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2019


Towards Geometric Understanding of Motion
Towards Geometric Understanding of Motion

Ranjan, A.

University of Tübingen, December 2019 (phdthesis)

Abstract

The motion of the world is inherently dependent on the spatial structure of the world and its geometry. Therefore, classical optical flow methods try to model this geometry to solve for the motion. However, recent deep learning methods take a completely different approach. They try to predict optical flow by learning from labelled data. Although deep networks have shown state-of-the-art performance on classification problems in computer vision, they have not been as effective in solving optical flow. The key reason is that deep learning methods do not explicitly model the structure of the world in a neural network, and instead expect the network to learn about the structure from data. We hypothesize that it is difficult for a network to learn about motion without any constraint on the structure of the world. Therefore, we explore several approaches to explicitly model the geometry of the world and its spatial structure in deep neural networks.

The spatial structure in images can be captured by representing it at multiple scales. To represent multiple scales of images in deep neural nets, we introduce a Spatial Pyramid Network (SpyNet). Such a network can leverage global information for estimating large motions and local information for estimating small motions. We show that SpyNet significantly improves over previous optical flow networks while also being the smallest and fastest neural network for motion estimation. SPyNet achieves a 97% reduction in model parameters over previous methods and is more accurate.

The spatial structure of the world extends to people and their motion. Humans have a very well-defined structure, and this information is useful in estimating optical flow for humans. To leverage this information, we create a synthetic dataset for human optical flow using a statistical human body model and motion capture sequences. We use this dataset to train deep networks and see significant improvement in the ability of the networks to estimate human optical flow.

The structure and geometry of the world affects the motion. Therefore, learning about the structure of the scene together with the motion can benefit both problems. To facilitate this, we introduce Competitive Collaboration, where several neural networks are constrained by geometry and can jointly learn about structure and motion in the scene without any labels. To this end, we show that jointly learning single view depth prediction, camera motion, optical flow and motion segmentation using Competitive Collaboration achieves state-of-the-art results among unsupervised approaches.

Our findings provide support for our hypothesis that explicit constraints on structure and geometry of the world lead to better methods for motion estimation.

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PhD Thesis [BibTex]

2019


PhD Thesis [BibTex]


The acoustic hologram and particle manipulation with structured acoustic fields
The acoustic hologram and particle manipulation with structured acoustic fields

Melde, K.

Karlsruher Institut für Technologie (KIT), May 2019 (phdthesis)

Abstract
This thesis presents holograms as a novel approach to create arbitrary ultrasound fields. It is shown how any wavefront can simply be encoded in the thickness profile of a phase plate. Contemporary 3D-printers enable fabrication of structured surfaces with feature sizes corresponding to wavelengths of ultrasound up to 7.5 MHz in water—covering the majority of medical and industrial applications. The whole workflow for designing and creating acoustic holograms has been developed and is presented in this thesis. To reconstruct the encoded fields a single transducer element is sufficient. Arbitrary fields are demonstrated in transmission and reflection configurations in water and air and validated by extensive hydrophone scans. To complement these time-consuming measurements a new approach, based on thermography, is presented, which enables volumetric sound field scans in just a few seconds. Several original experiments demonstrate the advantages of using acoustic holograms for particle manipulation. Most notably, directed parallel assembly of microparticles in the shape of a projected acoustic image has been shown and extended to a fabrication method by fusing the particles in a polymerization reaction. Further, seemingly dynamic propulsion from a static hologram is demonstrated by controlling the phase gradient along a projected track. The necessary complexity to create ultrasound fields with set amplitude and phase distributions is easily managed using acoustic holograms. The acoustic hologram is a simple and cost-effective tool for shaping ultrasound fields with high-fidelity. It is expected to have an impact in many applications where ultrasound is employed.

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link (url) DOI [BibTex]


Dynamics of self-propelled colloids and their application as active matter
Dynamics of self-propelled colloids and their application as active matter

Choudhury, U.

University of Groningen, Zernike Institute for Advanced Materials, 2019 (phdthesis)

Abstract
In this thesis, the behavior of active particles spanning from single particle dynamics to collective behavior of many particles is explored. Active colloids are out-of equilibrium systems that have been studied extensively over the past 15 years. This thesis addresses several phenomena that arise in the field of active colloids.

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link (url) [BibTex]

link (url) [BibTex]

2011


Spatial Models of Human Motion
Spatial Models of Human Motion

Soren Hauberg

University of Copenhagen, 2011 (phdthesis)

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PDF [BibTex]

2011


PDF [BibTex]