This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

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Distinctive Image Features from Scale-Invariant Keypoints

The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.

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Multiple View Geometry in Computer Vision.

The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work. For example, we desire to obtain an understanding of natural scenes, containing roads, buildings, trees, bushes, etc., as typified by the two frames from a sequence illustrated in Figure 1. The solution to this problem that we are pursuing is to use a computer vision system based upon motion analysis of a monocular image sequence from a mobile camera. By extraction and tracking of image features, representations of the 3D analogues of these features can be constructed.

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A Combined Corner and Edge Detector

In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.

This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.

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SURF: speeded up robust features

The apparatus comprises video cameras for viewing part of a patient's body and a slide projector for projecting a predetermined pattern of light onto the patient's body which is viewed by the cameras. A workstation and video frame grabber performs controlling and processing functions and are arranged to process said images from the cameras using image processing algorithms. The images from the cameras are superimposed upon prestored images which are generated from X-ray or CT scans. A passive pointer, having a predetermined pattern thereon which is recognisable by the controlling and processing means is used by the surgeon. The pattern is used to identify the exact position of a tip of the pointer in relation to the patient's body and is displayed on a monitor with the superimposed images.

Video-based system for computer assisted surgery and localisation

Development and preliminary evaluation of VISLAN, a surgical planning and guidance system using intra-operative video imaging

Dynamic movements of the hand, fingers, and thumb are difficult to measure due to the versatility and complexity of movement inherent in function. An innovative approach to measuring hand kinematics is proposed and validated. The proposed system utilizes the Microsoft Kinect and goes beyond gesture recognition to develop a validated measurement technique of finger kinematics. The proposed system adopted landmark definition (validated through ground truth estimation against assessors) and grip classification algorithms, including kinematic definitions (validated against a laboratory-based motion capture system). The results of the validation show 78% accuracy when identifying specific markerless landmarks. In addition, comparative data with a previously validated kinematic measurement technique show accuracy of MCP ± 10° (average absolute error (AAE) = 2.4°), PIP ± 12° (AAE = 4.8°), and DIP ± 11° (AAE = 4.8°). These results are notably better than clinically based alternative manual measurement techniques. The ability to measure hand movements, and therefore functional dexterity, without interfering with underlying composite movements, is the paramount objective to any bespoke measurement system. The proposed system is the first validated markerless measurement system using the Microsoft Kinect that is capable of measuring finger joint kinematics. It is suitable for home-based motion capture for the hand and, therefore, achieves this objective.

Markerless Motion Capture and Measurement of Hand Kinematics: Validation and Application to Home-Based Upper Limb Rehabilitation

A method for tracking the position and orientation of an ultrasound beam emitted from an ultrasound probe. The method comprises the steps of: storing a model of the visible object in a memory of an optical tracking system; calculating the position and orientation of the visible object; and calculating the position and orientation of the ultrasound beam by applying a known geometric relationship to the deduced position and orientation of the visible object.

Chris Harris

Papers

A Combined Corner and Edge Detector

Video-based system for computer assisted surgery and localisation

Development and preliminary evaluation of VISLAN, a surgical planning and guidance system using intra-operative video imaging

Markerless Motion Capture and Measurement of Hand Kinematics: Validation and Application to Home-Based Upper Limb Rehabilitation

Optical tracking systems