In this research we propose a fast and robust ellipse detection algorithm based on a multipass Hough transform and an image pyramid data structure. The algorithm starts with an exhaustive search on a low-resolution image in the image pyramid using elliptical Hough transform. Then the image resolution is iteratively increased while the candidate ellipses with higher resolution are updated at each step until the original image resolution is reached. After removing the detected ellipses, the Hough transform is repeatedly applied in multiple passes to search for remaining ellipses, and terminates when no more ellipses are found. This approach significantly reduces the false positive error of ellipse detection as compared with the conventional randomized Hough transform method. The analysis shows that the computing complexity of this algorithm is Θ(n(5/2)), and thus the computation time and memory requirement are significantly reduced. The developed algorithm was tested with images containing various numbers of ellipses. The effects of noise-to-signal ratio combined with various ellipse sizes on the detection accuracy were analyzed and discussed. Experimental results revealed that the algorithm is robust to noise. The average detection accuracies were all above 90% for images with less than seven ellipses, and slightly decreased to about 80% for images with more ellipses. The average false positive error was less than 2%.

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Recent developments of microcomputer-based machine vision systems has offered convenient and non-destructive ways for measurements of plant characteristics that allow plant growth assessment. This paper presents brief reviews and comparisons of machine vision approaches for non-destructive plant growth measurement. The simple approach of using projected silhouette image of a plant was most commonly used in acquiring growth data in various experiments. Plant fresh or dry weight can be indirectly estimated from projected leaf area by calibration with data from standard measurement methods. However, the estimation error usually increases as the extent of overlapped leaves increases. Images acquired from different views or utilizing dual cameras allow the estimation of leaf area without predetermined calibration relationship but the image processing algorithms usually become more sophisticated. By incorporating multiple images of a plant, three-dimensional and structural information may be extracted for more detailed growth analyses and modeling.

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