Abstract: 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.

Abstract: Aim of the present observational single center study was to objectively assess facial function in patients with idiopathic facial palsy with a new computer-based system that automatically recognizes action units (AUs) defined by the Facial Action Coding System (FACS). Still photographs using posed facial expressions of 28 healthy subjects and of 299 patients with acute facial palsy were automatically analyzed for bilateral AU expression profiles. All palsies were graded with the House–Brackmann (HB) grading system and with the Stennert Index (SI). Changes of the AU profiles during follow-up were analyzed for 77 patients. The initial HB grading of all patients was 3.3 ± 1.2. SI at rest was 1.86 ± 1.3 and during motion 3.79 ± 4.3. Healthy subjects showed a significant AU asymmetry score of 21 ± 11 % and there was no significant difference to patients (p = 0.128). At initial examination of patients, the number of activated AUs was significantly lower on the paralyzed side than on the healthy side (p < 0.0001). The final examination for patients took place 4 ± 6 months post baseline. The number of activated AUs and the ratio between affected and healthy side increased significantly between baseline and final examination (both p < 0.0001). The asymmetry score decreased between baseline and final examination (p < 0.0001). The number of activated AUs on the healthy side did not change significantly (p = 0.779). Radical rethinking in facial grading is worthwhile: automated FACS delivers fast and objective global and regional data on facial motor function for use in clinical routine and clinical trials.

Abstract: The detailed understanding of animals in locomotion is a relevant field of research in biology, biomechanics and robotics. To examine the locomotor system of birds in vivo and in a surgically non-invasive manner, high-speed X-ray acquisition is the state of the art. For a biological evaluation, it is crucial to locate relevant anatomical structures of the locomotor system. There is an urgent need for automating this task, as vast amounts of data exist and a manual annotation is extremely time-consuming. We present a biologically motivated skeleton model tracking framework based on a pictorial structure approach which is extended by robust sub-template matching. This combination makes it possible to deal with severe self-occlusions and challenging ambiguities. As opposed to model-driven methods which require a substantial amount of labeled training samples, our approach is entirely data-driven and can easily handle unseen cases. Thus, it is well suited for large scale biological applications at a minimum of manual interaction. We validate the performance of our approach based on 24 real-world X-ray locomotion datasets, and achieve results which are comparable to established methods while clearly outperforming more general approaches.

Abstract: 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.

Abstract: 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.

Abstract: Recent advances in the understanding of animal locomotion have proven it to be a key element of many fields in biology, motion science, and robotics. For the analysis of walking animals, high-speed x-ray videography is employed. For a biological evaluation of these x-ray sequences, anatomical landmarks have to be located in each frame. However, due to the motion of the animals, severe occlusions complicate this task and standard tracking methods can not be applied. We present a robust tracking approach which is based on the idea of dividing a template into sub-templates to overcome occlusions. The difference to other sub-template approaches is that we allow soft decisions for the fusion of the single hypotheses, which greatly benefits tracking stability. Also, we show how anatomical knowledge can be included into the tracking process to further improve the performance. Experiments on real datasets show that our method achieves results superior to those of existing robust approaches.

Abstract: 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.

Abstract: 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.

Abstract: 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.