scholarly journals Efficient tuning of attention to narrow and broad ranges of task-relevant feature values

2021 ◽  
Author(s):  
Angus F. Chapman ◽  
Viola S. Störmer
Keyword(s):  
Color Space ◽  
Feature Space ◽  
Relevant Information ◽  
Attentional Focus ◽  
Coherent Motion ◽  
Motion Direction ◽  
Single Feature ◽  
Feature Based ◽  
Feature Values ◽  
High Level

Feature-based attention is the ability to select relevant information on the basis of visual features, such as a particular color or motion direction. In contrast to spatial attention, where the attentional focus has been shown to be flexibly adjustable to select small or large regions, it is unclear whether feature-based attention can be efficiently tuned to different feature ranges. Here, we establish that the focus of feature-based attention can be adjusted more broadly or narrowly to select currently relevant features. Participants attended to a set of target-colored dots among distractor dots to detect brief decreases in luminance (Experiments 1a and 1b) or bursts of coherent motion (Experiments 2). To vary the size of the attentional focus, we manipulated the range of colors that the target dots spanned and found that while participants’ performance decreased with larger feature ranges to select, it remained at a relatively high level even at the largest color range, suggesting that broadening the focus of feature-based attention comes only at a small cost and that large feature ranges can be selected relatively efficiently at once. We further show that this broad selection occurs uniformly in color space (Experiment 3). Overall, our findings argue against the idea that feature-based attention is limited to a single feature value at a time and demonstrate that selecting large swaths of feature space is surprisingly efficient. Broadly, these results are consistent with accounts that propose a flexible and generalized set of attentional mechanisms that act across both spatial and feature-based domains.

scholarly journals Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 371
Author(s):  
Yu Jin ◽  
Jiawei Guo ◽  
Huichun Ye ◽  
Jinling Zhao ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Random Forest ◽  
Satellite Imagery ◽  
Feature Space ◽  
Kappa Coefficient ◽  
Classification Model ◽  
Support Vector ◽  
Textural Feature ◽  
Monitoring Accuracy ◽  
Areca Catechu ◽  
High Level

The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to investigate and monitor the agricultural and forestry vegetation in Hainan. However, the monitoring accuracy is affected by the cloudy and rainy climate of this region, as well as the high level of land fragmentation. In this paper, we used PlanetScope imagery at a 3 m spatial resolution over the Hainan arecanut planting area to investigate the high-precision extraction of the arecanut planting distribution based on feature space optimization. First, spectral and textural feature variables were selected to form the initial feature space, followed by the implementation of the random forest algorithm to optimize the feature space. Arecanut planting area extraction models based on the support vector machine (SVM), BP neural network (BPNN), and random forest (RF) classification algorithms were then constructed. The overall classification accuracies of the SVM, BPNN, and RF models optimized by the RF features were determined as 74.82%, 83.67%, and 88.30%, with Kappa coefficients of 0.680, 0.795, and 0.853, respectively. The RF model with optimized features exhibited the highest overall classification accuracy and kappa coefficient. The overall accuracy of the SVM, BPNN, and RF models following feature optimization was improved by 3.90%, 7.77%, and 7.45%, respectively, compared with the corresponding unoptimized classification model. The kappa coefficient also improved. The results demonstrate the ability of PlanetScope satellite imagery to extract the planting distribution of arecanut. Furthermore, the RF is proven to effectively optimize the initial feature space, composed of spectral and textural feature variables, further improving the extraction accuracy of the arecanut planting distribution. This work can act as a theoretical and technical reference for the agricultural and forestry industries.

scholarly journals Decision-related feedback in visual cortex lacks spatial selectivity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Katrina R. Quinn ◽  
Lenka Seillier ◽  
Daniel A. Butts ◽  
Hendrikje Nienborg
Keyword(s):  
Contextual Information ◽  
Relevant Information ◽  
Common Mechanism ◽  
Spatial Selectivity ◽  
Population Responses ◽  
Stimulus Choice ◽  
Visual Areas ◽  
Computational Work ◽  
Feature Based ◽  
The Brain

AbstractFeedback in the brain is thought to convey contextual information that underlies our flexibility to perform different tasks. Empirical and computational work on the visual system suggests this is achieved by targeting task-relevant neuronal subpopulations. We combine two tasks, each resulting in selective modulation by feedback, to test whether the feedback reflected the combination of both selectivities. We used visual feature-discrimination specified at one of two possible locations and uncoupled the decision formation from motor plans to report it, while recording in macaque mid-level visual areas. Here we show that although the behavior is spatially selective, using only task-relevant information, modulation by decision-related feedback is spatially unselective. Population responses reveal similar stimulus-choice alignments irrespective of stimulus relevance. The results suggest a common mechanism across tasks, independent of the spatial selectivity these tasks demand. This may reflect biological constraints and facilitate generalization across tasks. Our findings also support a previously hypothesized link between feature-based attention and decision-related activity.

Diminishing Reality: Potential Benefits and Risks

2021 ◽  
Vol 65 (1) ◽  
pp. 164-168
Author(s):  
I. Murph ◽  
M. McDonald ◽  
K. Richardson ◽  
M. Wilkinson ◽  
S. Robertson ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Situational Awareness ◽  
Relevant Information ◽  
Attentional Focus ◽  
Primary Task ◽  
Training Methods ◽  
Cognitive Aids ◽  
Potential Benefits ◽  
Study Participants ◽  
Completion Times

Within distracting environments, it is difficult to maintain attentional focus on complex tasks. Cognitive aids can support attention by adding relevant information to the environment, such as via augmented reality (AR). However, there may be a benefit in removing elements from the environment, such as irrelevant alarms, displays, and conversations. De-emphasis of distracting elements is a type of AR called Diminished Reality (DR). Although de-emphasizing distraction may help focus on a primary task, it may also reduce situational awareness (SA) of other activities that may become relevant. In the current study, participants will assemble a medical ventilator during a simulated emergency while experiencing varying levels of DR. Participants will also be probed to assess secondary SA. We anticipate that participants will have better accuracy and completion times in the full DR conditions but their SA will suffer. Future applications include the design of future DR systems and improved training methods.

An Interactive Graphical Approach to Feature Modeling Using Halfspaces and Geometric Constraints

1990 ◽  
Author(s):  
Maarten J. G. M. van Emmerik
Keyword(s):  
Spatial Relations ◽  
Feature Modeling ◽  
Geometric Constraints ◽  
Boolean Combination ◽  
Graphical Approach ◽  
Geometric Characteristics ◽  
Feature Based ◽  
New Feature ◽  
High Level ◽  
And Control

Abstract Feature modeling enables the specification of a model with standardized high-level shape aspects that have a functional meaning for design or manufacturing. In this paper an interactive graphical approach to feature-based modeling is presented. The user can represent features as new CSG primitives, specified as a Boolean combination of halfspaces. Constraints between halfspaces specify the geometric characteristics of a feature and control feature validity. Once a new feature is defined and stored in a library, it can be used in other objects and positioned, oriented and dimensioned by direct manipulation with a graphics cursor. Constraints between features prevent feature interference and specify spatial relations between features.

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 Rats spontaneously perceive global motion direction of drifting plaids

2021 ◽  
Author(s):  
Giulio Matteucci ◽  
Benedetta Zattera ◽  
Rosilari Bellacosa Marotti ◽  
Davide Zoccolan
Keyword(s):  
Visual Cortex ◽  
Perceived Similarity ◽  
Global Motion ◽  
Motion Direction ◽  
Visual Objects ◽  
Visual Priming ◽  
Priming Paradigm ◽  
High Level ◽  
Mouse Visual Cortex ◽  
Motion Detectors

AbstractComputing global motion direction of extended visual objects is a hallmark of primate high-level vision. Although neurons selective for global motion have also been found in mouse visual cortex, it remains unknown whether rodents can combine multiple motion signals into global, integrated percepts. To address this question, we trained two groups of rats to discriminate either gratings (G group) or plaids (i.e., superpositions of gratings with different orientations; P group) drifting horizontally along opposite directions. After the animals learned the task, we applied a visual priming paradigm, where presentation of the target stimulus was preceded by the brief presentation of either a grating or a plaid. The extent to which rat responses to the targets were biased by such prime stimuli provided a measure of the spontaneous, perceived similarity between primes and targets. We found that gratings and plaids, when uses as primes, were equally effective at biasing the perception of plaid direction for the rats of the P group. Conversely, for G group, only the gratings acted as effective prime stimuli, while the plaids failed to alter the perception of grating direction. To interpret these observations, we simulated a decision neuron reading out the representations of gratings and plaids, as conveyed by populations of either component or pattern cells (i.e., local or global motion detectors). We concluded that the findings for the P group are highly consistent with the existence of a population of pattern cells, playing a functional role similar to that demonstrated in primates. We also explored different scenarios that could explain the failure of the plaid stimuli to elicit a sizable priming magnitude for the G group. These simulations yielded testable predictions about the properties of motion representations in rodent visual cortex at the single-cell and circuitry level, thus paving the way to future neurophysiology experiments.

scholarly journals Selection of efficient ships according to the criterion of marginal price at the feasibility study

2021 ◽  
pp. 172-181
Author(s):  
Oksana Y. Vasileva ◽  
Marina V. Nikulina Nikulina ◽  
Juri I. Platov Platov
Keyword(s):  
Fuel Consumption ◽  
Feasibility Study ◽  
Information Technologies ◽  
Optimization Methods ◽  
Relevant Information ◽  
Operating Conditions ◽  
Initial Stage ◽  
Level Of Use ◽  
High Level ◽  
Selection Of

The article deals with the problem of selecting efficient ships by the feasibility study in which brake power, main dimensions, payload, speed and fuel consumption are determined. The necessity of using the proposed selection at the initial stage of the ship's design is justified; the problems that arise at the present time are denoted. The purpose of the article is to propose a criterion for the selection of efficient vessels, "tied" to the operating conditions, based on the marginal cost of the ship. A method for its determination is presented. At the same time, annual revenues and operating costs should be determined by modern methods of business planning for the operation of the fleet. When searching for the parameters of the ship, the optimal fuel consumption is determined. The rest of the costs can be found according to the coefficients "tied" to the fuel consumption and calculated on the basis of existing prototypes. The results of calculations by the proposed method are shown; its merits and opportunities for improvement are noted with the availability of relevant information. The conclusion is made about the convenience and applicability of the proposed option for selecting efficient ship for the feasibility study based on optimization methods for determining the parameters of vessels under conditions of a high level of use of information technologies.

Representing Tolerance and Assembly Information in a Feature-Based Design Environment

1991 ◽  
Author(s):  
Rajneet Sodhi ◽  
Joshua U. Turner
Keyword(s):  
User Interface ◽  
Design Environment ◽  
Assembly Design ◽  
Assembly Features ◽  
Feature Based ◽  
The Creation ◽  
High Level ◽  
Feature Based Design

Abstract This paper describes a strategy for representing tolerance information and assembly information in a feature-based design environment. The concept of designing with features is extended to incorporate the specification of tolerance information. This allows appropriate tolerancing strategies to be provided within the feature definitions themselves. Thus a closer connection is formed between features and the functional intent implicit in their use. The concept of designing with features is also extended to incorporate the specification of assembly information, through the use of assembly features which provide a high-level user interface for the creation and modeling of assemblies, and which handle the identification and creation of mating relations between components. Several examples of component and assembly design using this extended feature-based approach are presented.

scholarly journals EMCM: A Novel Binary Edge-Feature-Based Maximum Clique Framework for Multispectral Image Matching

2019 ◽  
Vol 11 (24) ◽  
pp. 3026
Author(s):  
Bin Fang ◽  
Kun Yu ◽  
Jie Ma ◽  
Pei An
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Maximum Clique ◽  
Feature Space ◽  
Multispectral Image ◽  
Feature Descriptor ◽  
Ranking Algorithm ◽  
Feature Based ◽  
Edge Feature

Seeking reliable correspondence between multispectral images is a fundamental and important task in computer vision. To overcome the nonlinearity problem occurring in multispectral image matching, a novel, edge-feature-based maximum clique-matching frame (EMCM) is proposed, which contains three main parts: (1) a novel strong edge binary feature descriptor, (2) a new correspondence-ranking algorithm based on keypoint distinctiveness analysis algorithms in the feature space of the graph, and (3) a false match removal algorithm based on maximum clique searching in the correspondence space of the graph considering both position and angle consistency. Extensive experiments are conducted on two standard multispectral image datasets with respect to the three parts. The feature-matching experiments suggest that the proposed feature descriptor is of high descriptiveness, robustness, and efficiency. The correspondence-ranking experiments validate the superiority of our correspondences-ranking algorithm over the nearest neighbor algorithm, and the coarse registration experiments show the robustness of EMCM with varied interferences.

scholarly journals Application of Color Featuring and Deep Learning in Maize Plant Detection

2020 ◽  
Vol 12 (14) ◽  
pp. 2229
Author(s):  
Haojie Liu ◽  
Hong Sun ◽  
Minzan Li ◽  
Michihisa Iida
Keyword(s):  
Deep Learning ◽  
False Negative ◽  
Color Space ◽  
Performance Comparison ◽  
Maize Plant ◽  
Detection Accuracy ◽  
Learning Technology ◽  
Practical Applications ◽  
Feature Based ◽  
Detection Speed

Maize plant detection was conducted in this study with the goals of target fertilization and reduction of fertilization waste in weed spots and gaps between maize plants. The methods used included two types of color featuring and deep learning (DL). The four color indices used were excess green (ExG), excess red (ExR), ExG minus ExR, and the hue value from the HSV (hue, saturation, and value) color space, while the DL methods used were YOLOv3 and YOLOv3_tiny. For practical application, this study focused on performance comparison in detection accuracy, robustness to complex field conditions, and detection speed. Detection accuracy was evaluated by the resulting images, which were divided into three categories: true positive, false positive, and false negative. The robustness evaluation was performed by comparing the average intersection over union of each detection method across different sub–datasets—namely original subset, blur processing subset, increased brightness subset, and reduced brightness subset. The detection speed was evaluated by the indicator of frames per second. Results demonstrated that the DL methods outperformed the color index–based methods in detection accuracy and robustness to complex conditions, while they were inferior to color feature–based methods in detection speed. This research shows the application potential of deep learning technology in maize plant detection. Future efforts are needed to improve the detection speed for practical applications.

Sign in / Sign up

Export Citation Format

Share Document