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About 10,000 papers for “scale selection”

Scale Selection

Tony Lindeberg · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2014

Cited by 21Open access

Scale Selection

Tony Lindeberg · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2020

Cited by 6Open access

Scale invariance without scale selection

Iasonas Kokkinos, Alan Yuille · 2008

In this work we construct scale invariant descriptors (SIDs) without requiring the estimation of image scale; we thereby avoid scale selection which is often unreliable. Our starting point is a combination of log-polar sampling and spatially-varying smoothing that converts image scalings and rotations into translations. Scale invariance can then be guaranteed by estimating the Fourier transform modulus (FTM) of the formed signal as the FTM is translation invariant. We build our descriptors using phase, orientation and amplitude features that compactly capture the local image structure. Our results show that the constructed SIDs outperform state-of-the-art descriptors on standard datasets. A main advantage of SIDs is that they are applicable to a broader range of image structures, such as edges, for which scale selection is unreliable. We demonstrate this by combining SIDs with contour segments and show that the performance of a boundary-based model is systematically improved on an object detection task.

Cited by 90

Adaptive-Scale Selection

Vladimir Katkovnik, Karen Egiazarian, Jaakko Astola · SPIE eBooks · 2009

Adaptability is a crucial feature of filtering and differentiating methods. It was demonstrated in the previous chapters that the performance of LPA kernel estimates depends strongly on the selection of the scale parameter. In Fig. 2.6 we saw the Nadaraya-Watson estimates for different h. A small h gives the limit estimate Å⋅ h (x) as a stepwise curve passing exactly through the observations. For large h the same estimator gives a constant value equal to the sample mean of the observations. For intermediate h between these small and large values, we may obtain a large variety of the estimate curves, which are different by their curvature and closeness to the observations. The idea of the Nadaraya-Watson estimator is natural and fruitful. However, the estimation curve is quite sensitive to h, and the selection of h is a key factor that is able to transform this reasonable idea into an effective working tool. A selection of a proper scale for estimation is a hot topic in both signal processing and nonparametric regression methods. The number of publications in this area is very large and growing quickly.

Cited by 1

Lexicase selection at scale

Li Ding, Ryan Boldi, Thomas Helmuth et al. · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2022

Lexicase selection is a semantic-aware parent selection method, which assesses individual test cases in a randomly-shuffled data stream. It has demonstrated success in multiple research areas including genetic programming, genetic algorithms, and more recently symbolic regression and deep learning. One potential drawback of lexicase selection and its variants is that the selection procedure requires evaluating training cases in a single data stream, making it difficult to handle tasks where the evaluation is computationally heavy or the dataset is large-scale, e.g., deep learning. In this work, we investigate how the weighted shuffle methods can be employed to improve the efficiency of lexicase selection. We propose a novel method, fast lexicase selection, which incorporates lexicase selection and weighted shuffle with partial evaluation. Experiments on both classic genetic programming and deep learning tasks indicate that the proposed method can significantly reduce the number of evaluation steps needed for lexicase selection to select an individual, improving its efficiency while maintaining the performance.

Cited by 15Open access

THE SELECTION SUITABILITY SCALE

Shawn Marie Flower, Ph. D · 2016

The rising costs of incarceration and a renewed interest in rehabilitation has prompted a resurgence of interest in community corrections. A major concern is determining which offenders are appropriate for community corrections without compromising public safety. The Montgomery County Pre-Release Center (PRC) is a work release facility that offers comprehensive services designed to assist offenders with transitioning back to the community after a period of incarceration. The PRC uses the “Selection Suitability Scale ” (SSS), a structured instrument created by PRC staff over 20 years ago, to ascertain which offenders are appropriate for admission to the institution. The SSS quantifies criteria believed to influence the applicant’s probability of success in the PRC, and classify their level of risk to the community. Criteria include measures of criminal history, employment history, residential stability, as well as mental health and substance abuse. Those with higher scores on the SSS are hypothesized to be more likely to succeed in the institution. This study assessed whether the instrument predicted an offender’s performance using three outcome measures, and whether the SSS, the total scale score and

Cited by 0

Temporal scale selection in time-causal scale space

Lindeberg, Tony · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2017

When designing and developing scale selection mechanisms for generating hypotheses about characteristic scales in signals, it is essential that the selected scale levels reflect the extent of the underlying structures in the signal. This paper presents a theory and in-depth theoretical analysis about the scale selection properties of methods for automatically selecting local temporal scales in time-dependent signals based on local extrema over temporal scales of scale-normalized temporal derivative responses. Specifically, this paper develops a novel theoretical framework for performing such temporal scale selection over a time-causal and time-recursive temporal domain as is necessary when processing continuous video or audio streams in real time or when modelling biological perception. For a recently developed time-causal and time-recursive scale-space concept defined by convolution with a scale-invariant limit kernel, we show that it is possible to transfer a large number of the desirable scale selection properties that hold for the Gaussian scale-space concept over a non-causal temporal domain to this temporal scale-space concept over a truly time-causal domain. Specifically, we show that for this temporal scale-space concept, it is possible to achieve true temporal scale invariance although the temporal scale levels have to be discrete, which is a novel theoretical construction.

Cited by 0Open access

Principles for Automatic Scale Selection

Tony Lindeberg · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 1999

1Abstract: An inherent property of objects in the world is that they only exist as meaningful entities over certain ranges of scale. If one aims at describing the structure of unknown real-world signals, then a multi-scale representation of data is of crucial importance. Whereas conventional scale-space theory provides a well-founded framework for dealing with im-age structures at dierent scales, this theory does not directly address the problem of how to select appropriate scales for further analysis. This chap-ter outlines a systematic methodology of how mechanisms for automatic scale selection can be formulated in the problem domains of feature detec-tion and image matching (\n ow estimation), respectively. For feature detectors expressed in terms of Gaussian derivatives, hy-potheses about interesting scale levels can be generated from scales at which normalized measures of feature strength assume local maxima with respect to scale. It is shown how the notion of -normalized derivatives arises by necessity given the requirement that the scale selection mechanism should

Cited by 95Open access

Scale measures for bandwidth selection

Paul Janssen, J. S. Marron, Noël Veraverbeke et al. · Journal of nonparametric statistics · 1995

Both high tech and also quick and dirty methods for selecting the bandwidth of a kernel density estimator are usually based on measures of scale. An example is given to illustrate how the usual scale measures can be arbitrarily bad. This motivates development of several improved scale measures. These are assessed through simulation, and experience with a real data set. Our best scale measure works at least as well as all other proposals in nearly all settings, and is much better quite often.

Cited by 36

Scale Selection for Wave Models

J. William Kamphuis · Research Repository (Delft University of Technology) · 1972

The selection of scales for models with short waves, long waves and unindirectional current is discussed with particular emphasis on boundary layer motion and movement of sediment. The paper attempts to outline the present state of the art and to provide a framework for future research in the area of coastal sediment transport models at Queen's University. Furthermore, the report forms the basic material for lectures on wave models, hence the didactic style.

Cited by 6Open access

Natural Selection and Scale Invariance

A. F. Tuck · Life · 2023

This review points out that three of the essential features of natural selection-competition for a finite resource, variation, and transmission of memory-occur in an extremely simple, thermalized molecular population, one of colliding "billiard balls" subject to an anisotropy, a directional flux of energetic molecules. The emergence of scaling behavior, scale invariance, in such systems is considered in the context of the emergence of complexity driven by Gibbs free energy, the origins of life, and known chemistries in planetary and astrophysical conditions. It is suggested that the thermodynamic formalism of statistical multifractality offers a parallel between the microscopic and macroscopic views of non-equilibrium systems and their evolution, different from, empirically determinable, and therefore complementing traditional definitions of entropy and its production in living systems. Further, the approach supports the existence of a bridge between microscopic and macroscopic scales, the missing mesoscopic scale. It is argued that natural selection consequently operates on all scales-whether or not life results will depend on both the initial and the evolving boundary conditions. That life alters the boundary conditions ensures nonlinearity and scale invariance. Evolution by natural selection will have taken place in Earth's fluid envelope; both air and water display scale invariance and are far from chemical equilibrium, a complex condition driven by the Gibbs free energy arising from the entropy difference between the incoming solar beam and the outgoing infrared radiation to the cold sink of space acting on the initial conditions within evolving boundary conditions. Symmetry breaking's role in the atmospheric state is discussed, particularly in regard to aerosol fission in the context of airborne bacteria and viruses in both current and prebiotic times. Over 4.4 billion years, the factors operating to support natural selection will have evolved along with the entire system from relative simplicity to the current complexity.

Cited by 2Open access

Large-Scale Loan Portfolio Selection

Justin Sirignano, Gerry Tsoukalas, Kay Giesecke · Operations Research · 2016

We consider the problem of optimally selecting a large portfolio of risky loans, such as mortgages, credit cards, auto loans, student loans, or business loans. Examples include loan portfolios held by financial institutions and fixed-income investors as well as pools of loans backing mortgage- and asset-backed securities. The size of these portfolios can range from the thousands to even hundreds of thousands. Optimal portfolio selection requires the solution of a high-dimensional nonlinear integer program and is extremely computationally challenging. For larger portfolios, this optimization problem is intractable. We propose an approximate optimization approach that yields an asymptotically optimal portfolio for a broad class of data-driven models of loan delinquency and prepayment. We prove that the asymptotically optimal portfolio converges to the optimal portfolio as the portfolio size grows large. Numerical case studies using actual loan data demonstrate its computational efficiency. The asymptotically optimal portfolio’s computational cost does not increase with the size of the portfolio. It is typically many orders of magnitude faster than nonlinear integer program solvers while also being highly accurate even for moderate-sized portfolios.

Cited by 33

Hydrocyclone selection and scale-up

L. Svarovsky · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1981

The paper lays down the basis for scale-up of geometrically similar hydrocyclones. This is followed by a review of the 'standard' or 'optimum' designs proposed in the literature and by examples of the scale-up procedure. In order to compare the relative merits of different designs a criterion is derived based on minimum running costs. The above-mentioned literature designs as well as some commercially available cyclones are then compared using published test data and appropriate conclusions are made.

Cited by 1

Large-Scale Loan Portfolio Selection

Srignano, Justin A, Tsoukalas, Gerry, Giesecke, Kay · Scholarly Commons (University of Pennsylvania) · 2016

We consider the problem of optimally selecting a large portfolio of risky loans, such as mortgages, credit cards, auto loans, student loans, or business loans. Examples include loan portfolios held by financial institutions and fixed-income investors as well as pools of loans backing mortgage- and asset-backed securities. The size of these portfolios can range from the thousands to even hundreds of thousands. Optimal portfolio selection requires the solution of a high-dimensional nonlinear integer program and is extremely computationally challenging. For larger portfolios, this optimization problem is intractable. We propose an approximate optimization approach that yields an asymptotically optimal portfolio for a broad class of data-driven models of loan delinquency and prepayment. We prove that the asymptotically optimal portfolio converges to the optimal portfolio as the portfolio size grows large. Numerical case studies using actual loan data demonstrate its computational efficiency. The asymptotically optimal portfolio’s computational cost does not increase with the size of the portfolio. It is typically many orders of magnitude faster than nonlinear integer program solvers while also being highly accurate even for moderate-sized portfolios.

Cited by 0