Results 331 to 340 of about 15,521,488 (377)
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Image and Vision Computing, 1997
It is argued that image measurements should satisfy two requirements of physical plausibility: the measurements are of non-zero scale and non-zero imprecision; and two required invariances, nothing is lost by expanding the image and nothing is lost by increasing the contrast of the image.
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It is argued that image measurements should satisfy two requirements of physical plausibility: the measurements are of non-zero scale and non-zero imprecision; and two required invariances, nothing is lost by expanding the image and nothing is lost by increasing the contrast of the image.
openaire +1 more source
Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces
Computer Vision and Pattern RecognitionHumans possess the visual-spatial intelligence to remember spaces from sequential visual observations. However, can Multimodal Large Language Models (MLLMs) trained on million-scale video datasets also "think in space" from videos?
Jihan Yang +5 more
semanticscholar +1 more source
2019
We introduce a novel scale-space concept that is inspired by inpainting-based lossy image compression and the recent denoising by inpainting method of Adam et al. (2017). In the discrete setting, the main idea behind these so-called sparsification scale-spaces is as follows: Starting with the original image, one subsequently removes a pixel until a ...
Cárdenas, Marcelo +2 more
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We introduce a novel scale-space concept that is inspired by inpainting-based lossy image compression and the recent denoising by inpainting method of Adam et al. (2017). In the discrete setting, the main idea behind these so-called sparsification scale-spaces is as follows: Starting with the original image, one subsequently removes a pixel until a ...
Cárdenas, Marcelo +2 more
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2005
In this paper we extend the notion of Poisson scale-space. We propose a generalisation inspired by the linear parabolic pseudodifferential operator $\sqrt{-\Delta+m^2}-m$, 0≤m, connected with models of relativistic kinetic energy from quantum mechanics.
Bernhard Burgeth +2 more
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In this paper we extend the notion of Poisson scale-space. We propose a generalisation inspired by the linear parabolic pseudodifferential operator $\sqrt{-\Delta+m^2}-m$, 0≤m, connected with models of relativistic kinetic energy from quantum mechanics.
Bernhard Burgeth +2 more
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Scale-Space Theory in Computer Vision
Lecture Notes in Computer Science, 1993Tony Lindeberg
semanticscholar +1 more source
Multidimensional scaling in riemannian space
Journal of Mathematical Psychology, 1975Abstract A metric which is a function of position is proposed for the analysis of the intrinsic geometry involved in preference or similarity judgments. Variation in the distance function or metric is characteristic of the Riemannian spaces and may be interpreted as curvature, stress or distortion in distance estimates and thus in the subjective ...
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Scaling of Program Fitness Spaces
Evolutionary Computation, 1999We investigate the distribution of fitness of programs concentrating on those represented as parse trees and, particularly, how such distributions scale with respect to changes in the size of the programs. By using a combination of enumeration and Monte Carlo sampling on a large number of problems from three very different areas, we suggest that, in ...
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Representations Based on Zero-Crossing in Scale-Space-M
Computer Vision and Pattern Recognition, 2018A. Hummel
semanticscholar +1 more source
Responsive materials architected in space and time
Nature Reviews Materials, 2022Xiaoxing Xia +2 more
exaly
2001
We investigate the deep structure of a scale space image. We concentrate on scale space critical points – points with vanishing gradient with respect to both spatial and scale direction. We show that these points are always saddle points. They turn out to be extremely useful, since the iso-intensity manifolds through these points provide a scale space ...
Kuijper, A. +2 more
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We investigate the deep structure of a scale space image. We concentrate on scale space critical points – points with vanishing gradient with respect to both spatial and scale direction. We show that these points are always saddle points. They turn out to be extremely useful, since the iso-intensity manifolds through these points provide a scale space ...
Kuijper, A. +2 more
openaire +2 more sources