| 1. |
- Kecheril Sadanandan, Sajith, et al.
(author)
-
Spheroid segmentation using multiscale deep adversarial networks
- 2017
-
In: IEEE International Conference on Computer Vision. - : IEEE. - 9781538610343
-
Conference paper (peer-reviewed)abstract
- In this work, we segment spheroids with different sizes, shapes, and illumination conditions from bright-field microscopy images. To segment the spheroids we create a novel multiscale deep adversarial network with different deep feature extraction layers at different scales. We show that linearly increasing the adversarial loss contribution results in a stable segmentation algorithm for our dataset. We qualitatively and quantitatively compare the performance of our deep adversarial network with two other networks without adversarial losses. We show that our deep adversarial network performs better than the other two networks at segmenting the spheroids from our 2D bright-field microscopy images.
|
|
| 2. |
- Kristan, Matej, et al.
(author)
-
The Visual Object Tracking VOT2017 challenge results
- 2017
-
In: 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2017). - : IEEE. - 9781538610343 ; , s. 1949-1972
-
Conference paper (peer-reviewed)abstract
- The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).
|
|
| 3. |
- Olsson, Carl, 1978, et al.
(author)
-
A Non-Convex Relaxation for Fixed-Rank Approximation
- 2017
-
In: IEEE International Conference on Computer Vision Workshops. - 2473-9936. - 9781538610343 ; 2018-January, s. 1809-1817
-
Conference paper (peer-reviewed)abstract
- This paper considers the problem of finding a low rank matrix from observations of linear combinations of its elements. It is well known that if the problem fulfills a restricted isometry property (RIP), convex relaxations using the nuclear norm typically work well and come with theoretical performance guarantees. On the other hand these formulations suffer from a shrinking bias that can severely degrade the solution in the presence of noise. In this theoretical paper we study an alternative non-convex relaxation that in contrast to the nuclear norm does not penalize the leading singular values and thereby avoids this bias. We show that despite its non-convexity the proposed formulation will in many cases have a single stationary point if a RIP holds. Our numerical tests show that our approach typically converges to a better solution than nuclear norm based alternatives even in cases when the RIP does not hold.
|
|
| 4. |
- Suchan, Jakob, et al.
(author)
-
Commonsense Scene Semantics for Cognitive Robotics : Towards Grounding Embodied Visuo-Locomotive Interactions
- 2017
-
In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538610343 - 9781538610350 ; , s. 742-750
-
Conference paper (peer-reviewed)abstract
- We present a commonsense, qualitative model for the semantic grounding of embodied visuo-spatial and locomotive interactions. The key contribution is an integrative methodology combining low-level visual processing with high-level, human-centred representations of space and motion rooted in artificial intelligence. We demonstrate practical applicability with examples involving object interactions, and indoor movement.
|
|
| 5. |
- Toft, Carl, 1990, et al.
(author)
-
Long-term 3D Localization and Pose from Semantic Labellings
- 2017
-
In: IEEE International Conference on Computer Vision Workshops. - 2473-9936. - 9781538610343 ; 2018-January, s. 650-659
-
Conference paper (peer-reviewed)abstract
- One of the major challenges in camera pose estimation and 3D localization is identifying features that are approximately invariant across seasons and in different weather and lighting conditions. In this paper, we present a method for performing accurate and robust six degrees-of-freedom camera pose estimation based only on the pixelwise semantic labelling of a single query image. Localization is performed using a sparse 3D model consisting of semantically labelled points and curves, and an error function based on how well these project onto corresponding curves in the query image is developed. The method is evaluated on the recently released Oxford Robotcar dataset, showing that by minimizing this error function, the pose can be recovered with decimeter accuracy in many cases.
|
|
| 6. |
- Tongbuasirilai, Tanaboon, 1983-, et al.
(author)
-
Efficient BRDF Sampling Using Projected Deviation Vector Parameterization
- 2017
-
In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538610343 - 9781538610350 ; , s. 153-158
-
Conference paper (peer-reviewed)abstract
- This paper presents a novel approach for efficient sampling of isotropic Bidirectional Reflectance Distribution Functions (BRDFs). Our approach builds upon a new parameterization, the Projected Deviation Vector parameterization, in which isotropic BRDFs can be described by two 1D functions. We show that BRDFs can be efficiently and accurately measured in this space using simple mechanical measurement setups. To demonstrate the utility of our approach, we perform a thorough numerical evaluation and show that the BRDFs reconstructed from measurements along the two 1D bases produce rendering results that are visually comparable to the reference BRDF measurements which are densely sampled over the 4D domain described by the standard hemispherical parameterization.
|
|
| 7. |
|
|
