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01:25Shape and Texture of Coarse Aggregate
Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
01:09Random Sampling Method
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
02:16Light Acquisition
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
01:15Sampling Theorem
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
01:15Parseval's Theorem for Fourier transform
Parseval's theorem is a fundamental principle in signal processing that enables the calculation of a signal's energy in either the time domain or the frequency domain. This theorem is pivotal in demonstrating energy conservation between these two domains, ensuring that the computed energy value remains consistent regardless of the domain of analysis.
To understand Parseval's theorem, it is essential to first comprehend how signal energy is typically calculated. When considering a signal's...
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01:20Maxwell-Boltzmann Distribution: Problem Solving
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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Image-based Lagrangian Particle Tracking in Bed-load Experiments
Published on: July 20, 2017
Lightweight probabilistic texture retrieval.
1Department of Computer Sciences, University of Salzburg, 5020 Salzburg, Austria. rkwitt@cosy.sbg.ac.at
Summary
This study introduces a novel probabilistic image retrieval method using wavelet transforms and statistical models. It achieves competitive retrieval rates with low computational cost, addressing practical application bottlenecks.
Area of Science:
- Computer Vision
- Image Processing
- Computational Mathematics
Background:
- Probabilistic image retrieval is crucial for content-based image retrieval (CBIR).
- Wavelet domain analysis offers efficient image representation.
- Modeling wavelet coefficients' statistical distributions is key for accurate similarity measurement.
Purpose of the Study:
- To propose a computationally efficient probabilistic image retrieval framework in the wavelet domain.
- To address performance bottlenecks in practical image retrieval applications.
- To achieve high retrieval rates using statistical models of wavelet coefficients.
Main Methods:
- Utilizing the dual-tree complex wavelet transform (DTCWT) for image decomposition.
- Employing statistical models for wavelet transform coefficient magnitudes.
- Calculating image similarity via closed-form Kullback-Leibler divergences between statistical models.
Main Results:
- Demonstrated competitive retrieval performance on a standard texture image database.
- Achieved high retrieval rates with significantly reduced computational cost.
- Identified and analyzed computational bottlenecks for practical implementation.
Conclusions:
- The proposed method offers an efficient and effective approach to probabilistic image retrieval.
- Statistical modeling in the wavelet domain provides a robust framework for image similarity.
- The approach balances retrieval accuracy with computational feasibility for real-world applications.