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Detection of Inter-chromosomal Stable Aberrations by Multiple Fluorescence In Situ Hybridization mFISH and Spectral Karyotyping SKY in Irradiated Mice

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Sky Detection in Hazy Image.

Yingchao Song^1,2,3, Haibo Luo^4,5, Junkai Ma^6,7,8

¹Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. songyingchao@sia.cn.

Sensors (Basel, Switzerland)

|April 5, 2018

Summary

This summary is machine-generated.

This study introduces a new algorithm for detecting sky in hazy images by analyzing haze density. The method improves sky detection accuracy in various weather conditions, outperforming existing approaches.

Keywords:

HazySky haze-relevant features imbalance classifier perceptual hazy density sky detection sky labeling

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Area of Science:

Computer Vision
Image Processing

Background:

Sky detection is crucial for computer vision but current methods fail in adverse weather.
Existing sky detection algorithms struggle with hazy conditions, limiting their real-world applicability.

Purpose of the Study:

To develop a robust sky detection algorithm specifically for hazy images.
To improve the accuracy and reliability of sky detection under various weather and lighting conditions.

Main Methods:

Proposed a novel algorithm for sky detection in hazy images by probing haze density.
Employed image segmentation and region-level classification with new haze-relevant features.
Utilized two imbalance Support Vector Machine (SVM) classifiers and a similarity measurement for sky separation.
Created and utilized the HazySky dataset for training and evaluation.

Main Results:

The proposed method demonstrated superior detection accuracy on both the HazySky and SkyFinder datasets.
The algorithm effectively detects the sky even in hazy scenes and other adverse weather conditions.
Haze-relevant features significantly improved sky characterization in challenging environments.

Conclusions:

The novel algorithm offers a significant advancement in sky detection for computer vision applications.
The approach provides a reliable solution for sky detection under diverse and unfavorable environmental conditions.
The HazySky dataset serves as a valuable resource for future research in this domain.