A Second-Order Translation, Rotation and Scale Invariant Neural Network
نویسندگان
چکیده
A second-order architecture is presented here for translation, rotation and scale invariant processing of 2-D images mapped to n input units. This new architecture has a complexity of O( n) weights as opposed to the O( n3 ) weights usually required for a third-order, rotation invariant architecture. The reduction in complexity is due to the use of discrete frequency information. Simulations show favorable comparisons to other neural network architectures.
منابع مشابه
Invariant Object Recognition Using Neural Network Ensemble on the CM
ABSI'RACI' This paper concerns machine recognition of objects from their images, where the recognition is invariant to scale, translation, and rotation. A neural network used for recognizing input objects is four layer backpropagation network and a cluster of interconnected units spanning four layers of each network forms a functional block called a column. The 90" rotation invariance has been ...
متن کاملObject recognition using a neural network and invariant Zernike features
In this paper, a neural network (NN) based approach for translation, scale, and rotation invariant recognition of objects is presented. The utilized network is a Multi-Layer Perceptron (MLP) classifier with one hidden layer. The back-propagation learning is used for its training. The image is represented by rotation invariant features which are the magnitudes of the Zernike moments of the image...
متن کاملA Novel Algorithm for Translation, Rotation and Scale Invariant Character Recognition
This paper presents a novel algorithm, called Radial Sector Coding (RSC), for Translation, Rotation and Scale invariant character recognition. Translation invariance is obtained using Center of Mass (CoM). Scaling invariance is achieved by normalizing the features of characters. To obtain most challenging rotation invariance, RSC searches a rotation invariant Line of Reference (LoR) by exploiti...
متن کاملPractical, Computation Efficient High-order Neural Network for Rotation and Shift Invariant Pattern Recognition
In this paper, a modification for the high-order neural network (HONN) is presented. Third order networks are considered for achieving translation, rotation and scale invariant pattern recognition. They require however much storage and computation power for the task. The proposed modified HONN takes into account a priori knowledge of the binary patterns that have to be learned, achieving signif...
متن کاملEncoding Geometric Invariances in Higher-Order Neural Networks
301 We describe a method of constructing higher-order neural networks that respond invariantly under geometric transformations on the input space. By requiring each unit to satisfy a set of constraints on the interconnection weights, a particular structure is imposed on the network. A network built using such an architecture maintains its invariant performance independent of the values the weig...
متن کامل