## 2 Intelligent Processing on Image and Optical Information
### Classification and Detection  
In the first paper of this category, entitled 'Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach', [PERSON], [PERSON], [PERSON], and [PERSON] [3] presented landscapemonitoring via satellite remote sensing. The authors analyzed the texture features of the image to address the characteristics of changes in certain types of surface, such as rivers and lakes.  
Texture classification is an important topic for many applications in image analysis. A paper, entitled 'A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer' by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [4], proposed a new texture classification based on the integrated optimization of the parameters and features of the Gabor filter.  
Calcaneal fractures are often caused by accidents during exercise or activity. In general, the detection of calcaneal fractures is still performed manually through computer tomography (CT) image observation. [PERSON] and [PERSON] [5], in their paper, 'Real-Time Automated Segmentation and Classification of Calcaneal Fractures in CT Images' proposed a method for detecting calcaneal fractures through localization and color segmentation of calcaneus.  
Two papers on biological research, entitled 'Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network' [6] and 'Zebrafish Larvae Phenotype Classification from Bright-field Microscopic Images Using a Two-Tier Deep-Learning Pipeline' [7] are presented for zebrafish egg recognition. In the former paper, [PERSON], [PERSON], [PERSON], and [PERSON] addressed an automated zebrafish egg microscopic image analysis based on deep convolution neural networks (CNN). This study applies deep learning technique to classify fertilized and unfertilized zebrafish eggs from bright-field microscopic images. Transfer learning and data augmentation schemes were used to overcome the problem of small unbalanced training datasets. In the latter paper, [PERSON] and his coworkers proposed a deep-learning pipeline for zebrafish larvae phenotype classification from brightfield microscopic images. Facing the difficulties of scarce training data, they used a two-tier classification pipeline.  
As the deep-learning model develops, a huge number of images is required for training. [PERSON] and [PERSON] [8] described in their article: 'Unsupervised Generation and Synthesis of Facial Images via an Auto-Encoder-Based Deep Generative Adversarial Network (GAN)' an auto-encoder-based GAN with an enhanced network structure and training scheme for database augmentation and image synthesis.  
As fruit production worldwide increases, the use of spectroscopy to monitor the surface condition of food continues to increase. A paper entitled 'Detecting Green Mold Pathogens on Lemons Using Hyperspectral Images' by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [9] investigates spectral images to detect green mold pathogens that parasitize on the lemon surface.  
In their review paper entitled 'Computer Aided Weld Defect Detection from Radiography Images', [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [10] reviewed the automatic defect inspection in three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of classification based on feature extraction, selection and classifier are summarized and the application of new models based on deep learning were introduced.