Synchronization of two independently moving cameras without feature correspondences

In this work, a method that synchronizes two video sequences is proposed. Unlike previous methods, which require the existence of correspondences between features tracked in the two sequences, and/or that the cameras are static or jointly moving, the proposed approach does not impose any of these constraints. It works when the cameras move independently, even if different features are tracked in the two sequences. The assumptions underlying the proposed strategy are that the intrinsic parameters of the cameras are known and that two rigid objects, with independent motions on the scene, are visible in both sequences. The relative motion between these objects is used as clue for the synchronization. The extrinsic parameters of the cameras are assumed to be unknown. A new synchronization algorithm for static or jointly moving cameras that see (possibly) different parts of a common rigidly moving object is also proposed. Proof-of-concept experiments that illustrate the performance of these methods are presented, as well as a comparison with a state-of-the-art approach.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

Dry grasslands of Central-Eastern and South-Eastern Europe shaped by environmental heterogeneity and human land use – Editorial to the 10th Dry Grassland Special Feature

Der diesjährige 10. Trockenrasen-Sonderteil von Tuexenia beginnt mit einem Bericht über die aktuellen Aktivitäten der European Dry Grassland Group (EDGG). Zunächst geben wir einen Überblick über die Entwicklung der Mitgliederzahl. Dann berichten wir vom letzten European Dry Grassland Meeting in Tula (Russland, 2014) und vom letzten European Dry Grassland Field Workshop in Navarra (Spanien, 2014) und informieren über künftige Veranstaltungen der EDGG. Anschließend erläutern wir die Publikationsaktivitäten der EDGG. Im zweiten Teil des Editorials geben wir eine Einführung zu den fünf Artikeln des diesjährigen Trockenrasen-Sonderteils. Zwei Artikel beschäftigen sich mit der Syntaxonomie von Trockenrasen in Ost- bzw. Südosteuropa: der eine präsentiert erstmalig eine Gesamtklassifikation der Trockenrasengesellschaften Serbiens und des Kosovo während der andere Originalaufnahmen sub-montaner Graslandgesellschaften aus den bislang kaum untersuchten ukrainischen Ostkarpaten analysiert. Zwei weitere Artikel behandeln Trockenrasen-Feuchtwiesen-Komplexe im ungarischen Tiefland: Der eine behandelt den Einfluss der Landnutzung auf die Phytodiversität von Steppen und Feuchtwiesen, der andere den Einfluss von Niederschlagsschwankungen in einem Zeitraum von drei Jahren auf die Ausbildung salzbeeinflusster Steppen-Feuchtwiesen-Komplexe. Der fünfte Artikel analysiert landnutzungsbedingte Veränderungen des Graslands des Tsentralen-Balkan-Nationalparks in Bulgarien über einen Zeitraum von 65 Jahren

Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography using Supervised Learning and a Novel Morphometric Feature Descriptor

Patient-specific biomechanical models including local bone mineral density and anisotropy have gained importance for assessing musculoskeletal disorders. However the trabecular bone anisotropy captured by high-resolution imaging is only available at the peripheral skeleton in clinical practice. In this work, we propose a supervised learning approach to predict trabecular bone anisotropy that builds on a novel set of pose invariant feature descriptors. The statistical relationship between trabecular bone anisotropy and feature descriptors were learned from a database of pairs of high resolution QCT and clinical QCT reconstructions. On a set of leave-one-out experiments, we compared the accuracy of the proposed approach to previous ones, and report a mean prediction error of 6% for the tensor norm, 6% for the degree of anisotropy and 19◦ for the principal tensor direction. These findings show the potential of the proposed approach to predict trabecular bone anisotropy from clinically available QCT images.

Regularizing Image Reconstruction for Gradient-Domain Rendering with Feature Patches

We present a novel algorithm to reconstruct high-quality images from sampled pixels and gradients in gradient-domain rendering. Our approach extends screened Poisson reconstruction by adding additional regularization constraints. Our key idea is to exploit local patches in feature images, which contain per-pixels normals, textures, position, etc., to formulate these constraints. We describe a GPU implementation of our approach that runs on the order of seconds on megapixel images. We demonstrate a significant improvement in image quality over screened Poisson reconstruction under the L1 norm. Because we adapt the regularization constraints to the noise level in the input, our algorithm is consistent and converges to the ground truth.

Computer-based summative assessment of clinical reasoning in clerkships: A mixed-method comparison of key feature problems with case-based multiple choice questions

Background: It is yet unclear if there are differences between using electronic key feature problems (KFPs) or electronic case-based multiple choice questions (cbMCQ) for the assessment of clinical decision making. Summary of Work: Fifth year medical students were exposed to clerkships which ended with a summative exam. Assessment of knowledge per exam was done by 6-9 KFPs, 9-20 cbMCQ and 9-28 MC questions. Each KFP consisted of a case vignette and three key features (KF) using “long menu” as question format. We sought students’ perceptions of the KFPs and cbMCQs in focus groups (n of students=39). Furthermore statistical data of 11 exams (n of students=377) concerning the KFPs and (cb)MCQs were compared. Summary of Results: The analysis of the focus groups resulted in four themes reflecting students’ perceptions of KFPs and their comparison with (cb)MCQ: KFPs were perceived as (i) more realistic, (ii) more difficult, (iii) more motivating for the intense study of clinical reasoning than (cb)MCQ and (iv) showed an overall good acceptance when some preconditions are taken into account. The statistical analysis revealed that there was no difference in difficulty; however KFP showed a higher discrimination and reliability (G-coefficient) even when corrected for testing times. Correlation of the different exam parts was intermediate. Conclusions: Students perceived the KFPs as more motivating for the study of clinical reasoning. Statistically KFPs showed a higher discrimination and higher reliability than cbMCQs. Take-home messages: Including KFPs with long menu questions into summative clerkship exams seems to offer positive educational effects.

Computerbasierte Prüfung von klinischem Denken in der klinischen Ausbildung: Ein Mixed-Method Vergleich von Key Feature Problemen mit fallbasierten Multiple Choice Typ A Fragen

Fragestellung/Einleitung: Es ist unklar inwiefern Unterschiede bestehen im Einsatz von Key Feature Problemen (KFP) mit Long Menu Fragen und fallbasierten Typ A Fragen (FTA) für die Überprüfung des klinischen Denkens (Clinical Reasoning) in der klinischen Ausbildung von Medizinstudierenden. Methoden: Medizinstudierende des fünften Studienjahres nahmen an ihrer klinischen Pädiatrie-Rotation teil, die mit einer summativen Prüfung endete. Die Überprüfung des Wissen wurde pro Prüfung elektronisch mit 6-9 KFP [1], [3], 9-20 FTA und 9-28 nichtfallbasierten Multiple Choice Fragen (NFTA) durchgeführt. Jedes KFP bestand aus einer Fallvignette und drei Key Features und nutzen ein sog. Long Menu [4] als Antwortformat. Wir untersuchten die Perzeption der KFP und FTA in Focus Gruppen [2] (n of students=39). Weiterhin wurden die statistischen Kennwerte der KFP und FTA von 11 Prüfungen (n of students=377) verglichen. Ergebnisse: Die Analyse der Fokusgruppen resultierte in vier Themen, die die Perzeption der KFP und deren Vergleich mit FTA darstellten: KFP wurden als 1. realistischer, 2. schwerer, und 3. motivierender für das intensive Selbststudium des klinischen Denkens als FTA aufgenommen und zeigten 4. insgesamt eine gute Akzeptanz sofern gewisse Voraussetzungen berücksichtigt werden. Die statistische Auswertung zeigte keinen Unterschied im Schwierigkeitsgrad; jedoch zeigten die KFP eine höhere Diskrimination und Reliabilität (G-coefficient) selbst wenn für die Prüfungszeit korrigiert wurde. Die Korrelation der verschiedenen Prüfungsteile war mittel. Diskussion/Schlussfolgerung: Die Studierenden erfuhren die KFP als motivierenden für das Selbststudium des klinischen Denkens. Statistisch zeigten die KFP eine grössere Diskrimination und höhere Relibilität als die FTA. Der Einbezug von KFP mit Long Menu in Prüfungen des klinischen Studienabschnitts erscheint vielversprechend und einen „educational effect“ zu haben.

Who cares about my feature request?

Previous studies on issue tracking systems for open source software (OSS) focused mainly on requests for bug fixes. However, requests to add a new feature or an improvement to an OSS project are often also made in an issue tracking system. These inquiries are particularly important because they determine the further development of the software. This study examines if there is any difference between requests of the IBM developer community and other sources in terms of the likelihood of successful implementation. Our study consists of a case study of the issue tracking system BugZilla in the Eclipse integrated development environment (IDE). Our hypothesis, which was that feature requests from outsiders have less chances of being implemented, than feature requests from IBM developers, was confirmed.

Automatic Stabilizer Feature of Fixed Exchange Rate Regimes in Emerging Markets

This paper shows that countries characterized by a financial accelerator mechanism may reverse the usual finding of the literature -- flexible exchange rate regimes do a worse job of insulating open economies from external shocks. I obtain this result with a calibrated small open economy model that endogenizes foreign interest rates by linking them to the banking sector's foreign currency leverage. This relationship renders exchange rate policy more important compared to the usual exogeneity assumption. I find empirical support for this prediction using the Local Projections method. Finally, 2nd order approximation to the model finds larger welfare losses under flexible regimes.

Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction

Radiomics is the high-throughput extraction and analysis of quantitative image features. For non-small cell lung cancer (NSCLC) patients, radiomics can be applied to standard of care computed tomography (CT) images to improve tumor diagnosis, staging, and response assessment. The first objective of this work was to show that CT image features extracted from pre-treatment NSCLC tumors could be used to predict tumor shrinkage in response to therapy. This is important since tumor shrinkage is an important cancer treatment endpoint that is correlated with probability of disease progression and overall survival. Accurate prediction of tumor shrinkage could also lead to individually customized treatment plans. To accomplish this objective, 64 stage NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. Quantitative image features were extracted and principal component regression with simulated annealing subset selection was used to predict shrinkage. Cross validation and permutation tests were used to validate the results. The optimal model gave a strong correlation between the observed and predicted shrinkages with . The second objective of this work was to identify sets of NSCLC CT image features that are reproducible, non-redundant, and informative across multiple machines. Feature sets with these qualities are needed for NSCLC radiomics models to be robust to machine variation and spurious correlation. To accomplish this objective, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. For each machine, quantitative image features with concordance correlation coefficient values greater than 0.90 were considered reproducible. Multi-machine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering. The findings showed that image feature reproducibility and redundancy depended on both the CT machine and the CT image type (average cine 4D-CT imaging vs. end-exhale cine 4D-CT imaging vs. helical inspiratory breath-hold 3D CT). For each image type, a set of cross-machine reproducible, non-redundant, and informative image features was identified. Compared to end-exhale 4D-CT and breath-hold 3D-CT, average 4D-CT derived image features showed superior multi-machine reproducibility and are the best candidates for clinical correlation.