291 research outputs found
The effect of time on ear biometrics
We present an experimental study to demonstrate the effect of the time difference in image acquisition for gallery and probe on the performance of ear recognition. This experimental research is the first study on the time effect on ear biometrics. For the purpose of recognition, we convolve banana wavelets with an ear image and then apply local binary pattern on the convolved image. The histograms of the produced image are then used as features to describe an ear. A histogram intersection technique is then applied on the histograms of two ears to measure the ear similarity for the recognition purposes. We also use analysis of variance (ANOVA) to select features to identify the best banana wavelets for the recognition process. The experimental results show that the recognition rate is only slightly reduced by time. The average recognition rate of 98.5% is achieved for an eleven month-difference between gallery and probe on an un-occluded ear dataset of 1491 images of ears selected from Southampton University ear database
Improved line/edge detection and visual reconstruction
Lines and edges provide important information for object categorization and recognition. In addition, one
brightness model is based on a symbolic interpretation of the cortical multi-scale line/edge representation. In
this paper we present an improved scheme for line/edge extraction from simple and complex cells and we illustrate
the multi-scale representation. This representation can be used for visual reconstruction, but also for nonphotorealistic
rendering. Together with keypoints and a new model of disparity estimation, a 3D wireframe representation
of e.g. faces can be obtained in the future
Face segregation and recognition by cortical multi-scale line and edge coding
Models of visual perception are based on image representations in
cortical area V1 and higher areas which contain many cell layers for feature
extraction. Basic simple, complex and end-stopped cells provide input for line,
edge and keypoint detection. In this paper we present an improved method for
multi-scale line/edge detection based on simple and complex cells. We illustrate
the line/edge representation for object reconstruction, and we present models for
multi-scale face (object) segregation and recognition that can be embedded into
feedforward dorsal and ventral data streams (the “what” and “where” subsystems)
with feedback streams from higher areas for obtaining translation, rotation
and scale invariance
High frequency oscillations as a correlate of visual perception
“NOTICE: this is the author’s version of a work that was accepted for publication in International journal of psychophysiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International journal of psychophysiology , 79, 1, (2011) DOI 10.1016/j.ijpsycho.2010.07.004Peer reviewedPostprin
Coding of visual object features and feature conjunctions in the human brain
Peer reviewedPublisher PD
Interpretable Transformations with Encoder-Decoder Networks
Deep feature spaces have the capacity to encode complex transformations of
their input data. However, understanding the relative feature-space
relationship between two transformed encoded images is difficult. For instance,
what is the relative feature space relationship between two rotated images?
What is decoded when we interpolate in feature space? Ideally, we want to
disentangle confounding factors, such as pose, appearance, and illumination,
from object identity. Disentangling these is difficult because they interact in
very nonlinear ways. We propose a simple method to construct a deep feature
space, with explicitly disentangled representations of several known
transformations. A person or algorithm can then manipulate the disentangled
representation, for example, to re-render an image with explicit control over
parameterized degrees of freedom. The feature space is constructed using a
transforming encoder-decoder network with a custom feature transform layer,
acting on the hidden representations. We demonstrate the advantages of explicit
disentangling on a variety of datasets and transformations, and as an aid for
traditional tasks, such as classification.Comment: Accepted at ICCV 201
Cortical object segregation and categorization by multi-scale line and edge coding
In this paper we present an improved scheme for line and edge detection in cortical area V1, based on responses
of simple and complex cells, truly multi-scale with no free parameters. We illustrate the multi-scale
representation for visual reconstruction, and show how object segregation can be achieved with coarse-to-finescale
groupings. A two-level object categorization scenario is tested in which pre-categorization is based on
coarse scales only, and final categorization on coarse plus fine scales. Processing schemes are discussed in the
framework of a complete cortical architecture
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