PPT-Convolutional Neural Networks for Image Processing with App

By Sruthi Moola Convolution Convolution is a common image processing technique that changes the intensities of a pixel to reflect the intensities of the surrounding pixels A common use of convolution is to create image filters. RECOGNITION. does size matter?. Karen . Simonyan. Andrew . Zisserman. Contents. Why I Care. Introduction. Convolutional Configuration . Classification. Experiments. Conclusion. Big Picture. Why I . care. using Convolutional Neural Network and Simple Logistic Classifier. Hurieh. . Khalajzadeh. Mohammad . Mansouri. Mohammad . Teshnehlab. Table of Contents. Convolutional Neural . Networks. Proposed CNN structure for face recognition. 1. Recurrent Networks. Some problems require previous history/context in order to be able to give proper output (speech recognition, stock forecasting, target tracking, etc.. One way to do that is to just provide all the necessary context in one "snap-shot" and use standard learning. Neural . Network Architectures:. f. rom . LeNet. to ResNet. Lana Lazebnik. Figure source: A. . Karpathy. What happened to my field?. . Classification:. . ImageNet. Challenge top-5 error. Figure source: . Week 5. Applications. Predict the taste of Coors beer as a function of its chemical composition. What are Artificial Neural Networks? . Artificial Intelligence (AI) Technique. Artificial . Neural Networks. Abhishek Narwekar, Anusri Pampari. CS 598: Deep Learning and Recognition, Fall 2016. Lecture Outline. Introduction. Learning Long Term Dependencies. Regularization. Visualization for RNNs. Section 1: Introduction. 2015/10/02. 陳柏任. Outline. Neural Networks. Convolutional Neural Networks. Some famous CNN structure. Applications. Toolkit. Conclusion. Reference. 2. Outline. Neural Networks. Convolutional Neural Networks. Last time. Linear classifiers on pixels bad, need non-linear classifiers. Multi-layer . perceptrons. . overparametrized. Reduce parameters by local connections and shift invariance => Convolution. Ali Cole. Charly. . Mccown. Madison . Kutchey. Xavier . henes. Definition. A directed network based on the structure of connections within an organism's brain. Many inputs and only a couple outputs. person 1. person 2. horse 1. horse 2. R-CNN: Regions with CNN features. Input. image. Extract region. proposals (~2k / image). Compute CNN. features. Classify regions. (linear SVM). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Article and Work by. : Justin . Salamon. and Juan Pablo Bello. Presented by . : . Dhara. Rana. Overall Goal of Paper. Create a way to classify environmental sound given an audio clip. Other methods of sound classification: (1) dictionary learning and (2) wavelet filter banks . person. grass. trees. motorbike. road. Evaluation metric. Pixel classification!. Accuracy?. Heavily unbalanced. Common classes are over-emphasized. Intersection over Union. Average across classes and images. José Ignacio Orlando. 1,2. , Elena Prokofyeva. 3,4. , Mariana del Fresno. 1,5. and Matthew B. Blaschko. 6. 1 . Instituto. . Pladema. , UNCPBA, . Tandil. , Argentina. 2. . Consejo. Nacional de . Investigaciones. An overview and applications. Outline. Overview of Convolutional Neural Networks. The Convolution operation. A typical CNN model architecture. Properties of CNN models. Applications of CNN models. Notable CNN models.