12,657 research outputs found
A note on patch-based low-rank minimization for fast image denoising
Patch-based low-rank minimization for image processing attracts much
attention in recent years. The minimization of the matrix rank coupled with the
Frobenius norm data fidelity can be solved by the hard thresholding filter with
principle component analysis (PCA) or singular value decomposition (SVD). Based
on this idea, we propose a patch-based low-rank minimization method for image
denoising. The main denoising process is stated in three equivalent way: PCA,
SVD and low-rank minimization. Compared to recent patch-based sparse
representation methods, experiments demonstrate that the proposed method is
rather rapid, and it is effective for a variety of natural grayscale images and
color images, especially for texture parts in images. Further improvements of
this method are also given. In addition, due to the simplicity of this method,
we could provide an explanation of the choice of the threshold parameter,
estimation of PSNR values, and give other insights into this method.Comment: 4pages (two columns
Generalized Boundaries from Multiple Image Interpretations
Boundary detection is essential for a variety of computer vision tasks such
as segmentation and recognition. In this paper we propose a unified formulation
and a novel algorithm that are applicable to the detection of different types
of boundaries, such as intensity edges, occlusion boundaries or object category
specific boundaries. Our formulation leads to a simple method with
state-of-the-art performance and significantly lower computational cost than
existing methods. We evaluate our algorithm on different types of boundaries,
from low-level boundaries extracted in natural images, to occlusion boundaries
obtained using motion cues and RGB-D cameras, to boundaries from
soft-segmentation. We also propose a novel method for figure/ground
soft-segmentation that can be used in conjunction with our boundary detection
method and improve its accuracy at almost no extra computational cost
Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
In recent years, various shadow detection methods from a single image have
been proposed and used in vision systems; however, most of them are not
appropriate for the robotic applications due to the expensive time complexity.
This paper introduces a fast shadow detection method using a deep learning
framework, with a time cost that is appropriate for robotic applications. In
our solution, we first obtain a shadow prior map with the help of multi-class
support vector machine using statistical features. Then, we use a semantic-
aware patch-level Convolutional Neural Network that efficiently trains on
shadow examples by combining the original image and the shadow prior map.
Experiments on benchmark datasets demonstrate the proposed method significantly
decreases the time complexity of shadow detection, by one or two orders of
magnitude compared with state-of-the-art methods, without losing accuracy.Comment: 6 pages, 5 figures, Submitted to IROS 201
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