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Related Concept Videos

Relative Frequency Histogram01:14

Relative Frequency Histogram

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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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High-Precision Dichotomous Image Segmentation With Frequency and Scale Awareness.

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    A new deep learning model, FSANet, improves dichotomous image segmentation (DIS) by integrating spatial and frequency information. This approach enhances feature representation and maintains high resolution for precise segmentation of detailed objects.

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    Area of Science:

    • Computer Vision
    • Deep Learning
    • Image Processing

    Background:

    • Dichotomous image segmentation (DIS) is challenging due to fine-grained details and cluttered backgrounds.
    • Existing deep learning methods struggle with generic object segmentation when boundaries are unclear.
    • Feature map resolution decrease and misleading texture cues are key limitations in current DIS models.

    Purpose of the Study:

    • To develop a novel deep neural network (FSANet) for high-precision dichotomous image segmentation.
    • To address limitations in feature representation and resolution maintenance in existing DIS methods.
    • To improve the segmentation of generic objects with cluttered boundaries.

    Main Methods:

    • Proposed FSANet integrates spatial and frequency domains using a multimodality fusion (MF) module.
    • A collaborative scale fusion module (CSFM) maintains high feature resolution throughout encoding.
    • Hierarchical context fusion (HCF) and selective feature fusion (SFF) modules are used in the decoder for refined segmentation.

    Main Results:

    • FSANet demonstrates superior performance compared to state-of-the-art methods on benchmark datasets.
    • Qualitative and quantitative experimental results confirm the effectiveness of the proposed approach.
    • The method successfully segments generic objects with complex and cluttered boundaries.

    Conclusions:

    • FSANet offers a significant advancement in high-precision dichotomous image segmentation.
    • The proposed fusion modules effectively enhance feature representation and preserve resolution.
    • The approach provides a robust solution for challenging DIS tasks with fine-grained details.