Artificial crawler model for texture analysis on silk fibroin scaffolds

Texture plays an important role in computer vision tasks. Several methods of texture analysis are available. However, these methods are not capable of extracting rich detail in images. This paper presents a novel approach to image texture classification based on the artificial crawler model. Here, we propose a new rule of movement that moves artificial crawler agents not only toward higher intensities but also toward lower ones. This strategy is able of capturing more detail because the agents explore the peaks as well as the valleys. Thus, compared with the state-of-the-art method, this approach shows an increased discriminatory power. Experiments on the most well known benchmark demonstrate the superior performance of our approach. We also tested our approach on silk fibroin scaffold analysis, and results indicate that our method is consistent and can be applied in real-world situations.