10,756 research outputs found
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
Efforts to automate the reconstruction of neural circuits from 3D electron
microscopic (EM) brain images are critical for the field of connectomics. An
important computation for reconstruction is the detection of neuronal
boundaries. Images acquired by serial section EM, a leading 3D EM technique,
are highly anisotropic, with inferior quality along the third dimension. For
such images, the 2D max-pooling convolutional network has set the standard for
performance at boundary detection. Here we achieve a substantial gain in
accuracy through three innovations. Following the trend towards deeper networks
for object recognition, we use a much deeper network than previously employed
for boundary detection. Second, we incorporate 3D as well as 2D filters, to
enable computations that use 3D context. Finally, we adopt a recursively
trained architecture in which a first network generates a preliminary boundary
map that is provided as input along with the original image to a second network
that generates a final boundary map. Backpropagation training is accelerated by
ZNN, a new implementation of 3D convolutional networks that uses multicore CPU
parallelism for speed. Our hybrid 2D-3D architecture could be more generally
applicable to other types of anisotropic 3D images, including video, and our
recursive framework for any image labeling problem
Maximum Persistency in Energy Minimization
We consider discrete pairwise energy minimization problem (weighted
constraint satisfaction, max-sum labeling) and methods that identify a globally
optimal partial assignment of variables. When finding a complete optimal
assignment is intractable, determining optimal values for a part of variables
is an interesting possibility. Existing methods are based on different
sufficient conditions. We propose a new sufficient condition for partial
optimality which is: (1) verifiable in polynomial time (2) invariant to
reparametrization of the problem and permutation of labels and (3) includes
many existing sufficient conditions as special cases. We pose the problem of
finding the maximum optimal partial assignment identifiable by the new
sufficient condition. A polynomial method is proposed which is guaranteed to
assign same or larger part of variables than several existing approaches. The
core of the method is a specially constructed linear program that identifies
persistent assignments in an arbitrary multi-label setting.Comment: Extended technical report for the CVPR 2014 paper. Update: correction
to the proof of characterization theore
Finding Connected Components in a Gray Scale Image
Abstract—Finding connected components are well defined for binary images. The concept of connected components can be extended for gray level image. But the problem is the criteria based on which a connected component would be defined. A gray level image is an image having 256 different pixel intensity levels. If we consider connected regions having only a particular pixel values, the number of connected components would not be meaningful and the purpose of finding connected components would be lost. So, we define a connected component in a gray scale image based on range of pixel mapping and new method to find connected components in a gray scale image is proposed. Three different types of pixel range mapping are introduced, using connected components in a gray level image can be successfully found. Connected components in a gray level image are the segments of image having the same range of pixel values. Different regions or segments of image can be found easily.Keywords—Connected component, gray scale labelling, pixel range mapping, linear mapping, logarithmic mapping, square root mapping.(Article history: Received 1 November 2016 and accepted 30 December 2016
Labeling Color 2D Digital Images in Theoretical Near Logarithmic Time
A design of a parallel algorithm for labeling color flat zones
(precisely, 4-connected components) of a gray-level or color 2D digital
image is given. The technique is based in the construction of a particular
Homological Spanning Forest (HSF) structure for encoding topological
information of any image.HSFis a pair of rooted trees connecting the image
elements at inter-pixel level without redundancy. In order to achieve a correct
color zone labeling, our proposal here is to correctly building a sub-
HSF structure for each image connected component, modifying an initial
HSF of the whole image. For validating the correctness of our algorithm,
an implementation in OCTAVE/MATLAB is written and its results are
checked. Several kinds of images are tested to compute the number of iterations
in which the theoretical computing time differs from the logarithm
of the width plus the height of an image. Finally, real images are to be computed
faster than random images using our approach.Ministerio de Economía y Competitividad TEC2016-77785-PMinisterio de Economía y Competitividad MTM2016-81030-
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