Texture Segmentation and the Familiarity Effect

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 265-265 ◽  
Author(s):  
C Meinecke
Keyword(s):  
Texture Segmentation ◽  
Data Driven ◽  
Visual Features ◽  
Familiarity Effect ◽  
Visual Processes ◽  
Background Texture ◽  
Presentation Time ◽  
Series Of Experiments ◽  
Short Presentation Time ◽  
Performance Results

Texture segmentation performance is usually defined as being data-driven and bottom - up: visual features of the stimulus—like orientation differences between target and background texture elements—are then evaluated automatically. The question investigated in the experiments reported here is: Are there some hints that not only ‘pure visual features’ determine segmentation performance, but other factors like the familiarity of the stimulus material already exert an influence at these early stages of information processing? The familiarity effect is revealed by better performance when detecting an unfamiliar element embedded in familiar elements (eg an inverted letter ‘N’ among correct ‘N's) compared with the familiar element embedded in unfamiliar elements (see, eg, Frith, 1974 Perception & Psychophysics16 113 – 116). In a series of experiments, spatial and temporal factors of the stimulus conditions (eg density, jitter, display size, presentation time) have been varied, so as to determine the constraints under which the familiarity effect influences texture-segmentation performance. Results showed that the familiarity of texture elements had a rather strong influence on early visual processes. This influence disappeared only under very restricted display conditions (very short presentation time, very high density). This provides further information on which framing conditions are typical for data-driven early vision processes.

When Feature Information Comes First! Early Processing of Inverted Faces

Perception ◽  
10.1068/p5192 ◽  
2005 ◽  
Vol 34 (9) ◽  
pp. 1117-1134 ◽  
Author(s):  
Claus-Christian Carbon ◽  
Helmut Leder
Keyword(s):  
Information Processing ◽  
Face Recognition ◽  
Face Processing ◽  
Contextual Information ◽  
Presentation Time ◽  
Early Processing ◽  
Feature Information ◽  
Short Presentation Time ◽  
Short Presentation

We investigated the early stages of face recognition and the role of featural and holistic face information. We exploited the fact that, on inversion, the alienating disorientation of the eyes and mouth in thatcherised faces is hardly detectable. This effect allows featural and holistic information to be dissociated and was used to test specific face-processing hypotheses. In inverted thatcherised faces, the cardinal features are already correctly oriented, whereas in undistorted faces, the whole Gestalt is coherent but all information is disoriented. Experiment 1 and experiment 3 revealed that, for inverted faces, featural information processing precedes holistic information. Moreover, the processing of contextual information is necessary to process local featural information within a short presentation time (26 ms). Furthermore, for upright faces, holistic information seems to be available faster than for inverted faces (experiment 2). These differences in processing inverted and upright faces presumably cause the differential importance of featural and holistic information for inverted and upright faces.

Recognition of Neonates by Facial-Visual Characteristics

PEDIATRICS ◽  
1984 ◽  
Vol 74 (4) ◽  
pp. 501-504
Author(s):  
Richard H. Porter ◽  
Jennifer M. Cernoch ◽  
Rene D. Balogh
Keyword(s):  
Post Partum ◽  
Newborn Infants ◽  
Sex Identification ◽  
Chance Level ◽  
Visual Features ◽  
Facial Features ◽  
Series Of Experiments ◽  
Visual Characteristics ◽  
Genetically Determined ◽  
Physical Resemblance

A series of experiments investigated the salience of newborn infants' facial-visual features for recognition and sex identification. Within 33 hours post-partum, mothers recognized photographs of their own offspring when presented with those of unrelated neonates. Furthermore, adult subjects were able to match photographs of unfamiliar mothers and their infants, and determine the sex of neonates, at a greater than chance level of accuracy. Although recognizable facial features are presumably genetically determined, maternal recognition of offspring probably results from brief exposure and familiarization as well as physical resemblance between the infant and other familiar family members, including the mother herself.

scholarly journals H.761 Support of a New concept Element and a New "recognition" Node-Event to Enable Deep Learning-based Analyses for Media-Nodes

2019 ◽  
Author(s):  
Antônio Busson ◽  
Alan L. V. Guedes ◽  
Sergio Colcher
Keyword(s):  
Deep Learning ◽  
Visual Features ◽  
Learning Methods ◽  
Field Methods ◽  
Visual Tasks ◽  
Presentation Time ◽  
The Media ◽  
Image Pixels ◽  
Specific Concept ◽  
Content Semantics

Machine Learning field, methods based on Deep Learning (e.g. CNN, RNN) becomes the state-of-the-art in several problems of the multimedia domain, especially in audio-visual tasks. Typically, the training of Deep Learning Methods is done in a supervised manner, and it is trained on datasets containing thousands/millions of media examples and several related concepts/classes. During training, the Deep Learning Methods learn a hierarchy of filters that are applied to input data to classify/recognize the media content. In computer vision scenario, for example, given image pixels, the series of layers of the network can learn to extract visual features from it, the shallow layers can extract lower-level features (e.g. edges, corner, contours), while the deeper combine these features to produce higher-level features (e.g. textures, part of objects). These representative features can be clustered into groups, each one representing a specific concept. H.761 NCL currently lacks support for Deep Learning Methods inside their application specification. Because those languages still focus on presentations tasks such as capture, streaming, and presentation. They do not consider programmers to describe the semantic understanding of the used media and handle recognition of such under-standing. In this proposal, we aim at extending NCL to provide such support. More precisely, our proposal able NCL application support: (1) describe learning-based on structured multimedia datasets; (2) recognize content semantics of the media elements in presentation time. To achieve such goals, we propose, an extension that includes: (a) the new "knowledge" element describe concepts based on multimedia datasets; (b) "area" anchor with an associated "recognition" event that describes when a concept occurrences in multimedia content.

scholarly journals On-sky results for adaptive optics control with data-driven models on low-order modes

2020 ◽  
Vol 498 (3) ◽  
pp. 3228-3240
Author(s):  
Baptiste Sinquin ◽  
Léonard Prengère ◽  
Caroline Kulcsár ◽  
Henri-François Raynaud ◽  
Eric Gendron ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
High Performance ◽  
Temporal Dynamics ◽  
Data Driven ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Integral Action ◽  
High Performance Systems ◽  
Performance Results ◽  
Low Order

ABSTRACT Dedicated tip–tilt loops are commonly implemented on adaptive optics (AO) systems. In addition, a number of recent high-performance systems feature tip–tilt controllers that are more efficient than the integral action controller. In this context, linear–quadratic–Gaussian (LQG) tip–tilt regulators based on stochastic models identified from AO telemetry have demonstrated their capacity to effectively compensate for the cumulated effects of atmospheric disturbance, windshake and vibrations. These tip–tilt LQG regulators can also be periodically retuned during AO operations, thus allowing to track changes in the disturbances’ temporal dynamics. This paper investigates the potential benefit of extending the number of low-order modes to be controlled using models identified from AO telemetry. The global stochastic dynamical model of a chosen number of turbulent low-order modes is identified through data-driven modelling from wavefront sensor measurements. The remaining higher modes are modelled using priors with autoregressive models of order 2. The loop is then globally controlled using the optimal LQG regulator build from all these models. Our control strategy allows for combining a dedicated tip–tilt loop with a deformable mirror that corrects for the remaining low-order modes and for the higher orders altogether, without resorting to mode decoupling. Performance results are obtained through evaluation of the Strehl ratio computed on H-band images from the scientific camera, or in replay mode using on-sky AO telemetry recorded in 2019 July on the CANARY instrument.

scholarly journals Simple action alters attention towards visual features

2021 ◽  
Author(s):  
Zixuan Wang ◽  
Blaire J. Weidler ◽  
Pei Sun ◽  
Richard A. Abrams
Keyword(s):  
Visual Search ◽  
Strong Influence ◽  
Visual Features ◽  
Prime Stimulus ◽  
Action Effect ◽  
Series Of Experiments ◽  
Simple Action ◽  
Prior Action

AbstractRecent studies have revealed anaction effect, in which a simple action towards a prime stimulus biases attention in a subsequent visual search in favor of objects that match the prime. However, to date the majority of research on the phenomenon has studied search elements that are exact matches to the prime, and that vary only on the dimension of color, making it unclear how general the phenomenon is. Here, across a series of experiments, we show that action can also prioritize objects that match the shape of the prime. Additionally, action can prioritize attention to objects that match only one of either the color or the shape of the prime, suggesting that action enhances individual visual features present in the acted-on objects. The pattern of results suggests that the effect may be stronger for color matches – prioritization for shape only occurred when attention was not drawn to the color of the prime, whereas prioritization for color occurred regardless. Taken together, the results reveal that a prior action can exert a strong influence on subsequent attention towards features of the acted-on object.

scholarly journals Rapid detection of social interactions is the result of domain general attentional processes

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0258832
Author(s):  
Jonathan C. Flavell ◽  
Harriet Over ◽  
Tim Vestner ◽  
Richard Cook ◽  
Steven P. Tipper
Keyword(s):  
Social Interactions ◽  
Visual Features ◽  
Search Performance ◽  
Attentional Processes ◽  
Low Level ◽  
Attention Orienting ◽  
Primary Mechanism ◽  
Series Of Experiments ◽  
Additional Role

Using visual search displays of interacting and non-interacting pairs, it has been demonstrated that detection of social interactions is facilitated. For example, two people facing each other are found faster than two people with their backs turned: an effect that may reflect social binding. However, recent work has shown the same effects with non-social arrow stimuli, where towards facing arrows are detected faster than away facing arrows. This latter work suggests a primary mechanism is an attention orienting process driven by basic low-level direction cues. However, evidence for lower level attentional processes does not preclude a potential additional role of higher-level social processes. Therefore, in this series of experiments we test this idea further by directly comparing basic visual features that orient attention with representations of socially interacting individuals. Results confirm the potency of orienting of attention via low-level visual features in the detection of interacting objects. In contrast, there is little evidence for the representation of social interactions influencing initial search performance.

scholarly journals Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data

2018 ◽  
Vol 51 (3-4) ◽  
pp. 83-93 ◽  
Author(s):  
Biao Wang ◽  
Zhizhong Mao
Keyword(s):  
Electric Arc Furnace ◽  
Electric Arc ◽  
Data Driven ◽  
Arc Furnace ◽  
Process Data ◽  
Series Of Experiments ◽  
Test Points ◽  
One Class Classifier ◽  
Comparative Results ◽  
One Class Classification

The presence of outliers is the main reason leading to ineffectiveness of advanced data-driven control methods in electric arc furnace systems. This paper proposes a hybrid method dedicated to detecting outliers in electric arc furnace systems, where process data are characterized as unlabeled, imbalanced, non-stationary and noisy. First, the raw data are divided into certain number of clusters. Then, with each cluster, a one-class classifier can be trained. So with these well-trained sub-models, new test points can be investigated. Those points that are rejected by all sub-models will be labeled as outliers. With the combination of one-class classification and clustering technique, the intricate data in electric arc furnace can be processed effectively. In addition, the detector will be updated with a specific strategy to enhance its adaptiveness. A series of experiments are carried out, and comparative results have shown the effectiveness of our method.

Data-Driven Approaches to Understanding Visual Neuron Activity

2019 ◽  
Vol 5 (1) ◽  
pp. 451-477 ◽  
Author(s):  
Daniel A. Butts
Keyword(s):  
Neural Activity ◽  
Visual Processing ◽  
Visual Stimulation ◽  
Visual Pathway ◽  
Data Driven ◽  
Visual Features ◽  
Neuron Activity ◽  
Visual Neurons ◽  
Neural Selectivity ◽  
Visual Neuron

With modern neurophysiological methods able to record neural activity throughout the visual pathway in the context of arbitrarily complex visual stimulation, our understanding of visual system function is becoming limited by the available models of visual neurons that can be directly related to such data. Different forms of statistical models are now being used to probe the cellular and circuit mechanisms shaping neural activity, understand how neural selectivity to complex visual features is computed, and derive the ways in which neurons contribute to systems-level visual processing. However, models that are able to more accurately reproduce observed neural activity often defy simple interpretations. As a result, rather than being used solely to connect with existing theories of visual processing, statistical modeling will increasingly drive the evolution of more sophisticated theories.

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