scholarly journals Untangling featural and conceptual object representations

2019 ◽  
Author(s):  
Tijl Grootswagers ◽  
Amanda K. Robinson ◽  
Sophia M. Shatek ◽  
Thomas A. Carlson
Keyword(s):  
Human Perception ◽  
Presentation Rate ◽  
Visual Display ◽  
Image Features ◽  
Visual Features ◽  
Conceptual Category ◽  
Object Categories ◽  
Visual Inputs ◽  
Statistical Regularities ◽  
Biased Processing

AbstractHow are visual inputs transformed into conceptual representations by the human visual system? The contents of human perception, such as objects presented on a visual display, can reliably be decoded from voxel activation patterns in fMRI, and in evoked sensor activations in MEG and EEG. A prevailing question is the extent to which brain activation associated with object categories is due to statistical regularities of visual features within object categories. Here, we assessed the contribution of mid-level features to conceptual category decoding using EEG and a novel fast periodic decoding paradigm. Our study used a stimulus set consisting of intact objects from the animate (e.g., fish) and inanimate categories (e.g., chair) and scrambled versions of the same objects that were unrecognizable and preserved their visual features (Long, Yu, & Konkle, 2018). By presenting the images at different periodic rates, we biased processing to different levels of the visual hierarchy. We found that scrambled objects and their intact counterparts elicited similar patterns of activation, which could be used to decode the conceptual category (animate or inanimate), even for the unrecognizable scrambled objects. Animacy decoding for the scrambled objects, however, was only possible at the slowest periodic presentation rate. Animacy decoding for intact objects was faster, more robust, and could be achieved at faster presentation rates. Our results confirm that the mid-level visual features preserved in the scrambled objects contribute to animacy decoding, but also demonstrate that the dynamics vary markedly for intact versus scrambled objects. Our findings suggest a complex interplay between visual feature coding and categorical representations that is mediated by the visual system’s capacity to use image features to resolve a recognisable object.

scholarly journals Contrast invariant tuning in human perception of image content

2019 ◽  
Author(s):  
Ingo Fruend ◽  
Jaykishan Patel ◽  
Elee D. Stalker
Keyword(s):  
Visual Processing ◽  
Discrimination Performance ◽  
Human Perception ◽  
Image Features ◽  
Image Contrast ◽  
Visual Features ◽  
Image Content ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Contrast Discrimination

AbstractHigher levels of visual processing are progressively more invariant to low-level visual factors such as contrast. Although this invariance trend has been well documented for simple stimuli like gratings and lines, it is difficult to characterize such invariances in images with naturalistic complexity. Here, we use a generative image model based on a hierarchy of learned visual features—a Generative Adversarial Network—to constrain image manipulations to remain within the vicinity of the manifold of natural images. This allows us to quantitatively characterize visual discrimination behaviour for naturalistically complex, non-linear image manipulations. We find that human tuning to such manipulations has a factorial structure. The first factor governs image contrast with discrimination thresholds following a power law with an exponent between 0.5 and 0.6, similar to contrast discrimination performance for simpler stimuli. A second factor governs image content with approximately constant discrimination thresholds throughout the range of images studied. These results support the idea that human perception factors out image contrast relatively early on, allowing later stages of processing to extract higher level image features in a stable and robust way.

Structure Preserving Non-Photorealistic Rendering Framework for Image Abstraction and Stylization of Low-Illuminated and Underexposed Images

2021 ◽  
Vol 11 (2) ◽  
pp. 22-45
Author(s):  
Pavan Kumar ◽  
Poornima B. ◽  
Nagendraswamy H. S. ◽  
Manjunath C.
Keyword(s):  
Structural Features ◽  
Image Features ◽  
Visual Features ◽  
Structure Preserving ◽  
Photorealistic Rendering ◽  
Assessment Techniques ◽  
Contemporary Work ◽  
Image Abstraction ◽  
Future Work ◽  
Background Content

The proposed abstraction framework manipulates the visual-features from low-illuminated and underexposed images while retaining the prominent structural, medium scale details, tonal information, and suppresses the superfluous details like noise, complexity, and irregular gradient. The significant image features are refined at every stage of the work by comprehensively integrating a series of AnshuTMO and NPR filters through rigorous experiments. The work effectively preserves the structural features in the foreground of an image and diminishes the background content of an image. Effectiveness of the work has been validated by conducting experiments on the standard datasets such as Mould, Wang, and many other interesting datasets and the obtained results are compared with similar contemporary work cited in the literature. In addition, user visual feedback and the quality assessment techniques were used to evaluate the work. Image abstraction and stylization applications, constraints, challenges, and future work in the fields of NPR domain are also envisaged in this paper.

scholarly journals Radiomics in radiation oncology—basics, methods, and limitations

2020 ◽  
Vol 196 (10) ◽  
pp. 848-855
Author(s):  
Philipp Lohmann ◽  
Khaled Bousabarah ◽  
Mauritius Hoevels ◽  
Harald Treuer
Keyword(s):  
Mathematical Models ◽  
Quantitative Imaging ◽  
Human Perception ◽  
Image Features ◽  
Radiotherapy Planning ◽  
Large Field ◽  
Imaging Features ◽  
Imaging Data ◽  
Complete Evaluation ◽  
Vast Number

Abstract Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

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 Preserved capacity for learning statistical regularities and directing selective attention after hippocampal lesions

2019 ◽  
Vol 116 (39) ◽  
pp. 19705-19710 ◽  
Author(s):  
Nuttida Rungratsameetaweemana ◽  
Larry R. Squire ◽  
John T. Serences
Keyword(s):  
Information Processing ◽  
Prior Knowledge ◽  
Human Perception ◽  
Motion Direction ◽  
Hippocampal Lesions ◽  
Perceptual Performance ◽  
Statistical Regularities ◽  
Probabilistic Structure ◽  
Sensory Evidence ◽  
Task Conditions

Prior knowledge about the probabilistic structure of visual environments is necessary to resolve ambiguous information about objects in the world. Expectations based on stimulus regularities exert a powerful influence on human perception and decision making by improving the efficiency of information processing. Another type of prior knowledge, termed top-down attention, can also improve perceptual performance by facilitating the selective processing of relevant over irrelevant information. While much is known about attention, the mechanisms that support expectations about statistical regularities are not well-understood. The hippocampus has been implicated as a key structure involved in or perhaps necessary for the learning of statistical regularities, consistent with its role in various kinds of learning and memory. Here, we tested this hypothesis using a motion discrimination task in which we manipulated the most likely direction of motion, the degree of attention afforded to the relevant stimulus, and the amount of available sensory evidence. We tested memory-impaired patients with bilateral damage to the hippocampus and compared their performance with controls. Despite a modest slowing in response initiation across all task conditions, patients performed similar to controls. Like controls, patients exhibited a tendency to respond faster and more accurately when the motion direction was more probable, the stimulus was better attended, and more sensory evidence was available. Together, these findings demonstrate a robust, hippocampus-independent capacity for learning statistical regularities in the sensory environment in order to improve information processing.

Recency Effects in Visual Memory

1981 ◽  
Vol 33 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Donald E. Broadbent ◽  
Margaret H. P. Broadbent
Keyword(s):  
Visual Memory ◽  
Short Term Memory ◽  
Visual Display ◽  
Recency Effect ◽  
General Mechanism ◽  
Visual Features ◽  
Short Term ◽  
Recency Effects ◽  
Test Items ◽  
The Subject

A number of studies were conducted, in each of which a series of abstract visual patterns was presented, and the subject was then asked to choose which of two test items was in the list. The items contained specifiable visual features, and similarity could therefore be varied in a relatively known way. As in earlier studies by other workers with randomly generated patterns, a recency effect was obtained. However, this effect did not depend on similarity between the items in the list, or between them and an intervening activity. Such factors do in some cases affect the average level of performance, but not the magnitude of recency. Nor was recency abolished by tasks interposed between presentation and test. These findings suggest a general mechanism of short-term memory, rather than a specifically sensory one. However, the recency effect did depend on the similarity of location of items in the visual display. Thus there is some evidence for a specific sensory store, with items arriving more recently over-writing those which came earlier and which were similar in location.

A Survey on Emotional Semantic Mapping in Image Retrieval

2012 ◽  
Vol 532-533 ◽  
pp. 1297-1302
Author(s):  
Zeng Rong Liu ◽  
Zhi Li ◽  
Xue Li Yu
Keyword(s):  
Decision Making ◽  
Image Retrieval ◽  
Human Perception ◽  
Semantic Gap ◽  
Visual Features ◽  
Semantic Mapping ◽  
Decision Making Process ◽  
Semantic Description ◽  
Important Challenge

Emotion plays an important role in the human perception and decision-making process. Human comprehension and perception of images is subjective, and not merely rely on lower-level visual features. Semantic gap is regarded as the most important challenge of image retrieval. In this paper, we analyzed the emotional features as well as emotional semantic description of images, which comes from the image emotional semantics retrieval framework. And also the mapping ways and means were summarized from image visual features to emotional semantics. Finally, the disadvantages of emotional semantic mapping and developing tendency were discussed.

scholarly journals Saturation-based quality assessment for colorful multi-exposure image fusion

2017 ◽  
Vol 14 (2) ◽  
pp. 172988141769462 ◽  
Author(s):  
Chenwei Deng ◽  
Zhen Li ◽  
Shuigen Wang ◽  
Xun Liu ◽  
Jiahui Dai
Keyword(s):  
Image Quality ◽  
Image Fusion ◽  
Quality Assessment ◽  
Visual Information ◽  
Image Quality Assessment ◽  
Human Perception ◽  
Image Features ◽  
Consumer Electronics ◽  
Structure Information ◽  
Learning Machine

Multi-exposure image fusion is becoming increasingly influential in enhancing the quality of experience of consumer electronics. However, until now few works have been conducted on the performance evaluation of multi-exposure image fusion, especially colorful multi-exposure image fusion. Conventional quality assessment methods for multi-exposure image fusion mainly focus on grayscale information, while ignoring the color components, which also convey vital visual information. We propose an objective method for the quality assessment of colored multi-exposure image fusion based on image saturation, together with texture and structure similarities, which are able to measure the perceived color, texture, and structure information of fused images. The final image quality is predicted using an extreme learning machine with texture, structure, and saturation similarities as image features. Experimental results for a public multi-exposure image fusion database show that the proposed model can accurately predict colored multi-exposure image fusion image quality and correlates well with human perception. Compared with state-of-the-art image quality assessment models for image fusion, the proposed metric has better evaluation performance.

scholarly journals Subjectively Measured Streetscape Perceptions to Inform Urban Design Strategies for Shanghai

2021 ◽  
Vol 10 (8) ◽  
pp. 493
Author(s):  
Waishan Qiu ◽  
Wenjing Li ◽  
Xun Liu ◽  
Xiaokai Huang
Keyword(s):  
Urban Design ◽  
Design Theory ◽  
Human Perception ◽  
Image Features ◽  
Design Strategies ◽  
Subjective Measures ◽  
Explanatory Variables ◽  
Visual Elements ◽  
Urban Amenities ◽  
Human Perceptions

Recently, many new studies applying computer vision (CV) to street view imagery (SVI) datasets to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities have emerged. However, human perception (e.g., imageability) have a subtle relationship to visual elements that cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain human behaviors more. However, the effectiveness of integrating subjective measures with SVI datasets has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected ratings from experts on sample SVIs regarding these four qualities, which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting scores. We found a strong correlation between the predicted complexity score and the density of urban amenities and services points of interest (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five urban cores that are renowned worldwide. Rather than predicting perceptual scores directly from generic image features using a convolution neural network, our approach follows what urban design theory has suggested and confirmed as various streetscape features affecting multi-dimensional human perceptions. Therefore, the results provide more interpretable and actionable implications for policymakers and city planners.

scholarly journals What to Expect When the Unexpected Becomes Expected: Harmonic Surprise and Preference Over Time in Popular Music

2021 ◽  
Vol 15 ◽  
Author(s):  
Scott A. Miles ◽  
David S. Rosen ◽  
Shaun Barry ◽  
David Grunberg ◽  
Norberto Grzywacz
Keyword(s):  
Human Brain ◽  
Human Perception ◽  
Music Preference ◽  
Specific Time ◽  
Preference Formation ◽  
Time Period ◽  
Statistical Regularities ◽  
Higher Harmonic ◽  
The Relationship ◽  
Over Time

Previous work demonstrates that music with more surprising chords tends to be perceived as more enjoyable than music with more conventional harmonic structures. In that work, harmonic surprise was computed based upon a static distribution of chords. This would assume that harmonic surprise is constant over time, and the effect of harmonic surprise on music preference is similarly static. In this study we assess that assumption and establish that the relationship between harmonic surprise (as measured according to a specific time period) and music preference is not constant as time goes on. Analyses of harmonic surprise and preference from 1958 to 1991 showed increased harmonic surprise over time, and that this increase was significantly more pronounced in preferred songs. Separate analyses showed similar increases over the years from 2000 to 2019. As such, these findings provide evidence that the human perception of tonality is influenced by exposure. Baseline harmonic expectations that were developed through listening to the music of “yesterday” are violated in the music of “today,” leading to preference. Then, once the music of “today” provides the baseline expectations for the music of “tomorrow,” more pronounced violations—and with them, higher harmonic surprise values—become associated with preference formation. We call this phenomenon the “Inflationary-Surprise Hypothesis.” Support for this hypothesis could impact the understanding of how the perception of tonality, and other statistical regularities, are developed in the human brain.

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