论文标题
使用CW-SSIM在纹理图像中用于纹理图像中异常检测的深度自动编码器
Deep Autoencoders for Anomaly Detection in Textured Images using CW-SSIM
论文作者
论文摘要
在图像中检测异常区域是工业监测中经常遇到的问题。一个相关的例子是对正常条件下符合特定纹理的组织和其他产品的分析，而缺陷会引入正常模式的变化。我们通过训练深层自动编码器来解决异常检测问题，并且我们表明，基于复杂的小波结构相似性（CW-SSIM）采用损失函数（CW-SSIM）与传统的自动编码器损失函数相比，这种类型的图像上的检测性能出色。我们对众所周知的异常检测基准的实验表明，使用此损失函数训练的简单模型可以实现可比性或优越的性能，以利用更深层次，更大且更加计算的神经网络的最先进方法。
Detecting anomalous regions in images is a frequently encountered problem in industrial monitoring. A relevant example is the analysis of tissues and other products that in normal conditions conform to a specific texture, while defects introduce changes in the normal pattern. We address the anomaly detection problem by training a deep autoencoder, and we show that adopting a loss function based on Complex Wavelet Structural Similarity (CW-SSIM) yields superior detection performance on this type of images compared to traditional autoencoder loss functions. Our experiments on well-known anomaly detection benchmarks show that a simple model trained with this loss function can achieve comparable or superior performance to state-of-the-art methods leveraging deeper, larger and more computationally demanding neural networks.
