Consistent Emotions Elicited by Low-Level Visual Features in Abstract Art

2014 ◽  
Vol 2 (1-2) ◽  
pp. 99-118 ◽  
Author(s):  
Jorien van Paasschen ◽  
Elisa Zamboni ◽  
Francesca Bacci ◽  
David Melcher
Keyword(s):  
Rating Scale ◽  
Stimulus Onset ◽  
Abstract Art ◽  
Visual Features ◽  
Artistic Style ◽  
Low Level ◽  
Visual Characteristics ◽  
Uniform Manner ◽  
Valence And Arousal ◽  
High Consistency

It is often assumed that works of art have the ability to elicit emotion in their observers. An emotional response to a visual stimulus can occur as early as 120 ms after stimulus onset, before object categorisation can take place. This implies that emotions elicited by an artwork may depend in part on bottom-up processing of its visual features (e.g., shape, colour, composition) and not just on object recognition or understanding of artistic style. We predicted that participants are able to judge the emotion conveyed by an artwork in a manner that is consistent across observers. We tested this hypothesis using abstract paintings; these do not provide any reference to objects or narrative contexts, so that any perceived emotion must stem from basic visual characteristics. Nineteen participants with no background in art rated 340 abstract artworks from different artistic movements on valence and arousal on a Likert scale. An intra-class correlation model showed a high consistency in ratings across observers. Importantly, observers used the whole range of the rating scale. Artworks with a high number of edges (complex) and dark colours were rated as more arousing and more negative compared to paintings containing clear lines, bright colours and geometric shapes. These findings provide evidence that emotions can be captured in a meaningful way by the artist in a set of low-level visual characteristics, and that observers interpret this emotional message in a consistent, uniform manner.

scholarly journals Electrophysiological correlates of the interplay between low-level visual features and emotional content during word reading

2018 ◽  
Author(s):  
Sebastian Schindler ◽  
Antonio Schettino ◽  
Gilles Pourtois
Keyword(s):  
Word Processing ◽  
Word Reading ◽  
Electrophysiological Study ◽  
Semantic Content ◽  
Stimulus Onset ◽  
Event Related Potential ◽  
Visual Features ◽  
Low Contrast ◽  
Attentional Processes ◽  
Low Level

Processing affectively charged visual stimuli typically results in increased amplitude of specific event-related potential (ERP) components. Low-level features similarly modulate electrophysiological responses, with amplitude changes proportional to variations in stimulus size and contrast. However, it remains unclear whether emotion-related amplifications during visual word processing are necessarily intertwined with changes in specific low-level features or, instead, may act independently.In this pre-registered electrophysiological study, we varied font size and contrast of neutral and negative words while participants were monitoring their semantic content. We examined ERP responses associated with early sensory and attentional processes as well as later stages of stimulus processing. Results showed amplitude modulations by low-level visual features early on following stimulus onset – i.e., P1 and N1 components –, while the LPP was independently modulated by these visual features. Independent effects of size and emotion were observed only at the level of the EPN. Here, larger EPN amplitudes for negative were observed only for small high contrast and large low contrast words. These results suggest that early increase in sensory processing at the EPN level for negative words is not automatic, but bound to specific combinations of low-level features, occurring presumably via attentional control processes.

scholarly journals View-tuned and view-invariant face encoding in IT cortex is explained by selected natural image fragments

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yunjun Nam ◽  
Takayuki Sato ◽  
Go Uchida ◽  
Ekaterina Malakhova ◽  
Shimon Ullman ◽  
...  
Keyword(s):  
Face Recognition ◽  
Visual Pathway ◽  
Visual Features ◽  
Natural Image ◽  
Face Representation ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
Neural Substrate ◽  
The Face

AbstractHumans recognize individual faces regardless of variation in the facial view. The view-tuned face neurons in the inferior temporal (IT) cortex are regarded as the neural substrate for view-invariant face recognition. This study approximated visual features encoded by these neurons as combinations of local orientations and colors, originated from natural image fragments. The resultant features reproduced the preference of these neurons to particular facial views. We also found that faces of one identity were separable from the faces of other identities in a space where each axis represented one of these features. These results suggested that view-invariant face representation was established by combining view sensitive visual features. The face representation with these features suggested that, with respect to view-invariant face representation, the seemingly complex and deeply layered ventral visual pathway can be approximated via a shallow network, comprised of layers of low-level processing for local orientations and colors (V1/V2-level) and the layers which detect particular sets of low-level elements derived from natural image fragments (IT-level).

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.

Anchor Effects and Figural Aftereffects: A Comparative Psychophysical Investigation

1975 ◽  
Vol 41 (3) ◽  
pp. 791-796 ◽  
Author(s):  
Johannes Abresch ◽  
Viktor Sarris
Keyword(s):  
Stimulus Onset Asynchrony ◽  
Contrast Effect ◽  
Rating Scale ◽  
Stimulus Onset ◽  
Figural Aftereffect ◽  
Anchor Effect ◽  
Substantial Influence ◽  
Perceptual Contrast ◽  
Points Of View ◽  
Onset Asynchrony

Perceptual contrast effect was studied from two points of view, as a special anchor effect and as a special figural aftereffect. Two experiments were conducted to investigate the influence of stimulus onset asynchrony on contrast and assimilation effects, induced and measured by different psychophysical methods. Stimuli were circular beams of light projected on screens (Delboef type of illusion). When anchor and series stimuli were shown and the latter were judged by means of a rating scale, stimulus onset asychrony had no substantial influence on the contrast effect (Exp. I). When the constant method was applied, however, the asynchrony altered the shape of the contrast effect considerably (Exp. II).

scholarly journals Robust Image Labeling Using Conditional Random Fields

2021 ◽  
Author(s):  
Maryam Nematollahi Arani
Keyword(s):  
Object Recognition ◽  
Mixture Models ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Semantic Gap ◽  
Visual Features ◽  
Image Labeling ◽  
Low Level ◽  
Robust Image ◽  
High Level

Object recognition has become a central topic in computer vision applications such as image search, robotics and vehicle safety systems. However, it is a challenging task due to the limited discriminative power of low-level visual features in describing the considerably diverse range of high-level visual semantics of objects. Semantic gap between low-level visual features and high-level concepts are a bottleneck in most systems. New content analysis models need to be developed to bridge the semantic gap. In this thesis, algorithms based on conditional random fields (CRF) from the class of probabilistic graphical models are developed to tackle the problem of multiclass image labeling for object recognition. Image labeling assigns a specific semantic category from a predefined set of object classes to each pixel in the image. By well capturing spatial interactions of visual concepts, CRF modeling has proved to be a successful tool for image labeling. This thesis proposes novel approaches to empowering the CRF modeling for robust image labeling. Our primary contributions are twofold. To better represent feature distributions of CRF potentials, new feature functions based on generalized Gaussian mixture models (GGMM) are designed and their efficacy is investigated. Due to its shape parameter, GGMM can provide a proper fit to multi-modal and skewed distribution of data in nature images. The new model proves more successful than Gaussian and Laplacian mixture models. It also outperforms a deep neural network model on Corel imageset by 1% accuracy. Further in this thesis, we apply scene level contextual information to integrate global visual semantics of the image with pixel-wise dense inference of fully-connected CRF to preserve small objects of foreground classes and to make dense inference robust to initial misclassifications of the unary classifier. Proposed inference algorithm factorizes the joint probability of labeling configuration and image scene type to obtain prediction update equations for labeling individual image pixels and also the overall scene type of the image. The proposed context-based dense CRF model outperforms conventional dense CRF model by about 2% in terms of labeling accuracy on MSRC imageset and by 4% on SIFT Flow imageset. Also, the proposed model obtains the highest scene classification rate of 86% on MSRC dataset.

scholarly journals A Comparative Assessment of Pain Caused by the Placement of Banded Orthodontic Appliances with and without Low-Level Laser Therapy: A Randomized Controlled Prospective Study

2020 ◽  
Vol 8 (1) ◽  
pp. 24 ◽  
Author(s):  
Carmelo Nicotra ◽  
Alessandro Polizzi ◽  
Graziano Zappalà ◽  
Alessandro Leonida ◽  
Francesco Indelicato ◽  
...  
Keyword(s):  
Laser Therapy ◽  
Orthodontic Treatment ◽  
Rating Scale ◽  
Low Level Laser Therapy ◽  
Orthodontic Appliances ◽  
Time Interval ◽  
Low Level ◽  
Low Level Laser ◽  
Level Laser ◽  
Maximum Pain

Patients still refuse or discontinue orthodontic treatment due to related pain and discomfort. In this study, we investigate if low-level laser therapy (LLLT) can reduce pain caused by orthodontic bands. Sixty subjects who needed bands placed on the upper permanent first molars were assigned randomly to the LLLT group, placebo, and control groups. Inclusion criteria were: age range 10–14 years, fully erupted upper first molars in healthy condition, presence of tight mesial proximal contact. Exclusion criteria were: systemic or metabolic diseases, chronic pain or neurological or psychiatric disorders, use of pharmacological agents interfering with pain perception, previous orthodontic treatment or the simultaneous presence of other devices in the patient’s mouth. The assessment of pain was performed by using a numeric rating scale (NRS) considering different time intervals, i.e., immediately after bands placement, 6 h, 24 h, and from day 2 to day 5. Differences in the maximum pain and in pain experienced at each time-point, among the three groups, was assessed by using the Kruskal–Wallis H. The final sample included 56 patients, 29 males, and 27 females, with a mean age of 12.03 ± 1.3 years. Patients were randomly allocated into three groups (tested, control, and placebo group) with each group consisting respectively of 19, 20, and 17 individuals. Subjects in the LLLT experienced less pain at each time interval as well as the maximum pain score being lower in the LLLT compared to control and placebo groups. These findings were all statistically significant (p < 0.05). LLLT can alleviate the intensity of pain after the placement of orthodontic bands.

Toward Semantically Meaningful Feature Spaces for Efficient Indexing in Large Image Databases

2011 ◽  
pp. 1-29
Author(s):  
Anne H.H. Ngu ◽  
Jialie Shen ◽  
John Shepherd
Keyword(s):  
Distance Function ◽  
Image Features ◽  
Image Databases ◽  
Multimedia Databases ◽  
Visual Features ◽  
Hybrid Architecture ◽  
Low Level ◽  
Feature Vectors ◽  
Training Cost ◽  
Low Dimensional

The optimized distance-based access methods currently available for multimedia databases are based on two major assumptions: a suitable distance function is known a priori, and the dimensionality of image features is low. The standard approach to building image databases is to represent images via vectors based on low-level visual features and make retrieval based on these vectors. However, due to the large gap between the semantic notions and low-level visual content, it is extremely difficult to define a distance function that accurately captures the similarity of images as perceived by humans. Furthermore, popular dimension reduction methods suffer from either the inability to capture the nonlinear correlations among raw data or very expensive training cost. To address the problems, in this chapter we introduce a new indexing technique called Combining Multiple Visual Features (CMVF) that integrates multiple visual features to get better query effectiveness. Our approach is able to produce low-dimensional image feature vectors that include not only low-level visual properties but also high-level semantic properties. The hybrid architecture can produce feature vectors that capture the salient properties of images yet are small enough to allow the use of existing high-dimensional indexing methods to provide efficient and effective retrieval.

scholarly journals Predicting visual fixations on video based on low-level visual features

2007 ◽  
Vol 47 (19) ◽  
pp. 2483-2498 ◽  
Author(s):  
Olivier Le Meur ◽  
Patrick Le Callet ◽  
Dominique Barba
Keyword(s):  
Visual Features ◽  
Low Level ◽  
Visual Fixations
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