Cover of: Fast learning and invariant object recognition | Branko SoucМЊek

Fast learning and invariant object recognition

the sixth-generation breakthrough
  • 279 Pages
  • 0.12 MB
  • 5222 Downloads
  • English
by
Wiley , New York
Image processing., Machine learning., Electronic digital compu
StatementBranko Souc̆ek and The IRIS Group.
SeriesSixth-generation computer technology series
ContributionsIRIS Group.
Classifications
LC ClassificationsTA1632 .S65 1992
The Physical Object
Paginationxiii, 279 p. :
ID Numbers
Open LibraryOL1565325M
ISBN 100471574309
LC Control Number91047113

Fast Learning and Invariant Object Recognition: The Sixth- Generation Breakthrough (Sixth Generation Computer Technologies) [Branko Soucek, The IRIS Group] on *FREE* shipping on qualifying offers.

Describes multiple possible generalizers, comparing different infinitesimal and discrete techniques in learning and in neural network by: 2.

Fast learning and invariant object recognition: the sixth-generation breakthrough. [Branko Souček; IRIS Group.] -- This applications-oriented book presents, for the first time, Learning-Generalization-Seeing-Recognition Hybrids.

Description Fast learning and invariant object recognition FB2

Tomaso A. Poggio is Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at MIT, where he is also Director of the Center for Brains, Minds, and Machines and Codirector of the Center for Biological and Computational Learning.

He is coeditor of Perceptual Learning (MIT Press).Cited by: Fast Learning and Invariant Object Recognition: The Sixth Generation Breakthrough (Sixth Generation Computer Technologies) Paperback – 24 Jun by Branko Soucek (Author), The IRIS Group (Author)Author: Branko Soucek, The IRIS Group.

Fast visual recognition in the mammalian cortex seems to be a hierarchical process by which the representation of the visual world is transformed in multiple stages from low-level retinotopic features to high-level, global and invariant features, and to object categories.

Every single step in this hierarchy seems to be subject to by: learning the parameters of the scale-invariant object model are estimated.

This is done using expectation-maximization in a maximum-likelihood setting. In recognition, this model is used in a Bayesian manner to classify images.

The flex-ible nature of the model is demonstrated by excellent re-sults over a range of datasets including geometrically con. Learning Location Invariance for Object Recognition and Localization.

A visual system not only Fast learning and invariant object recognition book to recognize a stimulus, it also needs to find the location of the stimulus. In this paper, we present a neural network model that is able to generalize its ability to identify objects to new locations in its visual field.

In learning the parameters of the scale-invariant object model are estimated. This is done using expectation-maximization in a maximum-likelihood setting. In recognition, this model. For object recognition regardless of its orientation, size and position feature vectors are computed with the assistance of nonlinear moment invariant functions.

After an efficient feature extraction, the main focus of this study, reco gnition performance of artificial classifiers in conjunction with moment-based feature sets, is : Muharrem Mercimek, Kayhan Gulez.

Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition. Abstract: We present an unsupervised method for learning a hierarchy of sparse feature detectors that are invariant to small shifts and distortions.

The resulting feature extractor consists of multiple convolution filters, Cited by: This paper describes an efficient two-stage neural network for invariant object recognition. It consists of a feature extractor trained by an ART-like Cited by: This chapter is concerned with invariant representations of faces and objects in the inferior temporal visual cortex.

It also covers computational approaches to invariant object recognition, feature spaces, structural descriptions and syntactic pattern recognition, template matching and the alignment approach, invertible networks that can reconstruct their inputs, and feature.

One key ability of human brain is invariant object recognition, which refers to rapid and accurate recognition of objects in the presence of variations such as size, rotation and position. Despite Cited by: 9. a robust, invariant, and linearly-separable object representation [10, 9].

Despite the extensive feed-back connections in the visual cortex, the rst feed-forward wave of spikes in IT (˘ ms post-stimulus presentation) appears to be su cient for crude object recognition [54, 19, 33]. During the last decades, various computationalCited by: In the context of object recognition, a particularly in-teresting and challenging question is whether unsupervised learning can be used to learn invariant features.

The abil-ity to learn robust invariant representations from a limited amount of labeled data is a crucial step towards building a solution to the object recognition problem.

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In this. VisNet2 is a model to investigate some aspects of invariant visual object recognition in the primate visual system. It is a four-layer feedforward network with convergence to each part of a layer from a small region of the preceding layer, with competition between the neurons within a layer and with a trace learning rule to help it learn transform by: Biederman’s theory is a reasonably plausible account of object recognition.

There is much evidence that the identification of concavities and edges is important for object recognition. Biederman’s theory has several limitations:  It focuses on bottom-up processes and de-emphasises top-down Size: KB.

learning the parameters of the scale-invariant object model are estimated. This is done using expectation-maximization in a maximum-likelihood setting. In recognition, this model is used in a Bayesian manner to classify images.

The flex-ible nature of the model is demonstrated by excellent re-sults over a range of datasets including. A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications.

The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological.

Abstract: We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion and relative scale.

Combining Harris Interest Points and the SIFT Descriptor for Fast Scale-Invariant Object Recognition Pedram Azad, Tamim Asfour, Rudiger Dillmann¨ Institute for Anthropomatics, University of Karlsruhe, Germany [email protected], [email protected], [email protected] Abstract—In the recent past, the recognition and localization.

To test and clarify the hypotheses just described about how the visual system may operate to learn invariant object recognition, Wallis and Rolls developed a simulation which implements many of the ideas just described, and is consistent with and based on much of the neurophysiology summarized by: The visual recognition problem is central to computer vision research.

From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image by:   Combining Harris Interest Points and the SIFT Descriptor for Fast Scale-Invariant Object Recognition.

approach in learning rotation-invariant image priors and in building rotation-equivariant and invariant descriptors of learned features, which result in state-of-the-art perfor-mance for rotation-invariant object detection.

Details Fast learning and invariant object recognition PDF

Introduction Despite having been extensively studied, the problem of identifying suitable feature representations for. The three machine learning approaches to object detection are The Viola-Jones Framework, SIFT and HOG (Histogram of Oriented Gradients).

Object detection is extensively used in performing computer vision tasks such as face detection, video object co. Ohba et al.: Appearance-based visual learning and object recognition with illumination invariance CCD Color Camera θ1 θ2 Light Source PUMA Fig.

Experimental setup Eigenspace technique Let M be the number of the images z1,z2,zM in a training set related to each rotation of viewpoints θ1 and θ2, as shown in Fig View-invariant object category learning, recognition, and search: How spatial and object attention are coordinated using surface-based attentional shroudsq Arash Fazl, Stephen Grossberg*, Ennio Mingolla Department of Cognitive and Neural Systems, Center for Adaptive Systems and Center of Excellence for Learning in Education.

Invariant object recognition refers to recognizing an object regardless of irrelevant image variations, such as variations in viewpoint, lighting, retinal size, background, etc.

The perceptual result of invariance, where the perception of a given object property is unaffected by irrelevant image variations, is often referred to as perceptual constancy (Kofka, ; Walsh and Cited by: 5.

The overview is intended to be useful to computer vision and multimedia analysis researchers, as well as to general machine learning researchers, who are interested in the state of the art in deep learning for computer vision tasks, such as object detection and recognition, face recognition, action/activity recognition, and human pose by:.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner.

Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion .Topics will include part-based models for recognition, invariant local features, bags of features, local spatial constraints, shape descriptors and matching, learning similarity measures, fast indexing methods, recognition with text and images, the role of context in recognition, and unsupervised category discovery.Visual object recognition refers to the ability to identify the objects in view based on visual input.

One important signature of visual object recognition is "object invariance", or the ability to identify objects across changes in the detailed context in which objects are viewed, including changes in illumination, object pose, and background context.