Roadway “corners†are common for pedestrian use, whether designated with markings or not. Different types of markings have been deployed, ranging from simple parallel lines to more complex designs. Understanding the impact of different types of crosswalks is important for public safety. In this work we explore methods to improve the logging of marked crosswalk types. We used the Roadway Information Database from the Second Strategic Highway Research Project and used active learning methods with transfer learning to identify the crosswalk types (marked or unmarked). Upon completion we found our classifiers were unable to perform above roughly 94% correct classifications. To improve their efficacy, we separated the crosswalks into their “fine grained†types and used Gradient-Weighted Class Activation Mapping to isolate and study the features that classified the crosswalks. We compared this with sampled manually marked crosswalks and present findings. We believe this use case can represent a process to improve the active learning method for some visual machine learning applications.
We present the results of our image analysis of portrait art from the Roman Empire's Julio-Claudian dynastic period. Our novel approach involves processing pictures of ancient statues, cameos, altar friezes, bas-reliefs, frescoes, and coins using modern mobile apps, such as Reface and FaceApp, to improve identification of the historical subjects depicted. In particular, we have discovered that the Reface app has limited, but useful capability to restore the approximate appearance of damaged noses of the statues. We confirm many traditional identifications, propose a few identification corrections for items located in museums and private collections around the world, and discuss the advantages and limitations of our approach. For example, Reface may make aquiline noses appear wider or shorter than they should be. This deficiency can be partially corrected if multiple views are available. We demonstrate that our approach can be extended to analyze portraiture from other cultures and historical periods. The article is intended for a broad section of the readers interested in how the modern AI-based solutions for mobile imaging merge with humanities to help improve our understanding of the modern civilization's ancient past and increase appreciation of our diverse cultural heritage.