14,879 research outputs found
Coding of details in very low bit-rate video systems
In this paper, the importance of including small image features at the initial levels of a progressive second generation video coding scheme is presented. It is shown that a number of meaningful small features called details should be coded, even at very low data bit-rates, in order to match their perceptual significance to the human visual system. We propose a method for extracting, perceptually selecting and coding of visual details in a video sequence using morphological techniques. Its application in the framework of a multiresolution segmentation-based coding algorithm yields better results than pure segmentation techniques at higher compression ratios, if the selection step fits some main subjective requirements. Details are extracted and coded separately from the region structure and included in the reconstructed images in a later stage. The bet of considering the local background of a given detail for its perceptual selection breaks the concept ofPeer ReviewedPostprint (published version
Morphological filter for lossless image subsampling
We present a morphological filter for lossless image subsampling for a given downsampling-upsampling strategy. This filter is applied in a multiresolution decomposition and results in a more efficient scheme for image coding purposes than other lossy sampling schemes. Its main advantage is a greatly reduced computational load compared to multiresolution schemes performed with linear filters.Peer ReviewedPostprint (published version
Cognitive Deficit of Deep Learning in Numerosity
Subitizing, or the sense of small natural numbers, is an innate cognitive
function of humans and primates; it responds to visual stimuli prior to the
development of any symbolic skills, language or arithmetic. Given successes of
deep learning (DL) in tasks of visual intelligence and given the primitivity of
number sense, a tantalizing question is whether DL can comprehend numbers and
perform subitizing. But somewhat disappointingly, extensive experiments of the
type of cognitive psychology demonstrate that the examples-driven black box DL
cannot see through superficial variations in visual representations and distill
the abstract notion of natural number, a task that children perform with high
accuracy and confidence. The failure is apparently due to the learning method
not the CNN computational machinery itself. A recurrent neural network capable
of subitizing does exist, which we construct by encoding a mechanism of
mathematical morphology into the CNN convolutional kernels. Also, we
investigate, using subitizing as a test bed, the ways to aid the black box DL
by cognitive priors derived from human insight. Our findings are mixed and
interesting, pointing to both cognitive deficit of pure DL, and some measured
successes of boosting DL by predetermined cognitive implements. This case study
of DL in cognitive computing is meaningful for visual numerosity represents a
minimum level of human intelligence.Comment: Accepted for presentation at the AAAI-1
Quantale Modules and their Operators, with Applications
The central topic of this work is the categories of modules over unital
quantales. The main categorical properties are established and a special class
of operators, called Q-module transforms, is defined. Such operators - that
turn out to be precisely the homomorphisms between free objects in those
categories - find concrete applications in two different branches of image
processing, namely fuzzy image compression and mathematical morphology
Quantitative characterization of pore structure of several biochars with 3D imaging
Pore space characteristics of biochars may vary depending on the used raw
material and processing technology. Pore structure has significant effects on
the water retention properties of biochar amended soils. In this work, several
biochars were characterized with three-dimensional imaging and image analysis.
X-ray computed microtomography was used to image biochars at resolution of 1.14
m and the obtained images were analysed for porosity, pore-size
distribution, specific surface area and structural anisotropy. In addition,
random walk simulations were used to relate structural anisotropy to diffusive
transport. Image analysis showed that considerable part of the biochar volume
consist of pores in size range relevant to hydrological processes and storage
of plant available water. Porosity and pore-size distribution were found to
depend on the biochar type and the structural anisotopy analysis showed that
used raw material considerably affects the pore characteristics at micrometre
scale. Therefore attention should be paid to raw material selection and quality
in applications requiring optimized pore structure.Comment: 16 pages, 4 figures. The final publication is available at Springer
via http://dx.doi.org/10.1007/s11356-017-8823-
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
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