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相关概念视频

Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Differential Leveling01:12

Differential Leveling

Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...

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ADStereo:高效的立体声匹配与自适应下面采样和差异调整.

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    此摘要是机器生成的。

    这项研究为立体匹配引入了新的深度学习方法,提高了速度和准确性. 适应性低采样模块和差异调整模块提高了计算机视觉应用程序的实时性能.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 立体声匹配 立体声匹配

    背景情况:

    • 平衡精度和计算效率对于深度学习立体匹配算法至关重要.
    • 当前的方法在下方采样过程中经常失去特征,并遭受空间错位.
    • 成本体积聚合是立体匹配的计算密集部分.

    研究的目的:

    • 开发新的采样策略,以实现高准确度的实时立体声匹配.
    • 在现有的深度学习方法中解决特征损失和空间错位问题.
    • 引入一个新的网络,ADStereo,它优先考虑推断速度而不影响准确性.

    主要方法:

    • 引入了使用本地特征和自适应权重进行有效下采样的自适应下采样模块 (ADM).
    • 开发了差异对齐模块 (DAM) 具有可学习的插值来纠正空间不对齐.
    • 将ADM和DAM集成到ADStereo网络架构中.

    主要成果:

    • ADStereo实现了实时性能,在速度方面超过了最先进的方法.
    • 该网络在公共基准上展示了与现有的领先立体声匹配解决方案相比的准确性.
    • 在KITTI 2015上,ADStereo实现了1.82%的精度,比CREStereo更快.

    结论:

    • 拟议的ADM和DAM模块有效地提高了立体声匹配的准确性和效率.
    • 在现实世界中的立体视觉任务中,ADStereo提供了速度和准确性之间的卓越平衡.
    • 开发的方法为基于深度学习的高效准确立体相匹配提供了有希望的方向.