Philip A. Tresadern

Synchronizing Image Sequences of Non-Rigid Objects.

For stereopsis, images of a given scene must be captured at the same instant to ensure temporal consistency. For sequences of images (i.e. video streams) this requires the potentially costly and technically complex process of synchronizing cameras. We present a simple but effective method for automatically recovering the sub-frame temporal offset between image sequences taken using unsynchronized cameras. Having recovered the offset, we obtain the affine structure of a non-rigid motion. The technique is demonstrated for the application of human motion capture.

Mobile Biometrics: Combined Face and Voice Verification for a Mobile Platform

The Mobile Biometrics (MoBio) project combines real-time face and voice verification for better security of personal data stored on, or accessible from, a mobile platform.

Real-Time Facial Feature Tracking on a Mobile Device

This paper presents an implementation of the Active Appearance Model that is able to track a face on a mobile device in real-time. We achieve this performance by discarding an explicit texture model, using fixed-point arithmetic for much of the computation, applying a sequence of models with increasing complexity, and exploiting a sparse basis projection via Haar-like features. Our results show that the Haar-like feature basis achieves similar performance to more traditional approaches while being more suitable for a mobile device. Finally, we discuss mobile applications of the system such as face verification, teleconferencing and human-computer interaction.

Additive update predictors in active appearance models

The Active Appearance Model (AAM) provides an efficient method for localizing objects that vary in both shape and texture, and uses a linear regressor to predict updates to model parameters based on current image residuals. This study investigates using additive (or ‘boosted’) predictors, both linear and non-linear, as a substitute for the linear predictor in order to improve accuracy and efficiency. We demonstrate: (a) a method for training additive models that is several times faster than the standard approach without sacrificing accuracy; (b) that linear additive models can serve as an effective substitute for linear regression; (c) that linear models are as effective as non-linear models when close to the true solution. Based on these observations, we compare a ‘hybrid’ AAM to the standard AAM for both the XM2VTS and BioID datasets, including cross-dataset evaluations.

Combining Local and Global Shape Models for Deformable Object Matching

We describe a method for modelling and locating deformable objects using a combination of global and local shape models. An object is represented as a set of patches together with a geometric model of their relative positions. The geometry is modelled with a global pose and linear shape model, together with a Markov Random Field (MRF) model of local displacements from the global model. Matching to a new image involves an alternating scheme in which an MRF inference technique selects the best candidates for each point, which are then used to update the parameters of the global pose and shape model. A cascade of increasingly complex models is used to achieve robust matching to new images. We explore the effect of model parameters on system performance and show that the proposed method achieves better accuracy than other widely used methods on standard datasets.

Uncalibrated and unsynchronized human motion capture: a stereo factorization approach

Human motion capture typically requires several high quality, synchronized and calibrated cameras in a studio environment and can be potentially costly and technically complex. Instead, we propose a system which combines and improves upon two existing techniques, yielding an efficient method that recovers maximum likelihood joint angles and anthropomorphic data of the subject by factorization. The first technique concerns using a rank constraint framework to synchronize sequences of non-rigid motions where we extend affine methods to perspective and homography projection models. The second is a self-calibration method for two affine cameras, using constraints derived from prior knowledge of the underlying structure. We propose a minimal parameterization of the system to obtain an initial solution then apply a full bundle adjustment over the free parameters based on a geometric error We demonstrate the efficacy of our method by comparing the recovered structure and motion with that from a commercial motion capture system.

Video synchronization from human motion using rank constraints

This paper presents a method of synchronizing video sequences that exploits the non-rigidity of sets of 3D point features (e.g., anatomical joint locations) within the scene. The theory is developed for homography, perspective and affine projection models within a unified rank constraint framework that is computationally cheap. An efficient method is then presented that recovers potential frame correspondences, estimates possible synchronization parameters via the Hough transform and refines these parameters using non-linear optimization methods in order to recover synchronization to sub-frame accuracy, even for sequences of unknown and different frame rates. The method is evaluated quantitatively using synthetic data and demonstrated qualitatively on several real sequences.

An Automated System for Detecting and Measuring Nailfold Capillaries

Nailfold capillaroscopy is an established qualitative technique in the assessment of patients displaying Raynauds phenomenon. We describe a fully automated system for extracting quantitative biomarkers from capillaroscopy images, using a layered machine learning approach. On an unseen set of 455 images, the system detects and locates individual capillaries as well as human experts, and makes measurements of vessel morphology that reveal statistically significant differences between patients with (relatively benign) primary Raynauds phenomenon, and those with potentially life-threatening systemic sclerosis.

Camera calibration from human motion

This paper presents a method for the self-calibration of non-rigid affine structure to a Euclidean co-ordinate frame from only two views by enforcing constraints derived from the known structure of the human body, such as piecewise rigidity and approximate symmetry. We show that the proposed algorithm is considerably more efficient yet equally accurate when compared to previous methods. The resulting structure and motion is then refined further using a full bundle adjustment to give maximum likelihood values for body segment lengths and joint angles. A quantitative analysis is presented using synthetic data whilst qualitative results are demonstrated for real examples of human motion.

An Evaluation of Shape Descriptors for Image Retrieval in Human Pose Estimation.

This paper presents an empirical comparison of several shape representations in order to search a database of training examples (silhouettes) for the task of human pose estimation. In particular, we compare the Discrete Cosine Transform (DCT), Lipschitz embeddings and the Histogram of Shape Contexts that has previously demonstrated some success in this task. Our results suggest that a simple linear transformation of the image (such as the DCT) is as effective as the more complex, non-linear methods.