A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

Image cropping aims at improving the aesthetic quality of images by adjusting their composition. Most weakly supervised cropping methods (without bounding box supervision) rely on the sliding window mechanism. The sliding window mechanism requires fixed aspect ratios and limits the cropping region with arbitrary size. Moreover, the sliding window method usually produces tens of thousands of windows on the input image which is very time-consuming. Motivated by these challenges, we firstly formulate the aesthetic image cropping as a sequential decision-making process and propose a weakly supervised Aesthetics Aware Reinforcement Learning (A2-RL) framework to address this problem. Particularly, the proposed method develops an aesthetics aware reward function which especially benefits image cropping. Similar to human's decision making, we use a comprehensive state representation including both the current observation and the historical experience. We train the agent using the actor-critic architecture in an end-to-end manner. The agent is evaluated on several popular unseen cropping datasets. Experiment results show that our method achieves the state-of-the-art performance with much fewer candidate windows and much less time compared with previous weakly supervised methods.Comment: Accepted by CVPR 201

Computer Analysis of Architecture Using Automatic Image Understanding

In the past few years, computer vision and pattern recognition systems have been becoming increasingly more powerful, expanding the range of automatic tasks enabled by machine vision. Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion. Images of buildings from 18 cities and three countries were acquired using Google StreetView, and were used to train a machine vision system to automatically identify the location of the imaged building based on the image visual content. Experimental results show that the automatic computer analysis can automatically identify the geographical location of the StreetView image. More importantly, the algorithm was able to group the cities and countries and provide a phylogeny of the similarities between architectural styles as captured by StreetView images. These results demonstrate that computer vision and pattern recognition algorithms can perform the complex cognitive task of analyzing images of buildings, and can be used to measure and quantify visual similarities and differences between different styles of architectures. This experiment provides a new paradigm for studying architecture, based on a quantitative approach that can enhance the traditional manual observation and analysis. The source code used for the analysis is open and publicly available

FEM and Von Mises analysis on prosthetic crowns structural elements: evaluation of different applied materials

The aim of this paper is to underline the mechanical properties of dental single crown prosthodontics materials in order to differentiate the possibility of using each material for typical clinical condition and masticatory load. Objective of the investigation is to highlight the stress distribution over different common dental crowns by using computer-aided design software and a three-dimensional virtual model. By using engineering systems of analyses like FEM and Von Mises investigations it has been highlighted the strength over simulated lower first premolar crowns made by chrome cobalt alloy, golden alloy, dental resin, and zirconia. The prosthodontics crown models have been created and put on simulated chewing stresses. The three-dimensional models were subjected to axial and oblique forces and both guaranteed expected results over simulated masticatory cycle. Dental resin presented the low value of fracture while high values have been recorded for the metal alloy and zirconia. Clinicians should choose the better prosthetic solution for the teeth they want to restore and replace. Both prosthetic dental crowns offer long-term success if applied following the manufacture guide limitations and suggestions

The patterning of finance/security : a designerly walkthrough of challenger banking apps

Culture is being ‘appified’. Diverse, pre-existing everyday activities are being redesigned so they happen with and through apps. While apps are often encountered as equivalent icons in apps stores or digital devices, the processes of appification – that is, the actions required to turn something into an app – vary significantly. In this article, we offer a comparative analysis of a number of ‘challenger’ banking apps in the United Kingdom. As a retail service, banking is highly regulated and banks must take steps to identify and verify their customers before entering a retail relationship. Once established, this ‘secured’ financial identity underpins a lot of everyday economic activity. Adopting the method of the walkthrough analysis, we study the specific ways these processes of identifying and verifying the identity of the customer (now the user) occur through user onboarding. We argue that banking apps provide a unique way of binding the user to an identity, one that combines the affordances of smart phones with the techniques, knowledge and patterns of user experience design. With the appification of banking, we see new processes of security folded into the everyday experience of apps. Our analysis shows how these binding identities are achieved through what we refer to as the patterning of finance/security. This patterning is significant, moreover, given its availability for wider circulation beyond the context of retail banking apps