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首页> 外文会议>International conference on graphic and image processing >Super-pixel Extraction Based on Multi-channel Pulse Coupled Neural Network
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Super-pixel Extraction Based on Multi-channel Pulse Coupled Neural Network

机译：基于多通道脉冲耦合神经网络的超像素提取

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摘要

Super-pixel extraction techniques group pixels to form over-segmented image blocks according to the similarity among pixels. Compared with the traditional pixel-based methods, the image descripting method based on super-pixel has advantages of less calculation, being easy to perceive, and has been widely used in image processing and computer vision applications. Pulse coupled neural network (PCNN) is a biologically inspired model, which stems from the phenomenon of synchronous pulse release in the visual cortex of cats. Each PCNN neuron can correspond to a pixel of an input image, and the dynamic firing pattern of each neuron contains both the pixel feature information and its context spatial structural information. In this paper, a new color super-pixel extraction algorithm based on multi-channel pulse coupled neural network (MPCNN) was proposed. The algorithm adopted the block dividing idea of SLIC algorithm, and the image was divided into blocks with same size first. Then, for each image block, the adjacent pixels of each seed with similar color were classified as a group, named a super-pixel. At last, post-processing was adopted for those pixels or pixel blocks which had not been grouped. Experiments show that the proposed method can adjust the number of super-pixel and segmentation precision by setting parameters, and has good potential for super-pixel extraction.
机译：超像素提取技术根据像素之间的相似性对像素进行分组，以形成过度分割的图像块。与传统的基于像素的方法相比，基于超像素的图像描述方法具有计算量少，易于感知的优点，已被广泛应用于图像处理和计算机视觉应用中。脉冲耦合神经网络（PCNN）是一种生物学启发的模型，其源于猫的视觉皮层中同步脉冲释放的现象。每个PCNN神经元可以对应于输入图像的像素，并且每个神经元的动态激发模式既包含像素特征信息，又包含其上下文空间结构信息。提出了一种基于多通道脉冲耦合神经网络（MPCNN）的彩色超像素提取算法。该算法采用了SLIC算法的块划分思想，首先将图像划分为相同大小的块。然后，对于每个图像块，将具有相似颜色的每个种子的相邻像素分类为一个组，称为超像素。最后，对未分组的像素或像素块采用后处理。实验表明，该方法可以通过设置参数来调整超像素的数量和分割精度，具有良好的超像素提取潜力。

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