Scale-space approach to image thinning using the most prominent ridge line in the image pyramid data structure

1998 ◽  
Author(s):  
Mark E. Hoffman ◽  
Edward K. Wong
Keyword(s):  
Data Structure ◽  
Scale Space ◽  
Image Pyramid ◽  
Ridge Line ◽  
Image Thinning ◽  
Prominent Ridge ◽  
Space Approach

A Scale-space Approach for Detecting Non-stationarities in Time Series

2008 ◽  
Vol 35 (1) ◽  
pp. 119-138 ◽  
Author(s):  
LENA R. OLSEN ◽  
SIGRUNN H. SØRBYE ◽  
FRED GODTLIEBSEN
Keyword(s):  
Time Series ◽  
Scale Space ◽  
Space Approach

scholarly journals A parameterless scale-space approach to find meaningful modes in histograms — Application to image and spectrum segmentation

2014 ◽  
Vol 12 (06) ◽  
pp. 1450044 ◽  
Author(s):  
Jérôme Gilles ◽  
Kathryn Heal
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Scale Space ◽  
Space Representation ◽  
Grayscale Image ◽  
Local Minima ◽  
Clustering Problem ◽  
Space Curves ◽  
Space Approach

In this paper, we present an algorithm to automatically detect meaningful modes in a histogram. The proposed method is based on the behavior of local minima in a scale-space representation. We show that the detection of such meaningful modes is equivalent in a two classes clustering problem on the length of minima scale-space curves. The algorithm is easy to implement, fast and does not require any parameter. We present several results on histogram and spectrum segmentation, grayscale image segmentation and color image reduction.

A Scale-space Approach for Detecting Non-stationarities in Time Series

2007 ◽  
Vol 0 (0) ◽  
pp. 070605025545002-??? ◽  
Author(s):  
LENA R. OLSEN ◽  
SIGRUNN H. SØRBYE ◽  
FRED GODTLIEBSEN
Keyword(s):  
Time Series ◽  
Scale Space ◽  
Space Approach

scholarly journals Transition Scale-Spaces: A Computational Theory for the Discretized Entorhinal Cortex

2020 ◽  
Vol 32 (2) ◽  
pp. 330-394
Author(s):  
Nicolai Waniek
Keyword(s):  
Data Structure ◽  
Theoretical Model ◽  
Entorhinal Cortex ◽  
Receptive Fields ◽  
Grid Cell ◽  
Scale Space ◽  
General Purpose ◽  
Single Scale ◽  
Scale Spaces ◽  
Space Data

Although hippocampal grid cells are thought to be crucial for spatial navigation, their computational purpose remains disputed. Recently, they were proposed to represent spatial transitions and convey this knowledge downstream to place cells. However, a single scale of transitions is insufficient to plan long goal-directed sequences in behaviorally acceptable time. Here, a scale-space data structure is suggested to optimally accelerate retrievals from transition systems, called transition scale-space (TSS). Remaining exclusively on an algorithmic level, the scale increment is proved to be ideally [Formula: see text] for biologically plausible receptive fields. It is then argued that temporal buffering is necessary to learn the scale-space online. Next, two modes for retrieval of sequences from the TSS are presented: top down and bottom up. The two modes are evaluated in symbolic simulations (i.e., without biologically plausible spiking neurons). Additionally, a TSS is used for short-cut discovery in a simulated Morris water maze. Finally, the results are discussed in depth with respect to biological plausibility, and several testable predictions are derived. Moreover, relations to other grid cell models, multiresolution path planning, and scale-space theory are highlighted. Summarized, reward-free transition encoding is shown here, in a theoretical model, to be compatible with the observed discretization along the dorso-ventral axis of the medial entorhinal cortex. Because the theoretical model generalizes beyond navigation, the TSS is suggested to be a general-purpose cortical data structure for fast retrieval of sequences and relational knowledge. Source code for all simulations presented in this paper can be found at https://github.com/rochus/transitionscalespace .

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