Handheld Tool for Microsurgery

We describe the design and performance of a handheld actively stabilized tool to increase accuracy in microsurgery or other precision manipulation. It removes involuntary motion, such as tremor, by the actuation of the tip to counteract the effect of the undesired handle motion. The key components are a 3-degree-of-freedom (DOF) piezoelectric manipulator that has a 400-μm range of motion, 1-N force capability, and bandwidth over 100 Hz, and an optical position-measurement subsystem that acquires the tool pose with 4-μm resolution at 2000 samples/s. A control system using these components attenuates hand motion by at least 15 dB (a fivefold reduction). By the consideration of the effect of the frequency response of Micron on the human visual feedback loop, we have developed a filter that reduces unintentional motion, yet preserves the intuitive eye-hand coordination. We evaluated the effectiveness of Micron by measuring the accuracy of the human/machine system in three simple manipulation tasks. Handheld testing by three eye surgeons and three nonsurgeons showed a reduction in the position error of between 32% and 52%, depending on the error metric.

Face recognition for web-scale datasets

With millions of users and billions of photos, web-scale face recognition is a challenging task that demands speed, accuracy, and scalability. Most current approaches do not address and do not scale well to Internet-sized scenarios such as tagging friends or finding celebrities. Focusing on web-scale face identification, we gather an 800,000 face dataset from the Facebook social network that models real-world situations where specific faces must be recognized and unknown identities rejected. We propose a novel Linearly Approximated Sparse Representation-based Classification (LASRC) algorithm that uses linear regression to perform sample selection for @?^1-minimization, thus harnessing the speed of least-squares and the robustness of sparse solutions such as SRC. Our efficient LASRC algorithm achieves comparable performance to SRC with a 100-250 times speedup and exhibits similar recall to SVMs with much faster training. Extensive tests demonstrate our proposed approach is competitive on pair-matching verification tasks and outperforms current state-of-the-art algorithms on open-universe identification in uncontrolled, web-scale scenarios.

Evaluation of face recognition techniques for application to facebook

This paper evaluates face recognition applied to the real-world application of Facebook. Because papers usually present results in terms of accuracy on constrained face datasets, it is difficult to assess how they would work on natural data in a real-world application. We present a method to automatically gather and extract face images from Facebook, resulting in over 60,000 faces datasets, we evaluate a variety of well-known face recognition algorithms (PCA, LDA, ICA, SVMs) against holistic performance metrics of accuracy, speed, memory usage, and storage size. SVMs perform best with ~65% accuracy, but lower accuracy algorithms such as IPCA are orders of magnitude more efficient in memory consumption and speed, yielding a more feasible system.

Semiautomated intraocular laser surgery using handheld instruments.

In laser retinal photocoagulation, hundreds of dot‐like burns are applied. We introduce a robot‐assisted technique to enhance the accuracy and reduce the tedium of the procedure.

Vision-Based Control of a Handheld Surgical Micromanipulator With Virtual Fixtures

Performing micromanipulation and delicate operations in submillimeter workspaces is difficult because of destabilizing tremor and imprecise targeting. Accurate micromanipulation is especially important for microsurgical procedures, such as vitreoretinal surgery, to maximize successful outcomes and minimize collateral damage. Robotic aid combined with filtering techniques that suppress tremor frequency bands increases performance; however, if knowledge of the operators goals is available, virtual fixtures have been shown to further improve performance. In this paper, we derive a virtual fixture framework for active handheld micromanipulators that is based on high-bandwidth position measurements rather than forces applied to a robot handle. For applicability in surgical environments, the fixtures are generated in real time from microscope video during the procedure. Additionally, we develop motion scaling behavior around virtual fixtures as a simple and direct extension to the proposed framework. We demonstrate that virtual fixtures significantly outperform tremor cancellation algorithms on a set of synthetic tracing tasks (p <; 0.05). In more medically relevant experiments of vein tracing and membrane peeling in eye phantoms, virtual fixtures can significantly reduce both positioning error and forces applied to tissue (p <; 0.05).

Evaluating Open-Universe Face Identification on the Web

Face recognition is becoming a widely used technique to organize and tag photos. Whether searching, viewing, or organizing photos on the web or in personal photo albums, there is a growing demand to index real-world photos by the subjects in them. Even consumer platforms such as Google Picasa, Microsoft Photo Gallery, and social network sites such as Facebook have integrated forms of automated face tagging and recognition, furthermore, a number of libraries and cloud-based APIs that perform face recognition have become available. With such a plethora of choices, comparisons of recent advances become more important to gauge the state of progress in the field. This paper evaluates face identification in the context of not only research algorithms, but also considers consumer photo products, client-side libraries, and cloud-based APIs on a new, large-scale dataset derived from PubFig83 and LFW in a realistic open-universe scenario.

Retinal vessel cannulation with an image-guided handheld robot

Cannulation of small retinal vessels is often prohibitively difficult for surgeons, since physiological tremor often exceeds the narrow diameter of the vessel (40–120 µm). Using an active handheld micromanipulator, we introduce an image-guided robotic system that reduces tremor and provides smooth, scaled motion during the procedure. The micromanipulator assists the surgeon during the approach, puncture, and injection stages of the procedure by tracking the pipette and anatomy viewed under the microscope. In experiments performed ex vivo by an experienced retinal surgeon on 40–60 µm vessels in porcine eyes, the success rate was 29% (2/7) without the aid of the system and 63% (5/8) with the aid of the system.

Handheld micromanipulation with vision-based virtual fixtures

Precise movement during micromanipulation becomes difficult in submillimeter workspaces, largely due to the destabilizing influence of tremor. Robotic aid combined with filtering techniques that suppress tremor frequency bands increases performance; however, if knowledge of the operators goals is available, virtual fixtures have been shown to greatly improve micromanipulator precision. In this paper, we derive a control law for position-based virtual fixtures within the framework of an active handheld micromanipulator, where the fixtures are generated in real-time from microscope video. Additionally, we develop motion scaling behavior centered on virtual fixtures as a simple and direct extension to our formulation. We demonstrate that hard and soft (motion-scaled) virtual fixtures outperform state-of-the-art tremor cancellation performance on a set of artificial but medically relevant tasks: holding, move-and-hold, curve tracing, and volume restriction.

Active guidance of a handheld micromanipulator using visual servoing

In microsurgery, a surgeon often deals with anatomical structures of sizes that are close to the limit of the human hand accuracy. Robotic assistants can help to push beyond the current state of practice by integrating imaging and robot-assisted tools. This paper demonstrates control of a handheld tremor reduction micromanipulator with visual servo techniques, aiding the operator by providing three behaviors: snap-to, motion-scaling, and standoff-regulation. A stereo camera setup viewing the workspace under high magnification tracks the tip of the micromanipulator and the desired target object being manipulated. Individual behaviors activate in task-specific situations when the micromanipulator tip is in the vicinity of the target. We show that the snap-to behavior can reach and maintain a position at a target with an accuracy of 17.5 ± 0.4µm Root Mean Squared Error (RMSE) distance between the tip and target. Scaling the operators motions and preventing unwanted contact with non-target objects also provides a larger margin of safety.

Autoregressive modeling of physiological tremor under microsurgical conditions

Tremor was recorded under simulated vitreoretinal microsurgical conditions as subjects attempted to hold an instrument motionless. Several autoregressive models (AR, ARMA, multivariate, and nonlinear) are generated to predict the next value of tremor. It is shown that a sixth order ARMA model predictor can predict a tremor having an amplitude of 96.6 ± 84.5 microns RMS with an error of 8.2 ± 5.9 microns RMS, a mean improvement of 47.5% over simple last-value prediction.