One or Two Research Paper Digests

In this paper , the authors propose a method to classify tuberculosis from chest X-ray images using Convolutional Neural Networks (CNN). They achieve a classification accuracy of 85.68%. They attribute the effectiveness of their approach to shuffle sampling with cross-validation while training the network. Methodology Convolutional Neural Network This has been the ultimate tool for researchers and engineers for computer vision tasks. It has been widely used for many general purpose image and video related tasks. There are many great resources to learn about them. I will link a few of them at the end of this post. In this paper, the authors study the famous AlexNet and GoogLeNet architectures in classifying tuberculosis images. A CNN model usually consists of convolutional layers, pooling layers and fully connected layers. Each layer is connected to the previous layers via kernels or filters. A CNN model learns parameters of the kernel to represent global and local features

Published in   2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, this paper introduces two main ideas Method noise Non-local (NL) means algorithm to denoise images Method noise It is defined as the difference between the original (noisy) image and its denoised version. Some of the intuitions that can be drawn by analysing method noise are Zero method noise means perfect denoising (complete removal of noise without lose of image data). If a denoising method performed well, the method noise must look like a noise and should contain as little structure as possible from the original image The authors then discuss the method noise properties for different denoising filters. They are derived based on the filter properties. We will not be going in detail for each filter as the properties of the filters are known facts. The paper explains those properties using the intuitions of method noise. NL-means idea Denoised value at point x