A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm

DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations

Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom-up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account, by using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant-this prevents them from modeling location-dependent patterns (e.g., centre-bias). Our network handles this by incorporating a novel location-biased convolutional layer. We evaluate our model on multiple challenging saliency data sets and show that it achieves the state-of-the-art results

Customizing CNNs for Blood Vessel Segmentation From Fundus Images

For automatic screening of eye diseases, it is very important to segment regions corresponding to the different eye-parts from the fundal images. A challenging task, in this context, is to segment the network of blood vessels. The blood vessel network runs all along the fundal image, varying in density and fineness of structure. Besides, changes in illumination, color and pathology also add to the difficulties in blood vessel segmentation. In this paper, we propose segmentation of blood vessels from fundal images in the deep learning framework, without any pre-processing. A deep convolutional network, consisting of 8 convolutional layers and 3 pooling layers in between, is used to achieve the segmentation. In this work, a Convolutional Neural Network currently in use for semantic image segmentation is customized for blood vessel segmentation by replacing the output layer with a convolutional layer of kernel size 1 x 1 which generates the final segmented image. The output of CNN is a gray scale image and is binarized by thresholding. The proposed method is applied on 2 publicly available databases DRIVE and HRF (capturing diversity in image resolution), consisting of healthy and diseased fundal images boosted by mirror versions of the originals. The method results in an accuracy of 93.94% and yields 0.894 as area under the ROC curve on the test data comprising of randomly selected 23 images from HRF dataset. The promising results illustrate generalizability of the proposed approach