Application-Oriented Retinal Image Models for Computer Vision

• Main Authors: Ewerton Silva, Ricardo da S. Torres, Allan Pinto, Lin Tzy Li, José Eduardo S. Vianna, Rodolfo Azevedo, Siome Goldenstein
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Sensors
• Subjects: retinal image model; space-variant computer vision; foveation; low-power; energy consumption
• Online Access: https://www.mdpi.com/1424-8220/20/13/3746

Energy and storage restrictions are relevant variables that software applications should be concerned about when running in low-power environments. In particular, computer vision (CV) applications exemplify well that concern, since conventional uniform image sensors typically capture large amounts of data to be further handled by the appropriate CV algorithms. Moreover, much of the acquired data are often redundant and outside of the application’s interest, which leads to unnecessary processing and energy spending. In the literature, techniques for sensing and re-sampling images in non-uniform fashions have emerged to cope with these problems. In this study, we propose Application-Oriented Retinal Image Models that define a space-variant configuration of uniform images and contemplate requirements of energy consumption and storage footprints for CV applications. We hypothesize that our models might decrease energy consumption in CV tasks. Moreover, we show how to create the models and validate their use in a face detection/recognition application, evidencing the compromise between storage, energy, and accuracy.