کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6203111 1263357 2015 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Investigating perceptual qualities of static surface appearance using real materials and displayed images
ترجمه فارسی عنوان
بررسی ویژگی های ادراکی ظاهر سطح استاتیک با استفاده از مواد واقعی و تصاویر نمایش داده شده
کلمات کلیدی
مواد، کیفیت ادراکی، درک سطحی، بازتولید تصویر،
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی سیستم های حسی
چکیده انگلیسی


- Method of representation of materials affects perceptual qualities.
- Material categories are confused when materials are represented as images.
- Qualities of materials are strongly influenced by their color information.
- Image resolution hardly influences the correlations between material classes.

Recent experimental evidence supports the idea that human observers are good at recognizing and categorizing materials. Fleming et al. reported that perceptual qualities and material classes are closely related using projected images (Journal of Vision 13(8) (2013) 9). In this paper, we further investigated their findings using real materials and degraded image versions of the same materials. We constructed a real material dataset, as well as four image datasets by varying chromaticity (color vs. gray) and resolution (high vs. low) of the material images. To investigate the fundamental properties of materials' static surface appearance, we used stimuli that lacked shape and saturated color information. We then investigated the relationship between these perceptual qualities and the various types of image representation through psychophysical experiments. Our results showed that the representation method of some materials affected their perceptual qualities. These cases could be classified into the following three types: (1) perceptual qualities decreased by reproducing the materials as images, (2) perceptual qualities decreased by creating gray images, and (3) perceptual qualities such as “Hardness” and “Coldness” tended to increase when the materials were reproduced as low-quality images. Through methods such as principal component analysis and k-means clustering, we found that material categories are more likely to be confused when materials are represented as images, especially gray images.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Vision Research - Volume 115, Part B, October 2015, Pages 246-258
نویسندگان
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