• Premise of the study: Because plant identification demands extensive knowledge and complex terminologies, even professional botanists require significant time in the field for mastery of the subject. As plant leaves are normally regarded as possessing useful characteristics for species identification, leaf recognition through images can be considered an important research issue for plant recognition.• Methods: This study proposes a feature extraction method for leaf contours, which describes the lines between the centroid and each contour point on an image. A length histogram is created to represent the distribution of distances in the leaf contour. Thereafter, a classifier is applied from a statistical model to calculate the matching score of the template and query leaf.• Results: The experimental results show that the top value achieves 92.7% and the first two values can achieve 97.3%. In the scale invariance test, those 45 correlation coefficients fall between the minimal value of 0.98611 and the maximal value of 0.99992. Like the scale invariance test, the rotation invariance test performed 45 comparison sets. The correlation coefficients range between 0.98071 and 0.99988.• Discussion: This study shows that the extracted features from leaf images are invariant to scale and rotation because those features are close to positive correlation in terms of coefficient correlation. Moreover, the experimental results indicated that the proposed method outperforms two other methods, Zernike moments and curvature scale space.
The purpose of this study is to propose a shoeprint retrieval method based on core point alignment for pattern analysis. The proposed method firstly detects contour points in a black-and-white shoeprint image. Those reliable contour points are selected to simulate the left and right sidelines of the shoeprint by curve fitting method. Subsequently, the most concave points along the left and right sidelines can determine the core point of the shoeprint, thereby partitioning the shoeprint into circular regions. Next, the Zernike moments of the circular regions are calculated for pattern description of each region. Finally, the Euclidean distance is measured to match the shoeprints with the same pattern. The highest = 0.726 is obtained from the first four Zernike moments with the radius = 90 pixels and three baselines. The experimental results also show that Zernike method in any orders always outperforms the compared moment invariants and GLCM method. This study has verified that the proposed method can effectively align the shoeprints for pattern comparison.
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