28,206 research outputs found
Elimination of Glass Artifacts and Object Segmentation
Many images nowadays are captured from behind the glasses and may have
certain stains discrepancy because of glass and must be processed to make
differentiation between the glass and objects behind it. This research paper
proposes an algorithm to remove the damaged or corrupted part of the image and
make it consistent with other part of the image and to segment objects behind
the glass. The damaged part is removed using total variation inpainting method
and segmentation is done using kmeans clustering, anisotropic diffusion and
watershed transformation. The final output is obtained by interpolation. This
algorithm can be useful to applications in which some part of the images are
corrupted due to data transmission or needs to segment objects from an image
for further processing
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Background subtraction based on Local Shape
We present a novel approach to background subtraction that is based on the
local shape of small image regions. In our approach, an image region centered
on a pixel is mod-eled using the local self-similarity descriptor. We aim at
obtaining a reliable change detection based on local shape change in an image
when foreground objects are moving. The method first builds a background model
and compares the local self-similarities between the background model and the
subsequent frames to distinguish background and foreground objects.
Post-processing is then used to refine the boundaries of moving objects.
Results show that this approach is promising as the foregrounds obtained are
com-plete, although they often include shadows.Comment: 4 pages, 5 figures, 3 tabl
Fast and robust 3D feature extraction from sparse point clouds
Matching 3D point clouds, a critical operation in map building and localization, is difficult with Velodyne-type sensors due to the sparse and non-uniform point clouds that they produce. Standard methods from dense 3D point clouds are generally not effective. In this paper, we describe a featurebased approach using Principal Components Analysis (PCA) of neighborhoods of points, which results in mathematically principled line and plane features. The key contribution in this work is to show how this type of feature extraction can be done efficiently and robustly even on non-uniformly sampled point clouds. The resulting detector runs in real-time and can be easily tuned to have a low false positive rate, simplifying data association. We evaluate the performance of our algorithm on an autonomous car at the MCity Test Facility using a Velodyne HDL-32E, and we compare our results against the state-of-theart NARF keypoint detector. © 2016 IEEE
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