研究目的
To classify whether a color image is daytime or night using intensity and chromaticity features, with a secondary step involving k-means segmentation for ambiguous cases.
研究成果
The proposed classification method is effective with an accuracy of 96%, but ambiguous results necessitate further calibration. Future work will focus on improving accuracy and integrating with fog detection and shadow removal.
研究不足
The method may produce ambiguous results in some cases, as indicated in the experimental results. The range of categories (pixel values) may vary depending on camera parameters, requiring calibration. Execution time for the second step increases with image resolution.
1:Experimental Design and Method Selection:
The method involves two steps: first, using intensity and chromaticity images to classify images into daytime, evening/dawn, or night based on pixel value ranges (0-50 for night, 51-100 for evening/dawn, 101+ for daytime). If results from both images agree, classification ends; otherwise, k-means segmentation with k=3 is applied for final classification.
2:Sample Selection and Data Sources:
50 different natural scene images were used, including various resolutions as shown in Table
3:List of Experimental Equipment and Materials:
No specific equipment mentioned; images are assumed to be from CCTV or similar sources.
4:Experimental Procedures and Operational Workflow:
Convert RGB image to intensity and chromaticity images, compute histograms, compare category counts, and apply k-means if necessary.
5:Data Analysis Methods:
Histogram analysis and k-means clustering for segmentation; execution times and accuracy were measured.
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