scholarly journals Dissociable effects of visual crowding on the perception of colour and motion

2019 ◽  
Author(s):  
John A. Greenwood ◽  
Michael J. Parsons
Keyword(s):  
Peripheral Vision ◽  
Population Coding ◽  
Visual Features ◽  
Double Dissociation ◽  
Multiple Processes ◽  
Underlying Mechanisms ◽  
High Level ◽  
Object Features ◽  
Visual Crowding ◽  
Selective Mechanism

AbstractOur ability to recognise objects in peripheral vision is fundamentally limited by crowding, the deleterious effect of clutter that disrupts the recognition of features ranging from orientation and colour to motion and depth. Prior research is equivocal on whether this reflects a singular process that disrupts all features simultaneously or multiple processes that affect each independently. We examined crowding for motion and colour, two features that allow a strong test of feature independence. ‘Cowhide’ stimuli were presented 15 degrees in peripheral vision, either in isolation or surrounded by flankers to give crowding. Observers reported either the target direction (clockwise/counterclockwise from upwards) or its hue (blue/purple). We first established that both features show systematic crowded errors (predominantly biased towards the flanker identities) and selectivity for target-flanker similarity (with reduced crowding for dissimilar target/flanker elements). The multiplicity of crowding was then tested with observers identifying both features: a singular object-selective mechanism predicts that when crowding is weak for one feature and strong for the other that crowding should be all-or-none for both. In contrast, when crowding was weak for colour and strong for motion, errors were reduced for colour but remained for motion, and vice versa with weak motion and strong colour crowding. This double dissociation reveals that crowding disrupts certain combinations of visual features in a feature-specific manner, ruling out a singular object-selective mechanism. The ability to recognise one aspect of a cluttered scene, like colour, thus offers no guarantees for the correct recognition of other aspects, like motion.Significance statementOur peripheral vision is primarily limited by crowding, the disruption to object recognition that arises in clutter. Crowding is widely assumed to be a singular process, affecting all of the features (orientation, motion, colour, etc.) within an object simultaneously. In contrast, we observe a double dissociation whereby observers make errors regarding the colour of a crowded object whilst correctly judging its direction, and vice versa. This dissociation can be reproduced by a population-coding model where the direction and hue of target/flanker elements are pooled independently. The selective disruption of some object features independently of others rules out a singular crowding mechanism, posing problems for high-level crowding theories, and suggesting that the underlying mechanisms may be distributed throughout the visual system.

scholarly journals Dissociable effects of visual crowding on the perception of color and motion

2020 ◽  
Vol 117 (14) ◽  
pp. 8196-8202 ◽  
Author(s):  
John A. Greenwood ◽  
Michael J. Parsons
Keyword(s):  
Deleterious Effect ◽  
Peripheral Vision ◽  
The Other ◽  
Correct Recognition ◽  
Visual Features ◽  
Double Dissociation ◽  
Multiple Processes ◽  
Specific Manner ◽  
Visual Crowding ◽  
Selective Mechanism

Our ability to recognize objects in peripheral vision is fundamentally limited by crowding, the deleterious effect of clutter that disrupts the recognition of features ranging from orientation and color to motion and depth. Previous research is equivocal on whether this reflects a singular process that disrupts all features simultaneously or multiple processes that affect each independently. We examined crowding for motion and color, two features that allow a strong test of feature independence. “Cowhide” stimuli were presented 15° in peripheral vision, either in isolation or surrounded by flankers to give crowding. Observers reported either the target direction (clockwise/counterclockwise from upward) or its hue (blue/purple). We first established that both features show systematic crowded errors (biased predominantly toward the flanker identities) and selectivity for target–flanker similarity (with reduced crowding for dissimilar target/flanker elements). The multiplicity of crowding was then tested with observers identifying both features. Here, a singular object-selective mechanism predicts that when crowding is weak for one feature and strong for the other that crowding should be all-or-none for both. In contrast, when crowding was weak for color and strong for motion, errors were reduced for color but remained for motion, and vice versa with weak motion and strong color crowding. This double dissociation reveals that crowding disrupts certain combinations of visual features in a feature-specific manner, ruling out a singular object-selective mechanism. Thus, the ability to recognize one aspect of a cluttered scene, like color, offers no guarantees for the correct recognition of other aspects, like motion.

Fuzzy Model for Human Color Perception and Its Application in E-Commerce

2016 ◽  
Vol 24 (Suppl. 2) ◽  
pp. 47-70 ◽  
Author(s):  
Pakizar Shamoi ◽  
Atsushi Inoue ◽  
Hiroharu Kawanaka
Keyword(s):  
Image Retrieval ◽  
Color Perception ◽  
Fuzzy Model ◽  
Visual Features ◽  
Perceptual Categorization ◽  
Purchasing Behavior ◽  
User Query ◽  
Hsi Space ◽  
Underlying Mechanisms ◽  
High Level

Although image retrieval for e-commerce field has a huge commercial potential, e-commerce oriented content-based image retrieval is still very raw. Modern online shopping systems have certain limitations. In particular, they use conventional tag-based retrieval and lack making use of visual content. The paper presents a methodology to retrieve images of shopping items based on fuzzy dominant colors. People regard color as an aesthetic issue, especially when it comes to choosing the colors of their clothing, apartment design and other objects around. No doubt, color inuences purchasing behavior — to a certain extent, it is a reection of human's likes and dislikes. The fuzzy color model that we are proposing represents the collection of fuzzy sets, providing the conceptual quantization of crisp HSI space having soft boundaries. The proposed method has two parts: assigning a fuzzy colorimetric profile to the image and processing the user query. We also use underlying mechanisms of attention from a theory of visual attention, like perceptual categorization. Subjectivity and sensitivity of humans in color perception and bridging the semantic gap between low-level color visual features and high-level concepts are major issues that we plan to tackle in this research.

scholarly journals A survey: which features are required for dynamic visual simultaneous localization and mapping?

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zewen Xu ◽  
Zheng Rong ◽  
Yihong Wu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Dynamic Environments ◽  
Visual Features ◽  
Advantages And Disadvantages ◽  
Intelligent Robots ◽  
Localization And Mapping ◽  
High Level ◽  
Static World ◽  
Robotic Applications ◽  
Significant Attention

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

scholarly journals A summary-statistic representation in peripheral vision explains visual crowding

2009 ◽  
Vol 9 (12) ◽  
pp. 13-13 ◽  
Author(s):  
B. Balas ◽  
L. Nakano ◽  
R. Rosenholtz
Keyword(s):  
Peripheral Vision ◽  
Summary Statistic ◽  
Visual Crowding

scholarly journals Knowledge-driven perceptual organization reshapes information sampling via eye movements

2021 ◽  
Author(s):  
Marek A. Pedziwiatr ◽  
Elisabeth von dem Hagen ◽  
Christoph Teufel
Keyword(s):  
Eye Movements ◽  
Feature Space ◽  
Object Perception ◽  
Visual Features ◽  
Object Representations ◽  
Object Knowledge ◽  
Recent Developments ◽  
Gaze Behaviour ◽  
Information Sampling ◽  
High Level

Humans constantly move their eyes to explore the environment and obtain information. Competing theories of gaze guidance consider the factors driving eye movements within a dichotomy between low-level visual features and high-level object representations. However, recent developments in object perception indicate a complex and intricate relationship between features and objects. Specifically, image-independent object-knowledge can generate objecthood by dynamically reconfiguring how feature space is carved up by the visual system. Here, we adopt this emerging perspective of object perception, moving away from the simplifying dichotomy between features and objects in explanations of gaze guidance. We recorded eye movements in response to stimuli that appear as meaningless patches on initial viewing but are experienced as coherent objects once relevant object-knowledge has been acquired. We demonstrate that gaze guidance differs substantially depending on whether observers experienced the same stimuli as meaningless patches or organised them into object representations. In particular, fixations on identical images became object-centred, less dispersed, and more consistent across observers once exposed to relevant prior object-knowledge. Observers' gaze behaviour also indicated a shift from exploratory information-sampling to a strategy of extracting information mainly from selected, object-related image areas. These effects were evident from the first fixations on the image. Importantly, however, eye-movements were not fully determined by object representations but were best explained by a simple model that integrates image-computable features and high-level, knowledge-dependent object representations. Overall, the results show how information sampling via eye-movements in humans is guided by a dynamic interaction between image-computable features and knowledge-driven perceptual organisation.

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.

Access Control Models for Online Social Networks

2014 ◽  
pp. 451-484
Author(s):  
Rula Sayaf ◽  
Dave Clarke
Keyword(s):  
Social Networks ◽  
Access Control ◽  
Online Social Networks ◽  
Personal Data ◽  
Security And Privacy ◽  
Open Problems ◽  
Access Control Models ◽  
Control Models ◽  
Underlying Mechanisms ◽  
High Level

Access control is one of the crucial aspects in information systems security. Authorizing access to resources is a fundamental process to limit potential privacy violations and protect users. The nature of personal data in online social networks (OSNs) requires a high-level of security and privacy protection. Recently, OSN-specific access control models (ACMs) have been proposed to address the particular structure, functionality and the underlying privacy issues of OSNs. In this survey chapter, the essential aspects of access control and review the fundamental classical ACMs are introduced. The specific OSNs features and review the main categories of OSN-specific ACMs are highlighted. Within each category, the most prominent ACMs and their underlying mechanisms that contribute enhancing privacy of OSNs are surveyed. Toward the end, more advanced issues of access control in OSNs are discussed. Throughout the discussion, different models and highlight open problems are contrasted. Based on these problems, the chapter is concluded by proposing requirements for future ACMs.

SEARCHING AND MINING VISUALLY OBSERVED PHENOTYPES OF MAIZE MUTANTS

2007 ◽  
Vol 05 (06) ◽  
pp. 1193-1213 ◽  
Author(s):  
CHI-REN SHYU ◽  
JATURON HARNSOMBURANA ◽  
JASON GREEN ◽  
ADRIAN S. BARB ◽  
TONI KAZIC ◽  
...  
Keyword(s):  
Cell Biology ◽  
Species Conservation ◽  
Genetic Maps ◽  
Cell Physiology ◽  
Complex Processes ◽  
Intimate Knowledge ◽  
Scientific Challenge ◽  
Underlying Mechanisms ◽  
Mutant Phenotypes ◽  
High Level

There are thousands of maize mutants, which are invaluable resources for plant research. Geneticists use them to study underlying mechanisms of biochemistry, cell biology, cell development, and cell physiology. To streamline the understanding of such complex processes, researchers need the most current versions of genetic and physical maps, tools with the ability to recognize novel phenotypes or classify known phenotypes, and an intimate knowledge of the biochemical processes generating physiological and phenotypic effects. They must also know how all of these factors change and differ among species, diverse alleles, germplasms, and environmental conditions. While there are robust databases, such as MaizeGDB, for some of these types of raw data, other crucial components are missing. Moreover, the management of visually observed mutant phenotypes is still in its infant stage, let alone the complex query methods that can draw upon high-level and aggregated information to answer the questions of geneticists. In this paper, we address the scientific challenge and propose to develop a robust framework for managing the knowledge of visually observed phenotypes, mining the correlation of visual characteristics with genetic maps, and discovering the knowledge relating to cross-species conservation of visual and genetic patterns. The ultimate goal of this research is to allow a geneticist to submit phenotypic and genomic information on a mutant to a knowledge base and ask, "What genes or environmental factors cause this visually observed phenotype?".

scholarly journals The role of Peripheral Vision in the Flashed Face Distortion Effect

Perception ◽  
2018 ◽  
Vol 48 (1) ◽  
pp. 93-101
Author(s):  
Jamie Bowden ◽  
David Whitaker ◽  
Matt J. Dunn
Keyword(s):  
Visual Acuity ◽  
Analogue Scale ◽  
Peripheral Vision ◽  
Crowding Effect ◽  
Visual Periphery ◽  
Peripheral Location ◽  
Distortion Effect ◽  
Optical Defocus ◽  
Visual Crowding

The flashed face distortion effect is a phenomenon whereby images of faces, presented at 4–5 Hz in the visual periphery, appear distorted. It has been hypothesized that the effect is driven by cortical, rather than retinal, components. Here, we investigated the role of peripheral viewing on the effect. Normally sighted participants viewed the stimulus peripherally, centrally, and centrally with a blurring lens (to match visual acuity in the peripheral location). Participants rated the level of distortion using a Visual Analogue Scale. Although optical defocus did have a significant effect on distortion ratings, peripheral viewing had a much greater effect, despite matched visual acuity. We suggest three potential mechanisms for this finding: increased positional uncertainty in the periphery, reduced deployment of attention to the visual periphery, or the visual crowding effect.

Sign in / Sign up

Export Citation Format

Share Document