Multi-view Active Appearance Research Articles

Interactive and Audience Adaptive Digital Signage Using Real-Time Computer Vision

In this paper we present the development of an interactive, content-aware and cost-effective digital signage system. Using a monocular camera installed within the frame of a digital signage display, we employ real-time computer vision algorithms to extract temporal, spatial and demographic features of the observers, which are further used for observer-specific broadcasting of digital signage content. The number of observers is obtained by the Viola and Jones face detection algorithm, whilst facial images are registered using multi-view Active Appearance Models. The distance of the observers from the system is estimated from the interpupillary distance of registered faces. Demographic features, including gender and age group, are determined using SVM classifiers to achieve individual observer-specific selection and adaption of the digital signage broadcasting content. The developed system was evaluated at the laboratory study level and in a field study performed for audience measurement research. Comparison of our monocular localization module with the Kinect stereo-system reveals a comparable level of accuracy. The facial characterization module is evaluated on the FERET database with 95% accuracy for gender classification and 92% for age group. Finally, the field study demonstrates the applicability of the developed system in real-life environments.

Automated contour detection in X-ray left ventricular angiograms using multiview active appearance models and dynamic programming

This paper describes a new approach to the automated segmentation of X-ray left ventricular (LV) angiograms, based on active appearance models (AAMs) and dynamic programming. A coupling of shape and texture information between the end-diastolic (ED) and end-systolic (ES) frame was achieved by constructing a multiview AAM. Over-constraining of the model was compensated for by employing dynamic programming, integrating both intensity and motion features in the cost function. Two applications are compared: a semi-automatic method with manual model initialization, and a fully automatic algorithm. The first proved to be highly robust and accurate, demonstrating high clinical relevance. Based on experiments involving 70 patient data sets, the algorithm's success rate was 100% for ED and 99% for ES, with average unsigned border positioning errors of 0.68 mm for ED and 1.45 mm for ES. Calculated volumes were accurate and unbiased. The fully automatic algorithm, with intrinsically less user interaction was less robust, but showed a high potential, mostly due to a controlled gradient descent in updating the model parameters. The success rate of the fully automatic method was 91% for ED and 83% for ES, with average unsigned border positioning errors of 0.79 mm for ED and 1.55 mm for ES.

Multiview Active Appearance Models for Simultaneous Segmentation of Cardiac 2- and 4-Chamber Long-Axis Magnetic Resonance Images

Long-axis cardiac magnetic resonance (MR) views enable a rapid, online evaluation of cardiac function from only 2 views. In this article, we aimed to evaluate a model-based method for the simultaneous detection of 2- and 4-chamber endocardial and epicardial contours in end-diastolic and end-systolic phases of MR images. We introduce multiview Active Appearance Models for the automated segmentation of long-axis cardiac MR images of the left ventricle. Two modes of initialization were used to test the accuracy of the model with minimal user interaction and the best-obtainable accuracy with this model. The segmentation was initialized by annotating 2 points in the base and one in the apex. We tested the method's performance by comparing the point-to-curve errors, ejection fractions, and biplane area-length volumes calculated with the automatically detected contours to those calculated from contours that were annotated manually by experts. Leave-one-out experiments were performed with 2- and 4-chamber long axis MR images of 59 subjects in end-diastolic and end-systolic phases. When initializing in all 4 frames, 97% of the segmentations were successful, and the standard deviation in the volume-errors with respect to the average manually identified volume was 9.0% for the end-diastolic volumes and 15% for the end-systolic volumes. When the method was initialized in the end-systolic frames only, 92% of the segmentations were successful, and the standard deviation in the errors in the volumes with respect to the average manually identified volume was 13.3% for the end-diastolic volumes and 16.7% for the end-systolic volumes. Bland-Altman plots showed that the errors were distributed randomly around 0, and by using a paired t test comparing manual and computer-determined volumes, we were able to detect that the volume differences were not significant. Simultaneous detection of the endocardial and epicardial contours in 2- and 4-chamber views and end-diastolic and end-systolic phases for one subject takes approximately 3 seconds. The accuracy of the reported method is comparable with the interobserver variability for initialization in all frames and slightly worse than the interobserver variability with initialization in the end-systolic frames only. However, in both cases the errors were not significant. Initialization in end-systolic frames only leads to a statistically insignificantly lower model accuracy; however, it requires only half the user interaction. Therefore, we can conclude that this method enables rapid analysis of the cardiac left ventricular function with little user interaction.

Multi-view active appearance models for consistent segmentation of multiple standard views: application to long- and short-axis cardiac MR images

This paper describes a multi-view Active Appearance Model (AAM) for coherent segmentation of multiple cardiac views. Cootes' AAM framework was adapted by considering shapes and intensities from multiple views as single shape and intensity samples, while eliminating trivial difference in object pose in different views. This way, the coherence in organ shape and intensities between different views is modeled and utilized during image search. In a leave-one-out validation study on a combination of four-chamber, two-chamber and short-axis cardiac MR views from 29 patients, low border positioning errors were found comparing manual and computer detected borders, whereas no statistically significant difference in contour area was present. The presented validation shows that the method combines a high robustness with clinically acceptable accuracy, and therefore is a promising tool to further automate the integral quantitative analysis of cardiac MR patient examinations.