An evaluation framework for stereo-based driver assistance

This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury database. However, equivalent data for automotive or robotics applications rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases. Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on pixel and object level. In more detail we evaluate an intermediate representation called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km

RECOGNITION AND ESTIMATION OF HUMAN LOCOMOTION WITH HIDDEN MARKOV MODELS

INTRODUCTION: The Collaborative Research Centre “Humanoid Robots” situated at the University of Karlsruhe is aimed to construct a learning and cooperating service robot. To cope with its tasks it is necessary that the robot is able to identify diverse objects as well as different persons. Looking at stochastic models for pattern recognition Hidden Markov Models (HMMs) are described to be most suitable to classify time arranged data (Bilmes 2002). The objective of this study is to screen if the HMMs supply satisfying rates of recognition of human trajectory and angle data. METHOD: Kinematic data of eight men and three women was captured at different walking and running speed (1.2 m/s, 3 m/s, 4 m/s, 5 m/s) on a treadmill. Data acquisition was realised with an infrared camera system with a frequency of 250Hz. For each walking/running speed there were 120 gait cycles of every test person available. The construction and training of the stochastic model was based on the gait data. Due to the fixed sequence of gait phases a HMM with a simple linear topology was chosen. Each state of the HMM represented a phase of the gait cycle. The different states were equipped with Gaussian distributions and transition probabilities to model the run of the angles observed. The HMM modelling human gait best was selected and trained with data of 17 double gait cycles for each data sequence of every test person. RESULTS: The trained HMMs showed recognition rates from 63% to 100% for the observed data sequences for five male test persons. Highest rates could be obtained with Centre of Mass and head angles. For some test person recognition rates decreased with data of gait cycles that were captured towards the end of one run. DISCUSSION: The high recognition rates based on kinematic data of Centre of Mass were expected due to the different mean values of the test persons according to their body height. The decrease of recognition rates that could be observed at some of the test person on late data of one run seems to be caused by acclimatisation to treadmill running. The achieved recognition rates exceed rates typical for speech recognition (Rabiner 1989). A combination of different angle data seems to promise increasing recognition rates. CONCLUSION: The study showed that HMMs seem to be suitable to identify humans based on their kinematic gait data satisfyingly stable. According to dislocation of the Gaussian distributions it could be possible to suggest on systematic changes on patterns over changes in walking-/running speed. REFERENCES: Bilmes, J. (2002). What HMMs Can Do. UWEE Technical Report, No UWEETR-2002-2003, University of Washington, Dept. of EE. Rabiner, L. R. (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77 (2), 257-286 Acknowledgement V. Wank, Institute of Sport Science, University of Tübingen German Research Foundation – CRC 588 Humanoid Robot

Cooperative supramolecular polymerization of an amine-substituted naphthalene-diimide and its impact on excited state photophysical properties

A donor-acceptor-donor (D-A-D) type naphthalene-diimide (NDI-H) chromophore exhibits highly cooperative J-aggregation leading to nanotubular self-assembly and gelation in n-decane, as demonstrated by UV/Vis, FT-IR, photoluminescence and microscopy studies. Analysis of temperature-dependent UV/Vis spectra using the nucleation-elongation model and FT-IR data reveals the molecular origin of the cooperative nature of the self-assembly. The supramolecular polymerization is initiated by H-bonding up to a degree of polymerization similar to 20-25, which in a subsequent elongation step promotes J-aggregation in orthogonal direction leading to possibly a sheet-like structure that eventually produces nanotubes. Time-resolved fluorescence and absorption measurements demonstrate that such a tubular assembly enables very effective delocalization of excited states resulting in a remarkably prolonged excited state lifetime

One or two trainees per workplace in a structured multimodality training curriculum for laparoscopic surgery? Study protocol for a randomized controlled trial – DRKS00004675

BACKGROUND: Laparoscopy training courses have been established in many centers worldwide to ensure adequate skill learning before performing operations on patients. Different training modalities and their combinations have been compared regarding training effects. Multimodality training combines different approaches for optimal training outcome. However, no standards currently exist for the number of trainees assigned per workplace. METHODS: This is a monocentric, open, three-arm randomized controlled trial. The participants are laparoscopically-naive medical students from Heidelberg University. After a standardized introduction to laparoscopic cholecystectomy (LC) with online learning modules, the participants perform a baseline test for basic skills and LC performance on a virtual reality (VR) trainer. A total of 100 students will be randomized into three study arms, in a 2:2:1 ratio. The intervention groups participate individually (Group 1) or in pairs (Group 2) in a standardized and structured multimodality training curriculum. Basic skills are trained on the box and VR trainers. Procedural skills and LC modules are trained on the VR trainer. The control group (Group C) does not receive training between tests. A post-test is performed to reassess basic skills and LC performance on the VR trainer. The performance of a cadaveric porcine LC is then measured as the primary outcome using standardized and validated ratings by blinded experts with the Objective Structured Assessment of Technical Skills. The Global Operative Assessment of Laparoscopic Surgical skills score and the time taken for completion are used as secondary outcome measures as well as the improvement of skills and VR LC performance between baseline and post-test. Cognitive tests and questionnaires are used to identify individual factors that might exert influence on training outcome. DISCUSSION: This study aims to assess whether workplaces in laparoscopy training courses for beginners should be used by one trainee or two trainees simultaneously, by measuring the impact on operative performance and learning curves. Possible factors of influence, such as the role of observing the training partner, exchange of thoughts, active reflection, model learning, motivation, pauses, and sympathy will be explored in the data analysis. This study will help optimize the efficiency of laparoscopy training courses. TRIAL REGISTRATION NUMBER: DRKS0000467

The Alliance for Cellular Signaling Plasmid Collection: A Flexible Resource for Protein Localization Studies and Signaling Pathway Analysis

Cellular responses to inputs that vary both temporally and spatially are determined by complex relationships between the components of cell signaling networks. Analysis of these relationships requires access to a wide range of experimental reagents and techniques, including the ability to express the protein components of the model cells in a variety of contexts. As part of the Alliance for Cellular Signaling, we developed a robust method for cloning large numbers of signaling ORFs into Gateway® entry vectors, and we created a wide range of compatible expression platforms for proteomics applications. To date, we have generated over 3000 plasmids that are available to the scientific community via the American Type Culture Collection. We have established a website at www.signaling-gateway.org/data/plasmid/ that allows users to browse, search, and blast Alliance for Cellular Signaling plasmids. The collection primarily contains murine signaling ORFs with an emphasis on kinases and G protein signaling genes. Here we describe the cloning, databasing, and application of this proteomics resource for large scale subcellular localization screens in mammalian cell lines