Daniel Haase

Mixed gaits in small avian terrestrial locomotion.

Scientists have historically categorized gaits discretely (e.g. regular gaits such as walking, running). However, previous results suggest that animals such as birds might mix or regularly or stochastically switch between gaits while maintaining a steady locomotor speed. Here, we combined a novel and completely automated large-scale study (over one million frames) on motions of the center of mass in several bird species (quail, oystercatcher, northern lapwing, pigeon, and avocet) with numerical simulations. The birds studied do not strictly prefer walking mechanics at lower speeds or running mechanics at higher speeds. Moreover, our results clearly display that the birds in our study employ mixed gaits (such as one step walking followed by one step using running mechanics) more often than walking and, surprisingly, maybe as often as grounded running. Using a bio-inspired model based on parameters obtained from real quails, we found two types of stable mixed gaits. In the first, both legs exhibit different gait mechanics, whereas in the second, legs gradually alternate from one gait mechanics into the other. Interestingly, mixed gaits parameters mostly overlap those of grounded running. Thus, perturbations or changes in the state induce a switch from grounded running to mixed gaits or vice versa.

Anatomical landmark tracking for the analysis of animal locomotion in X-ray videos using active appearance models

X-ray videography is one of the most important techniques for the locomotion analysis of animals in biology, motion science and robotics. Unfortunately, the evaluation of vast amounts of acquired data is a tedious and time-consuming task. Until today, the anatomical landmarks of interest have to be located manually in hundreds of images for each image sequence. Therefore, an automatization of this task is highly desirable. The main difficulties for the automated tracking of these landmarks are the numerous occlusions due to the movement of the animal and the low contrast in the x-ray images. For this reason, standard tracking approaches fail in this setting. To overcome this limitation, we analyze the application of Active Appearance Models for this task. Based on real data, we show that these models are capable of effectively dealing with occurring occlusions and low contrast and can provide sound tracking results.

Accurate 3D Multi-marker Tracking in X-ray Cardiac Sequences Using a Two-Stage Graph Modeling Approach

The in-depth analysis of heart movements under varying conditions is an important problem of cardiac surgery. To reveal the movement of relevant muscular parts, biplanar X-ray recordings of implanted radio-opaque markers are acquired. As manually locating these markers in the images is a very time-consuming task, our goal is to automate this process. Taking into account the difficulties in the recorded data such as missing detections or 2D occlusions, we propose a two-stage graph-based approach for both 3D tracklet and 3D track generation. In the first stage of our approach, we construct a directed acyclic graph of 3D observations to obtain tracklets via shortest path optimization. Afterwards, full tracks are extracted from a tracklet graph in a similar manner. This results in a globally optimal linking of detections and tracklets, while providing a flexible framework which can easily be adapted to various tracking scenarios based on the edge cost functions. We validate our approach on an X-ray sequence of a beating sheep heart based on manually labeled ground-truth marker positions. The results show that the performance of our method is comparable to human experts, while standard 3D tracking approaches such as particle filters are outperformed.

Instance-Weighted Transfer Learning of Active Appearance Models

There has been a lot of work on face modeling, analysis, and landmark detection, with Active Appearance Models being one of the most successful techniques. A major drawback of these models is the large number of detailed annotated training examples needed for learning. Therefore, we present a transfer learning method that is able to learn from related training data using an instance-weighted transfer technique. Our method is derived using a generalization of importance sampling and in contrast to previous work we explicitly try to tackle the transfer already during learning instead of adapting the fitting process. In our studied application of face landmark detection, we efficiently transfer facial expressions from other human individuals and are thus able to learn a precise face Active Appearance Model only from neutral faces of a single individual. Our approach is evaluated on two common face datasets and outperforms previous transfer methods.

Automated approximation of center of mass position in X-ray sequences of animal locomotion.

A crucial aspect of comparative biomechanical research is the center of mass (CoM) estimation in animal locomotion scenarios. Important applications include the parameter estimation of locomotion models, the discrimination of gaits, or the calculation of mechanical work during locomotion. Several methods exist to approximate the CoM position, e.g. force-plate-based approaches, kinematic approaches, or the reaction board method. However, they all share the drawback of not being suitable for large scale studies, as detailed initial conditions from kinematics are required (force-plates), manual interaction is necessary (kinematic approach), or only static settings can be analyzed (reaction board). For the increasingly popular case of X-ray-based animal locomotion analysis, we present an alternative approach for CoM estimation which overcomes these shortcomings. The main idea is to only use the recorded X-ray images, and to map each pixel to the mass of matter it represents. As a consequence, our approach is surgically noninvasive, independent of animal species and locomotion characteristics, and neither requires prior knowledge nor any kind of user interaction. To assess the quality of our approach, we conducted a comparison to highly accurate reaction board experiments for lapwing and rat cadavers, and achieved an average accuracy of 2.6mm (less than 2% of the animal body length). We additionally verified the practical applicability of the algorithm by comparison to a previously published CoM study which is based on the kinematic method, yielding comparable results.

Multi-view active appearance models for the X-ray based analysis of avian bipedal locomotion

Many fields of research in biology, motion science and robotics depend on the understanding of animal locomotion. Therefore, numerous experiments are performed using high-speed biplanar x-ray acquisition systems which record sequences of walking animals. Until now, the evaluation of these sequences is a very time-consuming task, as human experts have to manually annotate anatomical landmarks in the images. Therefore, an automation of this task at a minimum level of user interaction is worthwhile. However, many difficulties in the data--such as x-ray occlusions or anatomical ambiguities--drastically complicate this problem and require the use of global models. Active Appearance Models (AAMs) are known to be capable of dealing with occlusions, but have problems with ambiguities. We therefore analyze the application of multi-view AAMs in the scenario stated above and show that they can effectively handle uncertainties which can not be dealt with using single-view models. Furthermore, preliminary studies on the tracking performance of human experts indicate that the errors of multi-view AAMs are in the same order of magnitude as in the case of manual tracking.

Analysis of Structural Dependencies for the Automatic Visual Inspection of Wire Ropes

Automatic visual inspection is an arising field of research. Especially in security relevant applications, an automation of the inspection process would be a great benefit. For wire ropes, a first step is the acquisition of the curved surface with several cameras located all around the rope. Because most of the visible defects in such a rope are very inconspicuous, an automatic defect detection is a very challenging problem. As in general there is a lack of defective training data, most of the presented ideas for automatic rope inspection are embedded in a one-class classification framework. However, none of these methods makes use of the context information which results from the fact that all camera views image the same rope. In contrast to an individual analysis of each camera view, this work proposes the simultaneous analysis of all available camera views with the help of a vector autoregressive model. Moreover, various dependency analysis methods are used to give consideration to the regular rope structure and to deal with the high dimensionality of the problem. These dependencies are then used as constraints for the vector autoregressive model, which results in a sparse but powerful detection system. The proposed method is evaluated by using real wire rope data and the conducted experiments show that our approach clearly outperforms all previously presented methods.

2D and 3D analysis of animal locomotion from biplanar X-ray videos using augmented active appearance models

For many fundamental problems and applications in biomechanics, biology, and robotics, an in-depth understanding of animal locomotion is essential. To analyze the locomotion of animals, high-speed X-ray videos are recorded, in which anatomical landmarks of the locomotor system are of main interest and must be located. To date, several thousand sequences have been recorded, which makes a manual annotation of all landmarks practically impossible. Therefore, an automatization of X-ray landmark tracking in locomotion scenarios is worthwhile. However, tracking all landmarks of interest is a very challenging task, as severe self-occlusions of the animal and low contrast are present in the images due to the X-ray modality. For this reason, existing approaches are currently only applicable for very specific subsets of anatomical landmarks. In contrast, our goal is to present a holistic approach which models all anatomical landmarks in one consistent, probabilistic framework. While active appearance models (AAMs) provide a reasonable global modeling framework, they yield poor fitting results when applied on the full set of landmarks. In this paper, we propose to augment the AAM fitting process by imposing constraints from various sources. We derive a general probabilistic fitting approach and show how results of subset AAMs, local tracking, anatomical knowledge, and epipolar constraints can be included. The evaluation of our approach is based on 32 real-world datasets of five bird species which contain 175,942 ground-truth landmark positions provided by human experts. We show that our method clearly outperforms standard AAM fitting and provides reasonable tracking results for all landmark types. In addition, we show that the tracking accuracy of our approach is even sufficient to provide reliable three-dimensional landmark estimates for calibrated datasets.

Comparative large-scale evaluation of human and active appearance model based tracking performance of anatomical landmarks in X-ray locomotion sequences

The detailed understanding of animal locomotion is an important part of biology, motion science and robotics. To analyze the motion, high-speed x-ray sequences of walking animals are recorded. The biological evaluation is based on anatomical key points in the images, and the goal is to find these landmarks automatically. Unfortunately, low contrast and occlusions in the images drastically complicate this task. As recently shown, Active Appearance Models (AAMs) can be successfully applied to this problem. However, obtaining reliable quantitative results is a tedious task, as the human error is unknown. In this work, we present the results of a large scale study which allows us to quantify both the tracking performance of humans as well as AAMs. Furthermore, we show that the AAM-based approach provides results which are comparable to those of human experts.