Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop

User-centered design and evaluation of interactive segmentation methods for medical images

Segmentation of medical images is a challenging task that aims to identify a particular structure present on the image. Among the existing methods involving the user at different levels, from a fully-manual to a fully-automated task, interactive segmentation methods provide assistance to the user during the task to reduce the variability in the results and allow occasional corrections of segmentation failures. Therefore, they offer a compromise between the segmentation efficiency and the accuracy of the results. It is the user who judges whether the results are satisfactory and how to correct them during the segmentation, making the process subject to human factors. Despite the strong influence of the user on the outcomes of a segmentation task, the impact of such factors has received little attention, with the literature focusing the assessment of segmentation processes on computational performance. Yet, involving the user performance in the analysis is more representative of a realistic scenario. Our goal is to explore the user behaviour in order to improve the efficiency of interactive image segmentation processes. This is achieved through three contributions. First, we developed a method which is based on a new user interaction mechanism to provide hints as to where to concentrate the computations. This significantly improves the computation efficiency without sacrificing the quality of the segmentation. The benefits of using such hints are twofold: (i) because our contribution is based on user interaction, it generalizes to a wide range of segmentation methods, and (ii) it gives comprehensive indications about where to focus the segmentation search. The latter advantage is used to achieve the second contribution. We developed an automated method based on a multi-scale strategy to: (i) reduce the user’s workload and, (ii) improve the computational time up to tenfold, allowing real-time segmentation feedback. Third, we have investigated the effects of such improvements in computations on the user’s performance. We report an experiment that manipulates the delay induced by the computation time while performing an interactive segmentation task. Results reveal that the influence of this delay can be significantly reduced with an appropriate interaction mechanism design. In conclusion, this project provides an effective image segmentation solution that has been developed in compliance with user performance requirements. We validated our approach through multiple user studies that provided a step forward into understanding the user behaviour during interactive image segmentation

Towards Predictive Rendering in Virtual Reality

The strive for generating predictive images, i.e., images representing radiometrically correct renditions of reality, has been a longstanding problem in computer graphics. The exactness of such images is extremely important for Virtual Reality applications like Virtual Prototyping, where users need to make decisions impacting large investments based on the simulated images. Unfortunately, generation of predictive imagery is still an unsolved problem due to manifold reasons, especially if real-time restrictions apply. First, existing scenes used for rendering are not modeled accurately enough to create predictive images. Second, even with huge computational efforts existing rendering algorithms are not able to produce radiometrically correct images. Third, current display devices need to convert rendered images into some low-dimensional color space, which prohibits display of radiometrically correct images. Overcoming these limitations is the focus of current state-of-the-art research. This thesis also contributes to this task. First, it briefly introduces the necessary background and identifies the steps required for real-time predictive image generation. Then, existing techniques targeting these steps are presented and their limitations are pointed out. To solve some of the remaining problems, novel techniques are proposed. They cover various steps in the predictive image generation process, ranging from accurate scene modeling over efficient data representation to high-quality, real-time rendering. A special focus of this thesis lays on real-time generation of predictive images using bidirectional texture functions (BTFs), i.e., very accurate representations for spatially varying surface materials. The techniques proposed by this thesis enable efficient handling of BTFs by compressing the huge amount of data contained in this material representation, applying them to geometric surfaces using texture and BTF synthesis techniques, and rendering BTF covered objects in real-time. Further approaches proposed in this thesis target inclusion of real-time global illumination effects or more efficient rendering using novel level-of-detail representations for geometric objects. Finally, this thesis assesses the rendering quality achievable with BTF materials, indicating a significant increase in realism but also confirming the remainder of problems to be solved to achieve truly predictive image generation

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

Physically-based Animation of ‘Sticky Lips’

Producing a realistic animation of the face is challenging due to the familiarity people have with facial expressions and movements. In recent years there has been increased activity in the use of physically-based models to create realistic animations of soft-tissue structures, as well as interest in modelling more subtle effects occurring in the mouth. This thesis presents a physically-based model of the mouth. In particular, the model recreates the effect of saliva on the movement of the lips, a largely unexplored topic. The research is composed of four novel components. The first component is a physically-based model of the mouth featuring a new stickiness model, recreating the effect of the saliva on the movements of the mouth. The model is supported by a novel moisture model which controls the stickiness level over time. The stickiness model itself provides more realistic behaviour than the few other current models and reproduces complex effects which can be seen in real mouths. The second component is a perceptual evaluation of the realism of mouth animations which incorporate stickiness. The evaluation concludes that the inclusion of the stickiness model results in an improvement in perceived realism of animations of the mouth. The third component is a new analysis process for capturing information about mouth movements from video. This analysis process is used to evaluate the developed model by comparing it against videos of real mouths. The analysis demonstrates that the stickiness model provides an improvement in accuracy of animation compared to models that do not incorporate stickiness. The fourth component is a corpus of mouth videos in which utterances and actions are recorded at varying levels of lip stickiness to produce high frame rate close up mouth videos which show stickiness effects in a variety of participants. This corpus is used in the objective evaluation

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Annals of Scientific Society for Assembly, Handling and Industrial Robotics

This Open Access proceedings present a good overview of the current research landscape of industrial robots. The objective of MHI Colloquium is a successful networking at academic and management level. Thereby the colloquium is focussing on a high level academic exchange to distribute the obtained research results, determine synergetic effects and trends, connect the actors personally and in conclusion strengthen the research field as well as the MHI community. Additionally there is the possibility to become acquainted with the organizing institute. Primary audience are members of the scientific association for assembly, handling and industrial robots (WG MHI)

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications