2,153 research outputs found
Pushing the Limits of 3D Color Printing: Error Diffusion with Translucent Materials
Accurate color reproduction is important in many applications of 3D printing,
from design prototypes to 3D color copies or portraits. Although full color is
available via other technologies, multi-jet printers have greater potential for
graphical 3D printing, in terms of reproducing complex appearance properties.
However, to date these printers cannot produce full color, and doing so poses
substantial technical challenges, from the shear amount of data to the
translucency of the available color materials. In this paper, we propose an
error diffusion halftoning approach to achieve full color with multi-jet
printers, which operates on multiple isosurfaces or layers within the object.
We propose a novel traversal algorithm for voxel surfaces, which allows the
transfer of existing error diffusion algorithms from 2D printing. The resulting
prints faithfully reproduce colors, color gradients and fine-scale details.Comment: 15 pages, 14 figures; includes supplemental figure
The Watershed Transformation Applied to Image Segmentation
Image segmentation by mathematical morphology is a methodology based upon the notions of watershed and homotopy modification.This paper aims at introducing this methodology through various examples of segmentation in materials sciences, electron microscopy and scene analysis.
First, we defined our basic tool, the watershed transform. We showed that this transformation can be built by implementing a flooding process on a greytone image. This flooding process can be performed by using elementary morphological operations such as geodesic skeleton and reconstruction. Other algorithms are also briefly presented (arrows representation).
Then, the use of this transformation for image segmentation purposes is discussed. The application of the watershed transform to gradient images and the problems raised by over-segmentation are emphasized. This leads, into the third part, to the introduction of a general methodology for segmentation, based on the definition of markers and on a transformation called homotopy modification. This complex tool is defined in detail and various types of implementations are given.
Many examples of segmentation are presented. These examples are taken from various fields: transmission electron microscopy, scanning electron microscopy (SEM), 3D holographic pictures, radiography, non destructive control and so on.
The final part of this paper is devoted to the use of the watershed transformation for hierarchical segmentation. This tool is particularly efficient for defining different levels of segmentation starting from a graph representation of the images based on the mosaic image transform. This approach will be explained by means of examples in industrial vision and scene analysis
On the Topological Disparity Characterization of Square-Pixel Binary Image Data by a Labeled Bipartite Graph
Given an nD digital image I based on cubical n-xel, to fully
characterize the degree of internal topological dissimilarity existing in I
when using different adjacency relations (mainly, comparing 2n or 2n −1
adjacency relations) is a relevant issue in current problems of digital
image processing relative to shape detection or identification. In this
paper, we design and implement a new self-dual representation for a
binary 2D image I, called {4, 8}-region adjacency forest of I ({4, 8}-RAF,
for short), that allows a thorough analysis of the differences between the
topology of the 4-regions and that of the 8-regions of I. This model can
be straightforwardly obtained from the classical region adjacency tree
of I and its binary complement image Ic, by a suitable region label
identification. With these two labeled rooted trees, it is possible: (a) to
compute Euler number of the set of foreground (resp. background) pixels
with regard to 4-adjacency or 8-adjacency; (b) to identify new local and
global measures and descriptors of topological dissimilarity not only for
one image but also between two or more images. The parallelization of
the algorithms to extract and manipulate these structures is complete,
thus producing efficient and unsophisticated codes with a theoretical
computing time near the logarithm of the width plus the height of an
image. Some toy examples serve to explain the representation and some
experiments with gray real images shows the influence of the topological
dissimilarity when detecting feature regions, like those returned by the
MSER (maximally stable extremal regions) method.Ministerio de Economía, Industria y Competitividad PID2019-110455GB-I00 (Par-HoT)Junta de Andalucía US-138107
Parallel Image Processing Using a Pure Topological Framework
Image processing is a fundamental operation
in many real time applications, where lots of parallelism
can be extracted. Segmenting the image into different
connected components is the most known operations, but
there are many others like extracting the region adjacency
graph (RAG) of these regions, or searching for features
points, being invariant to rotations, scales, brilliant
changes, etc. Most of these algorithms part from the basis
of Tracing-type approaches or scan/raster methods. This
fact necessarily implies a data dependence between the
processing of one pixel and the previous one, which
prevents using a pure parallel approach. In terms of time
complexity, this means that linear order O(N) (N being the
number of pixels) cannot be cut down. In this paper, we
describe a novel approach based on the building of a pure
Topological framework, which allows to implement fully
parallel algorithms. Concerning topological analysis, a first
stage is computed in parallel for every pixel, thus
conveying the local neighboring conditions. Then, they are
extended in a second parallel stage to the necessary global
relations (e.g. to join all the pixels of a connected
component). This combinatorial optimization process can
be seen as the compression of the whole image to just one
pixel. Using this final representation, every region can be
related with the rest, which yields to pure topological
construction of other image operations. Besides, complex
data structures can be avoided: all the processing can be
done using matrixes (with the same indexation as the
original image) and element-wise operations. The time
complexity order of our topological approach for a m×n
pixel image is near O(log(m+n)), under the assumption that
a processing element exists for each pixel. Results for a
multicore processor show very good scalability until the
memory bandwidth bottleneck is reached, both for bigger
images and for much optimized implementations. The
inherent parallelism of our approach points to the
direction that even better results will be obtained in other
less classical computing architectures.1Ministerio de Economía y Competitividad (España) TEC2012-37868-C04-02AEI/FEDER (UE) MTM2016-81030-PVPPI of the University of Sevill
Twilight revelation
The first inspiration came from the thrilling phenomenal nuance caused by the natural light, especially the diffused and cool light — that’s why I am so fascinated with the dawn. Also, the “conditional” idea proposed by Robert Irwin in his book “Being and Circumstance — Notes Toward a Conditional Art” influenced my attitude towards landscape and public art: the design could be a tool to “reveal” the phenomenal changes and make people become more aware of them, instead of changing the existing condition arbitrarily.
Then I chose “threshold” — the space between private and public condition, interior and exterior, which might be front or back yard, the street next to residential entrance, or semi-private park as my targeted sites, because they have great potential of physical, phenomenal, mental and programmatic changes.
I started my investigation from the residential zone in Providence and developed the prototypical strategy to play with dawn light, and then adapt it to New York city to achieve a wider practice. The outcomes are both a theoretical strategy and a design.
The overall objectives are to bring a new thought and attitude towards landscape design and public art, which makes people re-aware of the phenomena in the circumstance, reveal the fascination of dawn light, and create a prototypical design for residential area. During the whole process, the written article needs to be re-thought and revised along with the design
A synoptic description of coal basins via image processing
An existing image processing system is adapted to describe the geologic attributes of a regional coal basin. This scheme handles a map as if it were a matrix, in contrast to more conventional approaches which represent map information in terms of linked polygons. The utility of the image processing approach is demonstrated by a multiattribute analysis of the Herrin No. 6 coal seam in Illinois. Findings include the location of a resource and estimation of tonnage corresponding to constraints on seam thickness, overburden, and Btu value, which are illustrative of the need for new mining technology
Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease
In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders
Digital Morphometry : A Taxonomy Of Morphological Filters And Feature Parameters With Application To Alzheimer\u27s Disease Research
In this thesis the expression digital morphometry collectively describes all those procedures used to obtain quantitative measurements of objects within a two-dimensional digital image. Quantitative measurement is a two-step process: the application of geometrical transformations to extract the features of interest, and then the actual measurement of these features. With regard to the first step the morphological filters of mathematical morphology provide a wealth of suitable geometric transfomations. Traditional radiometric and spatial enhancement techniques provide an additional source of transformations. The second step is more classical (e.g. Underwood, 1970; Bookstein, 1978; and Weibull, 1980); yet here again mathematical morphology is applicable - morphologically derived feature parameters. This thesis focuses on mathematical morphology for digital morphometry. In particular it proffers a taxonomy of morphological filters and investigates the morphologically derived feature parameters (Minkowski functionals) for digital images sampled on a square grid. As originally conceived by Georges Matheron, mathematical morphology concerns the analysis of binary images by means of probing with structuring elements [typically convex geometric shapes] (Dougherty, 1993, preface). Since its inception the theory has been extended to grey-level images and most recently to complete lattices. It is within the very general framework of the complete lattice that the taxonomy of morphological filters is presented. Examples are provided to help illustrate the behaviour of each type of filter. This thesis also introduces DIMPAL (Mehnert, 1994) - a PC-based image processing and analysis language suitable for researching and developing algorithms for a wide range of image processing applications. Though DIMPAL was used to produce the majority of the images in this thesis it was principally written to provide an environment in which to investigate the application of mathematical morphology to Alzheimer\u27s disease research. Alzheimer\u27s disease is a form of progressive dementia associated with the degeneration of the brain. It is the commonest type of dementia and probably accounts for half the dementia of old age (Forsythe, 1990, p. 21 ). Post mortem examination of the brain reveals the presence of characteristic neuropathologic lesions; namely neuritic plaques and neurofibrillary tangles. They occur predominantly in the cerebral cortex and hippocampus. Quantitative studies of the distribution of plaques and tangles in normally aged and Alzheimer brains are hampered by the enormous amount of time and effort required to count and measure these lesions. Here in a morphological algorithm is proposed for the automatic segmentation and measurement of neuritic plaques from light micrographs of post mortem brain tissue
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