PPT-Group-Pair Convolutional Neural Networks for Multi-View based 3D Object Retrieval

Zan Gao Deyu Wang Xiangnan He Hua Zhang Tianjin University of Technology National University of Singapore Previous work Proposed method Experiments Conclusion Outline Previous work. ABSTRACT From the desire to update the maximum road speed data for navigation devices a speed sign recognition and detection system is proposed This system should prevent accidental speeding at roads where the map data is incorrect for example due t 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. Kuan-Chuan. Peng. Tsuhan. Chen. 1. Introduction. Breakthrough progress in object classification.. 2. O. . Russakovsky. . et al. . ImageNet. . large scale visual recognition challenge. .. . arXiv:1409.0575, 2014.. CAP5615 Intro. to Neural Networks. Xingquan (Hill) Zhu. Outline. Multi-layer Neural Networks. Feedforward Neural Networks. FF NN model. Backpropogation (BP) Algorithm. BP rules derivation. Practical Issues of FFNN. Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . 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. Multi-View Drawing. Shows two or more two-dimensional views of a three-dimensional object.. Provides the shape description of an object. . When combined with dimensions, serves as the main form of communication between designers and manufacturers.. Shows two or more two-dimensional views of a three-dimensional object.. Provides the shape description of an object. . When combined with dimensions, serves as the main form of communication between designers and manufacturers.. 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. Zhaoliang. . Lun. Matheus Gadelha. Evangelos. . Kalogerakis. Subhransu . Maji. Rui. Wang. Image from Autodesk 3D Maya. Creating 3D shapes is not easy. Goal: 2D line drawings in, 3D shapes out!. ShapeMVD. Generally a DAG, directed acyclic graph. VisGraph, HKUST. LeNet. AlexNet. ZF Net. GoogLeNet. VGGNet. ResNet. Learned convolutional filters: Stage 1. Visualizing and understanding convolutional neural networks.. Kannan . Neten. Dharan. Introduction . Alzheimer’s Disease is a kind of dementia which is caused by damage to nerve cells in the brain and the usual side effects of it are loss of memory or other cognitive impairments.. 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. By. Neha Ujjainkar & Abhishek Khandekar. Outline. Problem Statement. Introduction. Literature Review. Data set. Significance. Experiment Design. Timeline and Milestones. References. 2. Problem Statement.