Abstract
The digital image segmentation algorithm based on deep learning plays an important role in the monitoring of seabed mineral resources. The traditional segmentation algorithm has insufficient performance in the face of adhesion, and the segmentation boundary is fuzzy. For this reason, an improved segmentation algorithm by learning a deep convolution network is proposed. A typical encoder-decoder structure is used to construct the network model, and the decoder part is up-sampled at different scales to obtain the final segmentation map. The performance of the algorithm is tested on the gray scale electron microscopy (EM) image dataset and the seabed mineral image dataset. The experimental shows that the Rand theoretic score can achieve 0.916 on EM image dataset, and a better segmentation result on the seabed mineral image dataset than the original U-net Convolutional Network.
Highlights
With the development of unmanned driving, automatic navigation and augmented reality systems, the need for accurate and efficient image segmentation mechanisms is becoming more and more intense, but as the complexity of images continues to increase, the requirements for segmentation mechanisms are increasing.Many schemes have emerged in the last few years to segment images effectively
Our goal is to transform the gray scale electron microscopy (EM) images into an accurate boundary map, in which white indicates a pixel inside a cell, and black indicates a pixel at a boundary between neurite cross sections
In this paper, we propose an improved U-Net convolutional network, which performs up-sampled operations on convolutional feature maps of different scales and fuses to obtain the final segmentation image
Summary
With the development of unmanned driving, automatic navigation and augmented reality systems, the need for accurate and efficient image segmentation mechanisms is becoming more and more intense, but as the complexity of images continues to increase, the requirements for segmentation mechanisms are increasing.Many schemes have emerged in the last few years to segment images effectively. In 2000, Shi and Malik [1] tried to improve the traditional minimum cutting criterion, proposed the N-cut algorithm. Boykov et al [2] proposed the Graph Cuts algorithm. In addition to traditional graph cutting theory, some scholars perform image segmentation based on clustering theory. Chuang et al [3] presented a fuzzy c-means algorithm. On this basis, many scholars combine the FCM algorithm with the traditional watershed segmentation algorithm or local information to improve the accuracy [4], [5]. Dhanachandra et al [6] proposed an image segmentation algorithm which combines the K-means clustering algorithm and subtractive clustering algorithm.
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