Accurate segmentation of Ulva prolifera regions with superpixel and CNNs

Wang, Shengke; Liu, Lu; Duan, Lianghua; Yu, Changyin; Cai, Guiyan; Gao, Feng; Dong, Junyu

doi:10.1109/spac.2017.8304318

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Single object will be segmented in more than one region depending on gray level of the object area. Therefore, superpixel image segmentation requires further process to complete image segmentation for example superpixel combined with Convolutional Neural Network [18] or with Conditional Random Field [19].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Image Segmentation Algorithm using Superpixel and Cosine Similarity

Saputra¹,

Putra²,

Via³

2018

Proceedings of the International Conference on Science and Technology (ICST 2018)

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In computer vision, image segmentation is a process of dividing image to get several segments of image. Image segmentation aim to divide image into simple section that meaningful and easy to analyze. Image segmentation regularly use to locate boundary of object in image, so object in image can analyzed. Object area boundary recognized from edge of object, and the edge of object in the image can be recognized from high discoloration. Superpixel is one of popular image segmentation methods. Superpixel has commonly partitioning image into simply part and reduce computation in various computer vision task. On the other side cosine similarity is a method used to analyze the similarity of two objects based on its features. This paper proposed an image segmentation using Superpixel and the sine similarity. Superpixel used to process segmentation and partitioning image into mere parts. Each segment labeled and process to compare with each neighbor using the cosine similarity. The experiment result shows that cosine similarity can be used to recognize similar segment from superpixel segmentation and make the boundary of object more significant.

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Section: Introductionmentioning

confidence: 99%