Learning invariant features by harnessing the aperture problem
نویسندگان
چکیده
The energy model is a simple, biologically inspired approach to extracting relationships between images in tasks like stereopsis and motion analysis. We discuss how adding an extra pooling layer to the energy model makes it possible to learn encodings of transformations that are mostly invariant with respect to image content, and to learn encodings of images that are mostly invariant with respect to the observed transformations. We show how this makes it possible to learn 3D pose-invariant features of objects by watching videos of the objects. We test our approach on a dataset of videos derived from the NORB dataset.
منابع مشابه
Neural Network Performance Analysis for Real Time Hand Gesture Tracking Based on Hu Moment and Hybrid Features
This paper presents a comparison study between the multilayer perceptron (MLP) and radial basis function (RBF) neural networks with supervised learning and back propagation algorithm to track hand gestures. Both networks have two output classes which are hand and face. Skin is detected by a regional based algorithm in the image, and then networks are applied on video sequences frame by frame in...
متن کاملTASK-BASED CURRICULUM FROM THE NURSING EDUCATION EXPERTS' VIEWPOINT: A PHENOMENOLOGICAL STUDY
Background & Aims: One of the new educational approaches adopted by many medical schools around the world as a suitable method for teaching and learning is the task-based curriculum developed by Harden et al. (1996) in the medical education curricula. The purpose of the present study was to identify the characteristics and methods of teaching and learning task-based curriculum in nursing educat...
متن کاملAdvanced Gaussian MRF Rotation-Invariant Texture Features for Classification of Remote Sensing Imagery
The features based on Markov random field (MRF) models are usually sensitive to the rotation of image textures. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for modelling rotated image textures and retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate (LSE) method, an approximate least squares estimate (ALSE)...
متن کاملHierarchical Invariant Feature Learning with Marginalization for Person Re-Identification
This paper addresses the problem of matching pedestrians across multiple camera views, known as person re-identification. Variations in lighting conditions, environment and pose changes across camera views make re-identification a challenging problem. Previous methods address these challenges by designing specific features or by learning a distance function. We propose a hierarchical feature le...
متن کاملMax-Margin Invariant Features from Transformed Unlabelled Data
The study of representations invariant to common transformations of the data is important to learning. Most techniques have focused on local approximate invariance implemented within expensive optimization frameworks lacking explicit theoretical guarantees. In this paper, we study kernels that are invariant to a unitary group while having theoretical guarantees in addressing the important pract...
متن کامل