scholarly journals Color constancy based on the geometry of color distribution

2020 ◽  
Author(s):  
Takuma Morimoto ◽  
Takahiro Kusuyama ◽  
Kazuho Fukuda ◽  
Keiji Uchikawa
Keyword(s):  
Visual System ◽  
Computational Modeling ◽  
Color Constancy ◽  
Test Field ◽  
Color Gamut ◽  
Surface Color ◽  
Color Distribution ◽  
Proposed Model ◽  
Specific Shape

AbstractA white surface appears white under different lighting environments. This ability is referred to color constancy. The physical inputs to our visual system are dictated by the interplay between lights and surfaces, and thus for the surface color to be stably perceived, the illuminant influence needs to be discounted. To reveal our strategy to infer the illuminant color, we conducted three psychophysical experiments designed to test optimal color hypothesis: we internalize the physical color gamut under a particular illuminant and apply the prior to estimate the illuminant color. In each experiment, we presented 61 hexagons arranged without spatial gaps, where the surrounding 60 hexagons were set to have a specific shape in their color distribution. We asked participants to adjust the color of a center test field so that it appears a full-white surface placed under a test illuminant. Results and computational modeling suggested that although our proposed model is limited in accounting for estimation of illuminant intensity by human observers, it agrees fairly well with the estimates of illuminant chromaticity in most tested conditions. The accuracy of estimation generally outperformed other tested conventional color constancy models. These results support the hypothesis that our visual system can utilize the geometry of scene color distribution to achieve color constancy.

scholarly journals Luminosity thresholds of colored surfaces are determined by their heuristic upper-limit luminances in the visual system

2021 ◽  
Author(s):  
Takuma Morimoto ◽  
Ai Numata ◽  
Kazuho Fukuda ◽  
Keiji Uchikawa
Keyword(s):  
Visual System ◽  
Luminance Level ◽  
Natural Environments ◽  
Test Field ◽  
Maximum Luminance ◽  
Color Distribution ◽  
Upper Limit ◽  
Circular Test ◽  
Real Objects ◽  
Upper Boundary

Some objects in the real world themselves emit a light, and we typically have a fairly good idea as to whether a given object is self-luminous or illuminated by a light source. However, it is not well understood how our visual system makes this judgement. This study aimed to identify determinants of luminosity threshold, a luminance level at which the surface begins to appear self-luminous. We specifically tested a hypothesis that our visual system knows a maximum luminance level that a surface can reach under the physical constraint that surface cannot reflect more lights that incident lights and apply this prior to determine the luminosity thresholds. Observers were presented a 2-degree circular test field surrounded by numerous overlapping color circles, and luminosity thresholds were measured as a function of (i) the chromaticity of the test field, (ii) the shape of surrounding color distribution and (iii) the color of illuminant lighting surrounding colors. We found that the luminosity thresholds strongly depended on test chromaticity and peaked around the chromaticity of test illuminants and decreased as the purity of the test chromaticity increased. However, the locus of luminosity thresholds over chromaticities were nearly invariant regardless of the shape of surrounding color distribution and generally well resembled the locus drawn from theoretical upper-limit luminance but also the locus drawn from the upper boundary of real objects. These trends were particularly evident for test illuminants on blue-yellow axis and curiously did not hold under atypical illuminants such as magenta or green. Based on these results, we propose a theory that our visual system empirically internalizes the gamut of surface colors under illuminants typically found in natural environments and a given surface appears self-luminous when its luminance exceeds this heuristic upper-limit luminance.

scholarly journals Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment

2021 ◽  
Author(s):  
Wen-Han Zhu ◽  
Wei Sun ◽  
Xiong-Kuo Min ◽  
Guang-Tao Zhai ◽  
Xiao-Kang Yang
Keyword(s):  
Information Processing ◽  
Image Quality ◽  
Visual System ◽  
Computational Modeling ◽  
Quality Assessment ◽  
Human Visual System ◽  
Visual Communication ◽  
Visual Experience ◽  
Image Quality Assessment ◽  
Model Based

AbstractObjective image quality assessment (IQA) plays an important role in various visual communication systems, which can automatically and efficiently predict the perceived quality of images. The human eye is the ultimate evaluator for visual experience, thus the modeling of human visual system (HVS) is a core issue for objective IQA and visual experience optimization. The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively, while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity. For bridging the gap between signal distortion and visual experience, in this paper, we propose a novel perceptual no-reference (NR) IQA algorithm based on structural computational modeling of HVS. According to the mechanism of the human brain, we divide the visual signal processing into a low-level visual layer, a middle-level visual layer and a high-level visual layer, which conduct pixel information processing, primitive information processing and global image information processing, respectively. The natural scene statistics (NSS) based features, deep features and free-energy based features are extracted from these three layers. The support vector regression (SVR) is employed to aggregate features to the final quality prediction. Extensive experimental comparisons on three widely used benchmark IQA databases (LIVE, CSIQ and TID2013) demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.

Voice And Stream: Perceptual And Computational Modeling Of Voice Separation

2008 ◽  
Vol 26 (1) ◽  
pp. 75-94 ◽  
Author(s):  
Emilios Cambouropoulos
Keyword(s):  
Computational Modeling ◽  
Specific Problem ◽  
Cognitive Task ◽  
Ground Truth ◽  
Musical Score ◽  
Auditory Stream ◽  
Multiple Voices ◽  
Proposed Model ◽  
Single Voice ◽  
Small Dataset

LISTENERS ARE THOUGHT TO BE CAPABLE of perceiving multiple voices in music. This paper presents different views of what 'voice' means and how the problem of voice separation can be systematically described, with a view to understanding the problem better and developing a systematic description of the cognitive task of segregating voices in music. Well-established perceptual principles of auditory streaming are examined and then tailored to the more specific problem of voice separation in timbrally undifferentiated music. Adopting a perceptual view of musical voice, a computational prototype is developed that splits a musical score (symbolic musical data) into different voices. A single 'voice' may consist of one or more synchronous notes that are perceived as belonging to the same auditory stream. The proposed model is tested against a small dataset that acts as ground truth. The results support the theoretical viewpoint adopted in the paper.

scholarly journals The neural basis of lightness and color constancy in the visual system

2011 ◽  
Vol 11 (11) ◽  
pp. 351-351
Author(s):  
P. J. Kohler ◽  
S. V. Fogelson ◽  
E. A. Reavis ◽  
P. U. Tse
Keyword(s):  
Visual System ◽  
Color Constancy ◽  
Neural Basis

Spatial phenomenology requires potential illumination

2003 ◽  
Vol 26 (4) ◽  
pp. 425-426
Author(s):  
James A. Schirillo
Keyword(s):  
Visual System ◽  
Spatial Model ◽  
Dimensional Space ◽  
Color Constancy ◽  
Three Dimensional ◽  
Two Dimensions ◽  
Three Dimensional Space

Collapsing three-dimensional space into two violates Lehar's “volumetric mapping” constraint and can cause the visual system to construct illusory transparent regions to replace voxels that would have contained illumination. This may underlie why color constancy is worse in two dimensions, and argues for Lehar to revise his phenomenal spatial model by putting “potential illumination” in empty space.

Lightness Constancy: A Direct Test of the Illumination-Estimation Hypothesis

2002 ◽  
Vol 13 (2) ◽  
pp. 142-149 ◽  
Author(s):  
M.D. Rutherford ◽  
D.H. Brainard
Keyword(s):  
Visual System ◽  
Color Constancy ◽  
The Other ◽  
Color Appearance ◽  
Direct Test ◽  
Illumination Estimation ◽  
Test Patch ◽  
Lightness Constancy ◽  
Surface Lightness

Many models of color constancy assume that the visual system estimates the scene illuminant and uses this estimate to determine an object's color appearance. A version of this illumination-estimation hypothesis, in which the illuminant estimate is associated with the explicitly perceived illuminant, was tested. Observers made appearance matches between two experimental chambers. Observers adjusted the illumination in one chamber to match that in the other and then adjusted a test patch in one chamber to match the surface lightness of a patch in the other. The illumination-estimation hypothesis, as formulated here, predicted that after both matches the luminances of the light reflected from the test patches would be identical. The data contradict this prediction. A second experiment showed that manipulating the immediate surround of a test patch can affect perceived lightness without affecting perceived illumination. This finding also falsifies the illumination-estimation hypothesis.

Minimum-variance cone-excitation ratios and the limits of relational color constancy

2004 ◽  
Vol 21 (3) ◽  
pp. 337-340 ◽  
Author(s):  
SÉRGIO M.C. NASCIMENTO ◽  
VASCO M.N. de ALMEIDA ◽  
PAULO T. FIADEIRO ◽  
DAVID H. FOSTER
Keyword(s):  
Color Constancy ◽  
Three Dimensional ◽  
Spectral Composition ◽  
Minimum Variance ◽  
Physical Basis ◽  
Test Surface ◽  
Two Dimensional ◽  
Surface Color

Relational color constancy refers to the constancy of the perceived relations between the colors of surfaces of a scene under changes in the spectral composition of the illuminant. Spatial ratios of cone excitations provide a natural physical basis for this constancy, as, on average, they are almost invariant under illuminant changes for large collections of natural surfaces and illuminants. The aim of the present work was to determine, computationally, for specific surfaces and illuminants, the constancy limits obtained by the application of a minimum-variance principle to cone-excitation ratios and to investigate its validity in predicting observers' surface-color judgments. Cone excitations and their changes due to variations in the color of the illuminant were estimated for colored surfaces in simulated two-dimensional scenes of colored papers and real three-dimensional scenes of solid colored objects. For various test surfaces, scenes, and illuminants, the estimated levels of relational color constancy mediated by cone-excitation ratios varied significantly with the test surface and only with certain desaturated surfaces corresponded to ideal matches. Observers' experimental matches were compared with predictions expressed in CIE 1976 (u′,v′) space and were found to be generally consistent with minimum-variance predictions.

scholarly journals Surface color perception in three-dimensional scenes

2006 ◽  
Vol 23 (3-4) ◽  
pp. 311-321 ◽  
Author(s):  
HUSEYIN BOYACI ◽  
KATJA DOERSCHNER ◽  
JACQUELINE L. SNYDER ◽  
LAURENCE T. MALONEY
Keyword(s):  
Visual System ◽  
Recent Work ◽  
Human Visual System ◽  
Light Field ◽  
Three Dimensional ◽  
Color Perception ◽  
Line Of Sight ◽  
Surface Color ◽  
Matte Surface ◽  
Surface Lightness

Researchers studying surface color perception have typically used stimuli that consist of a small number of matte patches (real or simulated) embedded in a plane perpendicular to the line of sight (a “Mondrian,” Land & McCann, 1971). Reliable estimation of the color of a matte surface is a difficult if not impossible computational problem in such limited scenes (Maloney, 1999). In more realistic, three-dimensional scenes the difficulty of the problem increases, in part, because the effective illumination incident on the surface (the light field) now depends on surface orientation and location. We review recent work in multiple laboratories that examines (1) the degree to which the human visual system discounts the light field in judging matte surface lightness and color and (2) what illuminant cues the visual system uses in estimating the flow of light in a scene.

scholarly journals The Memory for Latent Representations: An Account of Working Memory that Builds on Visual Knowledge for Efficient and Detailed Visual Representations

2021 ◽  
Author(s):  
Shekoofeh Hedayati ◽  
Ryan O’Donnell ◽  
Brad Wyble
Keyword(s):  
Working Memory ◽  
Visual System ◽  
Critical Role ◽  
Mechanistic Explanation ◽  
Shape Information ◽  
Visual Knowledge ◽  
Proposed Model ◽  
Categorical Information ◽  
Latent Representations ◽  
Efficient Memory

AbstractVisual knowledge obtained from our lifelong experience of the world plays a critical role in our ability to build short-term memories. We propose a mechanistic explanation of how working memories are built from the latent representations of visual knowledge and can then be reconstructed. The proposed model, Memory for Latent Representations (MLR), features a variational autoencoder with an architecture that corresponds broadly to the human visual system and an activation-based binding pool of neurons that binds items' attributes to tokenized representations. The simulation results revealed that shape information for stimuli that the model was trained on, can be encoded and retrieved efficiently from latents in higher levels of the visual hierarchy. On the other hand, novel patterns that are completely outside the training set can be stored from a single exposure using only latents from early layers of the visual system. Moreover, a given stimulus in working memory can have multiple codes, representing specific visual features such as shape or color, in addition to categorical information. Finally, we validated our model by testing a series of predictions against behavioral results acquired from WM tasks. The model provides a compelling demonstration of visual knowledge yielding the formation of compact visual representation for efficient memory encoding.

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