Statistical quantification of the effects of viewing distance on texture perception

Zhang, Lipeng; Asano, Atsushi; Asano, Chie Muraki; Okajima, Katsunori

doi:10.1364/josaa.30.001394

Cited by 7 publications

(7 citation statements)

References 22 publications

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“…The potential of texture analysis has been demonstrated in numerous biomedical applications, including OCT imaging of skin [7], bladder [8], eye [9], atherosclerotic plaque [10], esophagus [11], and breast [12]. In general, texture analysis techniques can be classified into three groups: statistical technologies [3–10], spectral technologies [11, 12], and structural technologies [13]. The choice of optimal method for texture analysis may vary depending on the specific biomedical applications.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Kidney Microanatomy Changes During Ischemia-Reperfusion Process Using Texture Analysis of OCT Images

Tang

Jin

et al. 2017

IEEE Photonics J.

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kidney ischemia-reperfusion (I/R) accounts for the majority of acute kidney injury cases, whose consequences are commonly encountered after kidney transplantation. Optical coherence tomography (OCT) has been applied to image changes in kidney microanatomy and microcirculation. In this paper, we demonstrate a quantitative method for monitoring kidney status during ischemia-reperfusion process using texture properties of OCT images. This approach employs skewness to measure the distribution of en face OCT image intensities at different depths, thus allowing differentiating ischemia-reperfusion status of kidney. The skewness analysis based on quantitative intensity shows promise for monitoring kidney status during ischemia-reperfusion, and the potential for evaluating the viability of transplant kidney.

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Section: Introductionmentioning

confidence: 99%

Monitoring Kidney Microanatomy Changes During Ischemia-Reperfusion Process Using Texture Analysis of OCT Images

Tang

Jin

et al. 2017

IEEE Photonics J.

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“…Dulaney and Marks [16] reported that global interference decreases with training, whereas local interference develops with training. Li et al [17] noted that prior knowledge and viewing distance also affect global precedence in humans in tasks involving similarity selection.…”

Section: Introductionmentioning

confidence: 99%

“…Viewers are attracted to local features when viewing images from a short distance and to global features when viewing images from a long distance. In [17], a logistic-regression model was built to construct a statistical relationship between viewing distance and visual perception of global/local features. In this research, viewing distances in the visual-similarity experiment were determined according to the distance equation proposed in [17].…”

Section: Introductionmentioning

confidence: 99%

“…In [17], a logistic-regression model was built to construct a statistical relationship between viewing distance and visual perception of global/local features. In this research, viewing distances in the visual-similarity experiment were determined according to the distance equation proposed in [17]. We then set different viewing distances to ensure the equal probability of similarity selection in the experiment.…”

Section: Introductionmentioning

confidence: 99%

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Influences of Global and Local Features on Eye-Movement Patterns in Visual-Similarity Perception of Synthesized Texture Images

Guo

Asano

et al. 2020

Applied Sciences

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Global and local features are essential for visual-similarity texture perception. Therefore, understanding how people allocate their visual attention when viewing textures with global or local similarity is important. In this work, we investigate the influences of global and local features of a texture on eye-movement patterns and analyze the relationship between eye-movement patterns and visual-similarity selection. First, we synthesized textures by separately controlling global and local textural features through the primitive, grain, and point configuration (PGPC) texture model, a mathematical morphology-based texture model. Second, we conducted an experiment to acquire eye-movement data where participants identified the texture that was highly similar to the standard texture. Experiment data were obtained through an eye-tracker from 60 participants. The collected eye-tracking data were analyzed in terms of three metrics, including total fixation duration in each region of interest (ROI), fixation-point variance in each ROI, and fixation-transfer counts between different ROIs. Analysis results indicated the following. (1) The global and local features of a texture influenced eye-movement patterns. In particular, the texture image that was globally similar to the standard texture contained dispersed fixation points. By contrast, the texture image that was locally similar to the standard texture contained concentrated fixation points. The domination of global and local features influenced the viewers’ similarity choice. (2) The final visual-similarity selection was related to the fixation-transfer count between different ROIs, but not to the fixation time in each ROI. This research also extends the applicability of the mathematical morphology-based texture model to human visual perception.

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“…In this study, we constructed a VR system and applied it to investigate the visual complexity perceptions of texture images in 2D and 3D conditions. One of the reasons we chose textures as the stimuli in this study is that the properties of textures are more accessible and easier to manipulate than natural images [35]. Moreover, the visual perception of textures can be represented in a lower feature space.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Depth Information on Visual Complexity Perception in Three-Dimensional Textures

2020

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Visual complexity, as an attribute of images related to human perception, has been widely studied in computer science and psychology. In conventional studies, the research objects have been limited to the traditional two-dimensional (2D) patterns or images. Therefore, if depth information is introduced into this scenario, how will it affect our perception of visual complexity of an image? To answer this question, we developed an experimental virtual reality system that enables control and display of three-dimensional (3D) visual stimuli. In this study, we aimed to investigate the effect of depth information on visual complexity perception by comparing 2D and 3D displays of the same stimuli. We scanned three textures with different characteristics to create the experimental stimuli and recruited 25 participants for the experiment. The results showed that depth information significantly increased the visual complexity perception of the texture images. Moreover, depth information had different degrees of impact on visual complexity for different textures. The higher the maximum depth introduced in the 3D image, the more significant the increase in visual complexity perception. The experimental virtual reality system used in this study also provides a feasible experimental tool for future experiments.

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Statistical quantification of the effects of viewing distance on texture perception

Cited by 7 publications

References 22 publications

Monitoring Kidney Microanatomy Changes During Ischemia-Reperfusion Process Using Texture Analysis of OCT Images

Monitoring Kidney Microanatomy Changes During Ischemia-Reperfusion Process Using Texture Analysis of OCT Images

Influences of Global and Local Features on Eye-Movement Patterns in Visual-Similarity Perception of Synthesized Texture Images

The Effect of Depth Information on Visual Complexity Perception in Three-Dimensional Textures

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