Ondrej Miksik

I am a DPhil student in Engineering Science at the University of Oxford co-advised by Philip Torr and Patrick Perez. I am funded by Technicolor and closely collaborate with Microsoft Research (I3D), the Nuffield Department of Clinical Neurosciences (Oxford) and groups at Stanford.

My main research interests are somewhere in the areas of computer vision/machine learning applied to wereable/mobile robotics and film post-production with a focus on understanding dynamic aspects of videos. Recently, I have been involved in development of smart glasses for visually impaired, distributed inference in large scale graphical models and temporal consistency for scene understanding algorithms.

Before that, I had the pleasure to work in the Center for Machine Perception (CMP) at Czech Technical University in Prague, visit the Robotics Institute at Carnegie Mellon University and the Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey. I received my MSc. and Bc. degrees from Brno University of Technology.

2015 - IEEE ICRA Best Robotic Vision Paper Award Finalist
2012 - Werner von Siemens Excellence Award 2012
2012 - Czech & Slovakia ACM chapter - Student Project of the Year
2012 - The Master Thesis of Year 2012 in Informatics
2012 - The Prize of the Dean (outstanding master’s thesis)
2012 - ABB University Award, the best MSc project (Robotics)
2011 - Student EEICT 2011, best paper award (MSc projects: cybernetics and automation)
2010 - The Prize of the Dean (outstanding bachelor's thesis)
2007 – Ceska hlava, national prize for the best pre-college scientific publication
2007 – The Herbert Hoover Young Engineer Award
2007 – The Prize of the Dean

Philip Torr, Patrick Perez, Vibhav Vineet, Jiri (George) Matas, Martial Hebert, Shahram Izadi, Drew Bagnell, Krystian Mikolajczyk, Daniel Munoz, Matthias Nießner, Jack Valmadre, Luca Bertinetto, Stuart Golodetz, Michael Sapienza, Anurag Arnab, Victor Adrian Prisacariu, Julien Valentin, Stephan Liwicki, Christopher Zach, Morten Lidegaard, Stephen Hicks, Tomas Vojir, Jan Sochman, Petr Petyovsky, Ludek Zalud, Pavel Jura, Miloslav Richter, Ram Prasaath, ...

Coarse-to-fine Planar Regularization for Dense Monocular Depth Estimation
Liwicki S. and Zach C. and Miksik O. and Torr P.H.S.
In Proceedings of the European Conference on Computer Vision (ECCV) 2016, Amsterdam, Netherlands
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Staple: Complementary Learners for Real-Time Tracking
Bertinetto L., Valmadre J., Golodetz S., Miksik O. and Torr P.H.S.
In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2016, Las Vegas, USA
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The Semantic Paintbrush: Interactive 3D Mapping and Recognition in Large Outdoor Spaces
Miksik O.*, Vineet V.*, Lidegaard M., Prasaath R., Nießner M., Golodetz S., Hicks S.L., Perez P., Izadi S. and Torr P.H.S.
In Proceedings of the 33nd annual ACM conference on Human factors in computing systems (CHI) 2015, Seoul, South Korea
* Joint first authors
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Incremental Dense Semantic Stereo Fusion for Large-Scale Semantic Scene Reconstruction
Vineet V.*, Miksik O.*, Lidegaard M., Nießner M., Golodetz S., Prisacariu V.A., Kähler O., Murray D.W., Izadi S., Perez P. and Torr P.H.S.
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2015, Seattle, USA
* Joint first authors
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Joint Object-Material Category Segmentation from Audio-Visual Cues
Arnab A., Sapienza M., Golodetz S., Valentin J., Miksik O., Izadi S. and Torr P.H.S.
In Proceedings of the British Machine Vision Conference (BMVC) 2015, Swansea, UK
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Incremental Dense Multi-modal 3D Scene Reconstruction
Miksik O., Amar Y., Vineet V., Perez P. and Torr P.H.S.
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2015, Hamburg, Germany
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Distributed Non-Convex ADMM-inference in Large-scale Random Fields
Miksik O., Vineet V., Perez P. and Torr P.H.S.
In Proceedings of the British Machine Vision Conference (BMVC) 2014, Nottingham, UK
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Efficient Temporal Consistency for Streaming Video Scene Analysis
Miksik O., Munoz D., Bagnell, J. A. and Hebert M.
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2013, Karlsruhe, Germany
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Abstract:

We address the problem of image-based scene analysis from streaming video, as would be seen from a moving platform, in order to efficiently generate spatially and temporally consistent predictions of semantic categories over time. In contrast to previous techniques which typically address this problem in batch and/or through graphical models, we demonstrate that by learning visual similarities between pixels across frames, a simple filtering algorithm is able to achieve high performance predictions in an efficient and online/causal manner. Our technique is a meta-algorithm that can be efficiently wrapped around any scene analysis technique that produces a per-pixel semantic label distribution. We validate our approach over three different scene analysis techniques on three different datasets that contain different semantic object categories. Our experiments demonstrate our approach is very efficient in practice and substantially improves the quality of predictions over time.

Video:

Images:

Evaluation of Local Detectors and Descriptors for Fast Feature Matching
Miksik O. and Mikolajczyk K.
In Proceedings of the International Conference on Pattern Recognition (ICPR) 2012, Tsukuba Science City, Japan
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Rapid Vanishing Point Estimation for General Road Detection
Miksik O.
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2012, St. Paul, USA
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Abstract:

This paper deals with fast vanishing point estimation for autonomous robot navigation. Preceding approaches showed suitable results and vanishing point estimation was used in many robotics tasks, especially in the detection of ill-structured roads. The main drawback of such approaches is the computational complexity - the possibilities of hardware accelerations are mentioned in many papers, however, we believe, that the biggest benefit of a vanishing point estimation algorithm is for primarily tele-operated robots in the case of signal loss, etc., that cannot use specialized hardware just for this feature. In this paper, we investigate possibilities of an efficient implementation by the expansion of Gabor wavelets into a linear combination of Haar-like box functions to perform fast filtering via integral image trick and discuss the utilization of superpixels in the voting scheme to provide a significant speed-up (more than 40 times), while we loose only 3-5% in precision.

Decomposition of Gabor wavelets:

Dictionary: 240 randomly selected base vectors from the dictionary.

Decomposition of a Gabor wavelet into a linear combination of Haar-like box functions.

Coarse-to-fine voting scheme:

Input image (a)

Dominant orientations: overlayed (b), jet (c); Superpixels (d)

Coarse-to-fine voting scheme (e) and (f); Output (g)

Results:

Robust Detection of Shady and Highlighted Roads for Monocular Camera Based Navigation of UGV
Miksik O., Petyovsky P., Zalud L. and Jura P.
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2011, Shanghai, China
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Abstract:

This paper addresses the problem of UGV navigation in various environments and lightning conditions. Previous approaches use a combination of different sensors, or work well, only in scenarios with noticeable road marking or borders. Our robot is used for chemical, nuclear and biological contamination measurement. Thus, to avoid complications with decontamination, only a monocular camera serves as a sensor since it is already equipped. In this paper, we propose a novel approach - a fusion of frequency based vanishing point estimation and probabilistically based color segmentation. Detection of a vanishing point, is based on the estimation of a texture flow, produced by a bank of Gabor wavelets and a voting function. Next, the vanishing point defines the training area, which is used for self-supervised learning of color models. Finally, road patches are selected by measuring of the roadness score. A few rules deal with dark cast shadows, overexposed highlights and adaptivity speed. In addition to the robustness of our system, it is easy-to-use since no calibration is needed.

Results:

The blue star denotes estimated vanishing point, yellow trapezoid is the training area, green and blue areas are shadows and highlight preprocessors and red area is the thresholded non-road region.

Video 2 (long):

Video 1 Video 2

Adapting Polynomial Mahalanobis Distance for Self-supervised Learning in an Outdoor Environment
Richter M., Petyovsky P. and Miksik O.
In Proceedings of the IEEE International Conference on Machine Learning and Applications (ICMLA) 2011, Honolulu, USA
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Abstract:

This paper addresses the problem of autonomous navigation of UGV in an unstructured environment. Generally, state-of-the-art approaches use color based segmentation of road/non-road regions in particular. There arises an important question, how is the distance between an input pixel and a color model measured. Many algorithms employ Mahalanobis distance, since Mahalanobis distance better follows the data distribution, however it is assumed, that the data points have a normal distribution. Recently proposed Polynomial Mahalanobis Distance (PMD) represents more discriminative metric, which provides superior results in an unstructured terrain, especially, if the road is barely visible even for humans. In this paper, we discuss properties of the Polynomial Mahalanobis Distance, and propose a novel framework - A Three Stage Algorithm (TSA), which deals with both, picking of suitable data points from the training area as well as self-supervised learning algorithm for long-term road representation.

Results:

The video shows detected road/non-road regions as well as it visualizes computed Polynomial Mahalanobis Distance with Three Stage Algorithm (white = zero).

Video

Dynamic Scene Understanding for Mobile Robot Navigation
Miksik O.
Master's thesis, advisor: Dr Ludek Zalud, supervisor: prof. Martial Hebert
consultants: Daniel Munoz, Dr J. Andrew Bagnell
Brno University of Technology, 2012
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Abstract:

The thesis deals with dynamic scene understanding for mobile robot navigation. In the first part, we propose a novel approach to self-supervised learning - a fusion of frequency based vanishing point estimation and probabilistically based color segmentation. Detection of a vanishing point is based on the estimation of a texture flow produced by a bank of Gabor wavelets and a voting function. Next, the vanishing point defines the training area which is used for self-supervised learning of color models. Finally, road patches are selected by measuring roadness score. A few rules deal with dark cast shadows, overexposed hightlights and adaptivity speed. In addition to that, the whole vanishing point estimation is refined - Gabor filters are approximated by Haar-like box functions, which enables efficient filtering via integral image trick. The tightest bottleneck, a voting scheme, is modified to coarse-to-fine, which provides a significant speed-up (more than 40x), while we loose only 3-5% in precision.

The second part proposes a smoothing filter for spatio-temporal consistency of structured predictions, that are useful for more mature systems. The key part of the proposed smoothing filter is a new similarity metric, which is more discriminative than the standard Euclidean distance and can be used for various computer vision tasks. The smoothing filter first estimates optical flow to define a local neighborhood. This neighborhood is used for recursive averaging based on the similarity metric. The total accuracy of proposed method measured on pixels with inconsistent labels between the raw and smooth predictions is almost 18% higher than original predictions. Although we have used SHIM, the algorithm can be combined with any other system for structured predictions (MRF/CRF, ...). The proposed smoothing filter represents a first step towards full inference.

Results:

Results of proposed systems: Self-supervised learning (a), Spatio-temporal consistency (b)

See respective papers for more details.

Miksik O.: Fast Feature Matching for Simultaneous Localization and Mapping
Bachelor's thesis, advisor: Dr Krystian Mikolajczyk
Brno University of Technology, 2010
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Abstract:

The thesis deals with the fast feature matching for simultaneous localization and mapping. A brief description of local features invariant to scale, rotation, translation and affine transformations, their detectors and descriptors are included. In general, real-time response for matching is crucial for various computer vision applications (SLAM, object retrieval, wide-robust baseline stereo, tracking, ...). We solve the problem of sub-linear search complexity by multiple randomised KD-trees. In addition, we propose a novel way of splitting dataset into the multiple trees. Moreover, a new evaluation package for general use (KD-trees, BBD-trees, k-means trees) was developed.