Rice stands as one of the most crucial staple crops worldwide. The traits of rice grains are important indicators for evaluating grain quality. Traditional grain trait measurement methods rely on manual measurement by experts, which not only suffer from low efficiency but also are susceptible to individual subjectivity, resulting in compromised accuracy and stability. To address these issues, this paper proposes an automatic method for measuring grain trait. Firstly, the grain foreground is extracted using superpixel segmentation. Then, a concavity point detection algorithm based on a sliding circular model is employed to detect concavity points on the contours of grain connected regions. Multiple-round ellipse fitting is applied to segment individual grains. Finally, the grain trait parameters are obtained using the minimum bounding rectangle. Experimental trials were conducted on a grain image dataset consisting of three rice varieties: Huanghuazhan, Japonica, and Xiushui 134. Comparative analysis between the proposed algorithm and the SmartGrain software was performed to evaluate their performance. The experimental results demonstrate the robustness and high accuracy of the proposed method.
Dermoscopic image segmentation aims to detect damaged skin areas. It is difficult to detect skin lesions accurately due to factors such as the method of image acquisition, the characteristics of the lesion, and the texture of the skin. This paper proposes a dermatoscopic image segmentation algorithm that combines the pigment separation algorithm with the Segnet network model. First, pigment separation is performed on the dermatoscopic images to obtain the corresponding melanin and hemosiderin images. Afterwards, the melanin and hemosiderin images are converted to single-channel grayscale maps and merged with the original images to produce image data with a channel number of 5. The channel-expanded images are then segmented using Segnet deep neural network. Experimental results on the ISIC-2018 dermoscopy image dataset show that the proposed algorithm achieves better segmentation results in terms of accuracy, sensitivity, and specificity.
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