181 research outputs found
Incremental Distance Transforms (IDT)
A new generic scheme for incremental implementations of distance transforms (DT) is presented: Incremental Distance Transforms (IDT). This scheme is applied on the cityblock, Chamfer, and three recent exact Euclidean DT (E2DT). A benchmark shows that for all five DT, the incremental implementation results in a significant speedup: 3.4×−10×. However, significant differences (i.e., up to 12.5×) among the DT remain present. The FEED transform, one of the recent E2DT, even showed to be faster than both city-block and Chamfer DT. So, through a very efficient incremental processing scheme for DT, a relief is found for E2DT’s computational burden
Comments on "On Approximating Euclidean Metrics by Weighted t-Cost Distances in Arbitrary Dimension"
Mukherjee (Pattern Recognition Letters, vol. 32, pp. 824-831, 2011) recently
introduced a class of distance functions called weighted t-cost distances that
generalize m-neighbor, octagonal, and t-cost distances. He proved that weighted
t-cost distances form a family of metrics and derived an approximation for the
Euclidean norm in . In this note we compare this approximation to
two previously proposed Euclidean norm approximations and demonstrate that the
empirical average errors given by Mukherjee are significantly optimistic in
. We also propose a simple normalization scheme that improves the
accuracy of his approximation substantially with respect to both average and
maximum relative errors.Comment: 7 pages, 1 figure, 3 tables. arXiv admin note: substantial text
overlap with arXiv:1008.487
Directed Exploration using a Modified Distance Transform
Mobile robots operating in unknown environments need to build maps. To do so they must have an exploration algorithm to plan a path. This algorithm should guarantee that the whole of the environment, or at least some designated area, will be mapped. The path should also be optimal in some sense and not simply a "random walk" which is clearly inefficient. When multiple robots are involved, the algorithm also needs to take advantage of the fact that the robots can share the task. In this paper we discuss a modification to the well-known distance transform that satisfies these requirements
Characterization of Posidonia Oceanica Seagrass Aerenchyma through Whole Slide Imaging: A Pilot Study
Characterizing the tissue morphology and anatomy of seagrasses is essential
to predicting their acoustic behavior. In this pilot study, we use histology
techniques and whole slide imaging (WSI) to describe the composition and
topology of the aerenchyma of an entire leaf blade in an automatic way
combining the advantages of X-ray microtomography and optical microscopy.
Paraffin blocks are prepared in such a way that microtome slices contain an
arbitrarily large number of cross sections distributed along the full length of
a blade. The sample organization in the paraffin block coupled with whole slide
image analysis allows high throughput data extraction and an exhaustive
characterization along the whole blade length. The core of the work are image
processing algorithms that can identify cells and air lacunae (or void) from
fiber strand, epidermis, mesophyll and vascular system. A set of specific
features is developed to adequately describe the convexity of cells and voids
where standard descriptors fail. The features scrutinize the local curvature of
the object borders to allow an accurate discrimination between void and cell
through machine learning. The algorithm allows to reconstruct the cells and
cell membrane features that are relevant to tissue density, compressibility and
rigidity. Size distribution of the different cell types and gas spaces, total
biomass and total void volume fraction are then extracted from the high
resolution slices to provide a complete characterization of the tissue along
the leave from its base to the apex
Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach
The main aim of this work is the development of a vision-based road detection
system fast enough to cope with the difficult real-time constraints imposed by
moving vehicle applications. The hardware platform, a special-purpose massively
parallel system, has been chosen to minimize system production and operational
costs. This paper presents a novel approach to expectation-driven low-level
image segmentation, which can be mapped naturally onto mesh-connected massively
parallel SIMD architectures capable of handling hierarchical data structures.
The input image is assumed to contain a distorted version of a given template;
a multiresolution stretching process is used to reshape the original template
in accordance with the acquired image content, minimizing a potential function.
The distorted template is the process output.Comment: See http://www.jair.org/ for any accompanying file
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