3,758 research outputs found
Convolutional Color Constancy
Color constancy is the problem of inferring the color of the light that
illuminated a scene, usually so that the illumination color can be removed.
Because this problem is underconstrained, it is often solved by modeling the
statistical regularities of the colors of natural objects and illumination. In
contrast, in this paper we reformulate the problem of color constancy as a 2D
spatial localization task in a log-chrominance space, thereby allowing us to
apply techniques from object detection and structured prediction to the color
constancy problem. By directly learning how to discriminate between correctly
white-balanced images and poorly white-balanced images, our model is able to
improve performance on standard benchmarks by nearly 40%
The Visual Centrifuge: Model-Free Layered Video Representations
True video understanding requires making sense of non-lambertian scenes where
the color of light arriving at the camera sensor encodes information about not
just the last object it collided with, but about multiple mediums -- colored
windows, dirty mirrors, smoke or rain. Layered video representations have the
potential of accurately modelling realistic scenes but have so far required
stringent assumptions on motion, lighting and shape. Here we propose a
learning-based approach for multi-layered video representation: we introduce
novel uncertainty-capturing 3D convolutional architectures and train them to
separate blended videos. We show that these models then generalize to single
videos, where they exhibit interesting abilities: color constancy, factoring
out shadows and separating reflections. We present quantitative and qualitative
results on real world videos.Comment: Appears in: 2019 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2019). This arXiv contains the CVPR Camera Ready version of
the paper (although we have included larger figures) as well as an appendix
detailing the model architectur
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
While most scene flow methods use either variational optimization or a strong
rigid motion assumption, we show for the first time that scene flow can also be
estimated by dense interpolation of sparse matches. To this end, we find sparse
matches across two stereo image pairs that are detected without any prior
regularization and perform dense interpolation preserving geometric and motion
boundaries by using edge information. A few iterations of variational energy
minimization are performed to refine our results, which are thoroughly
evaluated on the KITTI benchmark and additionally compared to state-of-the-art
on MPI Sintel. For application in an automotive context, we further show that
an optional ego-motion model helps to boost performance and blends smoothly
into our approach to produce a segmentation of the scene into static and
dynamic parts.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
Motion of glossy objects does not promote separation of lighting and surface colour
The surface properties of an object, such as texture, glossiness or colour, provide important cues to its identity. However, the actual visual stimulus received by the eye is determined by both the properties of the object and the illumination. We tested whether operational colour constancy for glossy objects (the ability to distinguish changes in spectral reflectance of the object, from changes in the spectrum of the illumination) was affected by rotational motion of either the object or the light source. The different chromatic and geometric properties of the specular and diffuse reflections provide the basis for this discrimination, and we systematically varied specularity to control the available information. Observers viewed animations of isolated objects undergoing either lighting or surface-based spectral transformations accompanied by motion. By varying the axis of rotation, and surface patterning or geometry, we manipulated: (i) motion-related information about the scene, (ii) relative motion between the surface patterning and the specular reflection of the lighting, and (iii) image disruption caused by this motion. Despite large individual differences in performance with static stimuli, motion manipulations neither improved nor degraded performance. As motion significantly disrupts frameby-frame low-level image statistics, we infer that operational constancy depends on a high-level scene interpretation, which is maintained in all condition
Occlusion Aware Unsupervised Learning of Optical Flow
It has been recently shown that a convolutional neural network can learn
optical flow estimation with unsupervised learning. However, the performance of
the unsupervised methods still has a relatively large gap compared to its
supervised counterpart. Occlusion and large motion are some of the major
factors that limit the current unsupervised learning of optical flow methods.
In this work we introduce a new method which models occlusion explicitly and a
new warping way that facilitates the learning of large motion. Our method shows
promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets.
Especially on KITTI dataset where abundant unlabeled samples exist, our
unsupervised method outperforms its counterpart trained with supervised
learning.Comment: CVPR 2018 Camera-read
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