Structural graph matching using the EM algorithm and singular value decomposition

This paper describes an efficient algorithm for inexact graph matching. The method is purely structural, that is, it uses only the edge or connectivity structure of the graph and does not draw on node or edge attributes. We make two contributions: 1) commencing from a probability distribution for matching errors, we show how the problem of graph matching can be posed as maximum-likelihood estimation using the apparatus of the EM algorithm; and 2) we cast the recovery of correspondence matches between the graph nodes in a matrix framework. This allows one to efficiently recover correspondence matches using the singular value decomposition. We experiment with the method on both real-world and synthetic data. Here, we demonstrate that the method offers comparable performance to more computationally demanding method

Influence of Patient Satisfaction of Total Knee Replacement Patients on Stair Negotiation and Walking Biomechanics, Strength, and Balance

Total knee replacement (TKR) patients have shown alterations in lower extremity biomechanics during level ground walking and stair negotiation, strength levels, and balance abilities, however, it is unknown how dissatisfied TKR patients compare to satisfied TKR patients in these activities. Study One examined the lower extremity biomechanics of dissatisfied and satisfied TKR patients during level ground walking. Study Two investigated knee biomechanics during stair ascent and descent activities. Study Three compared isokinetic strength, balance abilities, deep knee flexion abilities, and functional abilities of the dissatisfied patients to the satisfied patients. Study Four performed a logistic regression as a means of examining significant variables in models designed to predict patient satisfaction. Study One found reduced 1st and 2nd peak VGRF, knee flexion ROM, and peak loadingresponse knee extension and abduction moments in the dissatisfied patients compared to healthy controls. First and 2nd peak VGRFs and flexion ROM were reduced in the replaced limb of the dissatisfied patients compared to their non-replaced limb. Study Two showed reduced 2nd peak VGRF and loading-response knee extension moments in the replaced limb of the dissatisfied group compared to their non-replaced limb and to satisfied and healthy groups during stair ascent. 1st peak VGRF and both loading-response and push-off abduction moments showed reduced values in replaced limbs compared to non-replaced limbs for all groups. During stair descent, the dissatisfied group showed reduced loading-response and push-off knee extension moments in their replaced limb compared to their non-replaced limb and the healthy group. The loading-response knee extension and abduction moments were also reduced in the dissatisfied group compared to the satisfied group. Study Three showed reduced peak extension (180°/s) and flexion (60°/s) torque in dissatisfied patients compared to satisfied patients. No balance differences were evident, although an increased percentage of dissatisfied patients were unable to complete the unilateral balance tests. Study Four produced models via the logistic regression analysis which often included peak VGRFs and knee extension moments. Future research should examine the effects of attempting to alter the physical differences between patient satisfaction groups and whether it improves patient satisfaction rates

Efficient Human Pose Estimation with Image-dependent Interactions

Human pose estimation from 2D images is one of the most challenging and computationally-demanding problems in computer vision. Standard models such as Pictorial Structures consider interactions between kinematically connected joints or limbs, leading to inference cost that is quadratic in the number of pixels. As a result, researchers and practitioners have restricted themselves to simple models which only measure the quality of limb-pair possibilities by their 2D geometric plausibility. In this talk, we propose novel methods which allow for efficient inference in richer models with data-dependent interactions. First, we introduce structured prediction cascades, a structured analog of binary cascaded classifiers, which learn to focus computational effort where it is needed, filtering out many states cheaply while ensuring the correct output is unfiltered. Second, we propose a way to decompose models of human pose with cyclic dependencies into a collection of tree models, and provide novel methods to impose model agreement. Finally, we develop a local linear approach that learns bases centered around modes in the training data, giving us image-dependent local models which are fast and accurate. These techniques allow for sparse and efficient inference on the order of minutes or seconds per image. As a result, we can afford to model pairwise interaction potentials much more richly with data-dependent features such as contour continuity, segmentation alignment, color consistency, optical flow and multiple modes. We show empirically that these richer models are worthwhile, obtaining significantly more accurate pose estimation on popular datasets

Rich probabilistic models for semantic labeling

Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung

Registration of Optical Images to 3D Medical Images

The work described in this thesis deals with the registration of single and multiple 2-dimensional (2D) optical images to a single 3-dimensional (3D) medical image such as a magnetic resonance or computed tomography scan. The approach is to develop an intensity based method using an information theoretic framework, as opposed to the more typical feature or surface based methods. Relevant camera calibration and pose estimation literature is reviewed, along with medical 2D-3D image registration. An initial algorithm is developed, which performs registration by iteratively maximising the mutual information of a rendered image and a single optical image. The framework is extended to incorporate information from multiple optical and rendered images which signi cantly improves registration performance. A tracking algorithm is proposed, which augments this framework with texture mapping as a means of achieving alignment over a sequence of optical images. These methods are tested using images of skull phantoms and volunteers. A new measure based on the concept of photo-consistency, used in the surface reconstruction literature, is proposed as a measure of image alignment. The relevant theory is developed. This new method is tested using a variety of different photo-consistency based similarity measures, optical images, different numbers of images, images with varying amounts of added noise, different resolutions and different camera positions relative to the object of interest. In almost all cases, similarity measures based on this new framework perform accurately, precisely and robustly. Potential applications will be in radiotherapy patient positioning, image guided craniofacial, skull base and neurosurgery, computer vision and robotics, where the accurate alignment between a 3D image or model and multiple 2D optical images is required