# An Image Edge Detection Algorithm Based on an Artificial Plant Community
## 2 Relevant Work  
The problem with edge detection is determining how to accurately recognize significant changes in image attributes. Such changes usually reflect important events and changes in attributes, such as discontinuity of depth, discontinuity of the surface direction, changes in material attributes, and changes in scene lighting [9; 12]. There are many edge detection method, and these can be divided into two main categories, namely search-based algorithms and zero-crossing-based algorithms. The search-based algorithms are mainstream and first calculate the edge strength [26], which is usually expressed by the first derivative, such as the gradient modulus. Zero-crossing-based algorithms determine the edge according to the zero-crossing point of the second derivative of the image, such as the Laplace operator. Threshold-based edge detection is one of the most popular techniques and is the basis of many edge detection algorithms. It can be applied in many cases that emphasize computational efficiency and hardware implementation. In [26], a multilevel threshold in a hybrid transient search of a naked mole-rat optimizer was used for image segmentation. Region-based edge detection includes two typical serial region technologies, namely region growth and decomposition. Region growth describes the continuous growth of regions based on the existing pixels until the whole region is formed so that target extraction can be carried out. Region decomposition is the inverse process of region growth, where the whole image is continuously divided into many subregions, and then the foreground regions are merged to achieve target extraction. In [14], an adaptive fuzzy-region growing fusion method was developed, and CNN-ANFIS-based automated segmentation was improved for the classification of cervical cancer. This method can be used to judge the regional computing results from the previous steps. The feature-based clustering algorithm is a category-based edge detection method. It calculates the image space with corresponding feature space pixels, and makes segments of the feature space according to their feature aggregation. Then, it maps the feature space back onto the original image space to obtain the edge image. In [29], an image feature-based independent adversarial example detection model was developed, and in [30], an adaptive-window-based 3D feature selection method was developed for multispectral image classification, using a firefly algorithm. Fuzzy edge detection employs fuzzy mathematics to describe a large number of uncertain concepts in order to achieve proper processing and image edge detection. Fuzzy C-means clustering (FCM) [9] and fuzzy region growing [14] are the two commonly used fuzzy detection algorithms. In [31], automobile instrument detection using prior information and fuzzy sets was illustrated, and in [32], adaptive image steganography using fuzzy enhancement and the grey wolf optimizer was developed. In wavelet transform edge detection, the image histogram is divided into different levels of wavelet coefficients by a dyadic wavelet transform, and a threshold is then chosen according to the wavelet coefficients and given edge detection criteria. Lastly, a threshold is used to mark the region of the edge image. The calculation performance of the wavelet-transform-based edge detection algorithm varies linearly with the image size. In [33], image denoising was introduced to edge detection based on the wavelet transform. The histogram-based algorithm uses a histogram to measure the color and strength of the image pixels. The histogram indicates the image edges, and the peaks and troughs of the histogram can be used to select the clusters in the image. In [34], a histogram-matched chest X-ray-based tuberculosis detection method using CNN was introduced. Compared with other image edge detection algorithms, the histogram-based algorithm is very effective, since it only needs a passing pixel.