Detecting Diabetic Retinopathy Using Embedded Computer Vision

Diabetic retinopathy is one of the leading causes of vision loss in the United States and other countries around the world. People who have diabetic retinopathy may not have symptoms until the condition becomes severe, which may eventually lead to vision loss. Thus, the medically underserved populations are at an increased risk of diabetic retinopathy-related blindness. In this paper, we present development efforts on an embedded vision algorithm that can classify healthy versus diabetic retinopathic images. Convolution neural network and a k-fold cross-validation process were used. We used 88,000 labeled high-resolution retina images obtained from the publicly available Kaggle/EyePacs database. The trained algorithm was able to detect diabetic retinopathy with up to 76% accuracy. Although the accuracy needs to be further improved, the presented results represent a significant step forward in the direction of detecting diabetic retinopathy using embedded computer vision. This technology has the potential of being able to detect diabetic retinopathy without having to see an eye specialist in remote and medically underserved locations, which can have significant implications in reducing diabetes-related vision losses.

FPGA Based Optimized Reconfigurable Base-2 Constant Coefficient Multiplier Architecture for Image Filtering

Image convolution using FPGA has been comprehensively used for noise removal of Reconfigurable computing based image Processing Algorithm. Particularly these filters are widely used in embedded computer vision applications like edge detection and Feature extraction analysis. Practical implementation of filter requires enormous computational requirement. The multiplier plays very important role in the image convolution. The existed multiplier design requires more computational complexity for the 3x3 test image. For this the proposed reconfigurable constant coefficient multiplier uses base-2 Common sub expression algorithm. which reduces the computational complexity in a better way. The proposed 2D-convolution in image application is the value of resultant output is multiplication of image pixel with corresponding kernel value. In this work the realization of 2D convolution to be done using proposed constant coefficient multiplier and analyzed using Xilinx Virtex-5 FPGA platform

Smart Doll: Emotion Recognition Using Embedded Deep Learning

Computer vision and deep learning are clearly demonstrating a capability to create engaging cognitive applications and services. However, these applications have been mostly confined to powerful Graphic Processing Units (GPUs) or the cloud due to their demanding computational requirements. Cloud processing has obvious bandwidth, energy consumption and privacy issues. The Eyes of Things (EoT) is a powerful and versatile embedded computer vision platform which allows the user to develop artificial vision and deep learning applications that analyse images locally. In this article, we use the deep learning capabilities of an EoT device for a real-life facial informatics application: a doll capable of recognizing emotions, using deep learning techniques, and acting accordingly. The main impact and significance of the presented application is in showing that a toy can now do advanced processing locally, without the need of further computation in the cloud, thus reducing latency and removing most of the ethical issues involved. Finally, the performance of the convolutional neural network developed for that purpose is studied and a pilot was conducted on a panel of 12 children aged between four and ten years old to test the doll.