9,124 research outputs found
Point Pair Feature based Object Detection for Random Bin Picking
Point pair features are a popular representation for free form 3D object
detection and pose estimation. In this paper, their performance in an
industrial random bin picking context is investigated. A new method to generate
representative synthetic datasets is proposed. This allows to investigate the
influence of a high degree of clutter and the presence of self similar
features, which are typical to our application. We provide an overview of
solutions proposed in literature and discuss their strengths and weaknesses. A
simple heuristic method to drastically reduce the computational complexity is
introduced, which results in improved robustness, speed and accuracy compared
to the naive approach
Occlusion Coherence: Detecting and Localizing Occluded Faces
The presence of occluders significantly impacts object recognition accuracy.
However, occlusion is typically treated as an unstructured source of noise and
explicit models for occluders have lagged behind those for object appearance
and shape. In this paper we describe a hierarchical deformable part model for
face detection and landmark localization that explicitly models part occlusion.
The proposed model structure makes it possible to augment positive training
data with large numbers of synthetically occluded instances. This allows us to
easily incorporate the statistics of occlusion patterns in a discriminatively
trained model. We test the model on several benchmarks for landmark
localization and detection including challenging new data sets featuring
significant occlusion. We find that the addition of an explicit occlusion model
yields a detection system that outperforms existing approaches for occluded
instances while maintaining competitive accuracy in detection and landmark
localization for unoccluded instances
Edaq530: a transparent, open-end and open-source measurement solution in natural science education
We present Edaq530, a low-cost, compact and easy-to-use digital measurement
solution consisting of a thumb-sized USB-to-sensor interface and a measurement
software. The solution is fully open-source, our aim being to provide a viable
alternative to professional solutions. Our main focus in designing Edaq530 has
been versatility and transparency. In this paper, we shall introduce the
capabilities of Edaq530, complement it by showing a few sample experiments, and
discuss the feedback we have received in the course of a teacher training
workshop in which the participants received personal copies of Edaq530 and
later made reports on how they could utilise Edaq530 in their teaching
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
A Pooling Approach to Modelling Spatial Relations for Image Retrieval and Annotation
Over the last two decades we have witnessed strong progress on modeling
visual object classes, scenes and attributes that have significantly
contributed to automated image understanding. On the other hand, surprisingly
little progress has been made on incorporating a spatial representation and
reasoning in the inference process. In this work, we propose a pooling
interpretation of spatial relations and show how it improves image retrieval
and annotations tasks involving spatial language. Due to the complexity of the
spatial language, we argue for a learning-based approach that acquires a
representation of spatial relations by learning parameters of the pooling
operator. We show improvements on previous work on two datasets and two
different tasks as well as provide additional insights on a new dataset with an
explicit focus on spatial relations
Geometric analysis of macromolecule organization within cryo-electron tomograms
Cryo-electron tomography (CET) provides unprecedented views into the native cellular environment at molecular resolution. While subtomogram analysis yields high-resolution native structures of molecular complexes, it also determines the precise positions and orientations of these macromolecules within the cell. Analyzing the geometric relationships between adjacent macromolecules can offer structural insights into molecular interactions and identify supramolecular ensembles. However, computation..
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