研究目的
To propose methods for image preprocessing, specifically image enhancement and boundary detection, to improve the quality of iris images for more reliable iris recognition systems.
研究成果
The preprocessing of iris images, including hardware and software aspects, is crucial for improving recognition rates. Methods like filtering, edge detection, and boundary detection help in obtaining high-quality iris images, which are essential for accurate subsequent steps like normalization, feature extraction, and matching. Future work should focus on resolving remaining issues in image quality assessment and preprocessing.
研究不足
The paper mentions that iris recognition systems are affected by poor quality imaging, and there are many problems to be resolved in image preprocessing, such as handling interferential pixels and optimizing image quality based on the environment. The hardware design factors like image sensor selection (frequency, pixels, power consumption) and system usability are also constraints.
1：Experimental Design and Method Selection:
The paper discusses both hardware and software designs for iris image preprocessing, including image acquisition, filtering, edge detection, boundary detection, and interference detection. Methods such as Canny edge detection, Hough transform, Sobel edge detection, and histogram equalization are employed.
2：Sample Selection and Data Sources:
Iris images are used, but specific selection criteria or data sources are not detailed in the provided text.
3：List of Experimental Equipment and Materials:
Hardware components include a lens, CCD or CMOS sensor, processor, and peripherals. No specific models or brands are mentioned.
4：Experimental Procedures and Operational Workflow:
Steps include image acquisition, filtering (e.g., mean filter, median filter, Gaussian smoothing), edge detection (e.g., Canny, Sobel), inner and outer boundary detection using Hough transform, scope reduction, and interference detection.
5：Data Analysis Methods:
Analysis involves pattern recognition techniques, but specific statistical methods or software tools are not detailed.
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