586 research outputs found
CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition
Most of the traditional work on intrinsic image decomposition rely on
deriving priors about scene characteristics. On the other hand, recent research
use deep learning models as in-and-out black box and do not consider the
well-established, traditional image formation process as the basis of their
intrinsic learning process. As a consequence, although current deep learning
approaches show superior performance when considering quantitative benchmark
results, traditional approaches are still dominant in achieving high
qualitative results. In this paper, the aim is to exploit the best of the two
worlds. A method is proposed that (1) is empowered by deep learning
capabilities, (2) considers a physics-based reflection model to steer the
learning process, and (3) exploits the traditional approach to obtain intrinsic
images by exploiting reflectance and shading gradient information. The proposed
model is fast to compute and allows for the integration of all intrinsic
components. To train the new model, an object centered large-scale datasets
with intrinsic ground-truth images are created. The evaluation results
demonstrate that the new model outperforms existing methods. Visual inspection
shows that the image formation loss function augments color reproduction and
the use of gradient information produces sharper edges. Datasets, models and
higher resolution images are available at https://ivi.fnwi.uva.nl/cv/retinet.Comment: CVPR 201
Person Re-identification by Local Maximal Occurrence Representation and Metric Learning
Person re-identification is an important technique towards automatic search
of a person's presence in a surveillance video. Two fundamental problems are
critical for person re-identification, feature representation and metric
learning. An effective feature representation should be robust to illumination
and viewpoint changes, and a discriminant metric should be learned to match
various person images. In this paper, we propose an effective feature
representation called Local Maximal Occurrence (LOMO), and a subspace and
metric learning method called Cross-view Quadratic Discriminant Analysis
(XQDA). The LOMO feature analyzes the horizontal occurrence of local features,
and maximizes the occurrence to make a stable representation against viewpoint
changes. Besides, to handle illumination variations, we apply the Retinex
transform and a scale invariant texture operator. To learn a discriminant
metric, we propose to learn a discriminant low dimensional subspace by
cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is
learned on the derived subspace. We also present a practical computation method
for XQDA, as well as its regularization. Experiments on four challenging person
re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show
that the proposed method improves the state-of-the-art rank-1 identification
rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and
extracted features please visit
http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda
Gradient attenuation as an emergent property of reset-based Retinex models
The Retinex image filtering algorithms have been inspired by experimental findings on the behavior of the Human Vision System. They are known to locally adjust image color and contrast by preserving edges and attenuating gradients. In a reference formulation of the algorithm by Land and McCann, edge preservation and gradient attenuation are granted by two ad-hoc mechanisms: called respectively reset (the distinctive feature of all the Retinex algorithms) and thresholding. A somehow unanticipated finding is that gradient attenuation is also observed with algorithm variants that do not include the latter mechanism, which was explicitly devised to implement gradient attenuation. In this work, we provide an analytic demonstration of the capability of Retinex models to attenuate gradients using only the "reset" mechanism, combined with the local character of the mutual pixel influences. We show that this capability is an emergent property of all the reset-based Retinex models
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