堀内 隆彦

Although a physical gloss exists as a physically measurable index, people can also perceive a perceptual gloss on object surfaces. However, the physical gloss does not always match the perceptual gloss. Because the physical gloss is calculated based only on the specular gloss and does not reflect other physical features that affect the perceptual gloss. Thus, we analysed the relationships between physical features and perceptual gloss by measuring many physical properties of object surfaces, including their physical gloss. We prepared 127 flat objects comprising three materials: paper, resin and metal plating. The perceptual gloss was visually evaluated for observation angles of 20°, 60° and 85° using a magnitude estimation method. Multiple measurements were conducted to obtain physical features such as the gloss unit (GU), haze, distinctness of image and high dynamic range luminance. We then constructed prediction models for the perceptual gloss using these physical features and multiple regression analyses. By combining these multiple physical quantities and using the GU in the power scale, the prediction accuracy was improved. By the optimal power index (0.33 for physical gloss in the common prediction model, independent of the observation angle), we found that human gloss perception may be related to brightness perception.

Background and aim: Machine learning methods are examined by many researchers to identify weeds in crop images captured by drones. However, metaheuristic optimization is rarely used in optimizing the machine learning models used in weed classification. Therefore, this research targets developing a new optimization algorithm that can be used to optimize machine learning models and ensemble models to boost the classification accuracy of weed images. Methodology: This work proposes a new approach for classifying weed and wheat images captured by a sprayer drone. The proposed approach is based on a voting classifier that consists of three base models, namely, neural networks (NNs), support vector machines (SVMs), and K-nearest neighbors (KNN). This voting classifier is optimized using a new optimization algorithm composed of a hybrid of sine cosine and grey wolf optimizers. The features used in training the voting classifier are extracted based on AlexNet through transfer learning. The significant features are selected from the extracted features using a new feature selection algorithm. Results: The accuracy, precision, recall, false positive rate, and kappa coefficient were employed to assess the performance of the proposed voting classifier. In addition, a statistical analysis is performed using the one-way analysis of variance (ANOVA), and Wilcoxon signed-rank tests to measure the stability and significance of the proposed approach. On the other hand, a sensitivity analysis is performed to study the behavior of the parameters of the proposed approach in achieving the recorded results. Experimental results confirmed the effectiveness and superiority of the proposed approach when compared to the other competing optimization methods. The achieved detection accuracy using the proposed optimized voting classifier is 97.70%, F-score is 98.60%, specificity is 95.20%, and sensitivity is 98.40%. Conclusion: The proposed approach is confirmed to achieve better classification accuracy and outperforms other competing approaches.

Objects in nature have diverse appearances, and appearance is one of the elements constituting the unique visual aspect of an object. However, previous studies have shown that when an object is represented as a digital image, its appearance can change compared to the real object depending on the material. In this study, the focus was on the “bumpiness” of the object surface, whereby a method was proposed to edit the bumpiness of the object in the image. First, the statistics obtained from images were analyzed in relation to the sensibility value of the bumpiness perceived by humans. Since the statistics on the original image could not fully explain the perception of bumpiness, analyses were conducted on multiscale images. The results suggested that the mean, standard deviation, and top, defined as the average of the luminance values within the top 10 [percentile], were highly influential as cues for bumpiness perception. The results also indicated that the components in the range of 5–65 [cycles per image-width] were highly influential. Based on these analysis results, a method is proposed to edit bumpiness perception by modulating components in the low and medium frequency bands. The effectiveness of the proposed method is demonstrated by image modulation experiments on objects of various materials.

Retroreflective materials have the property of directional reflection, reflecting light strongly in the direction of the light source, and have been used for road traffic signs. In recent years, retroreflective materials have been used in entertainment and industrial technologies, in combination with projection mapping technology. In general, color calibration is important when projectors are used to control reflected colors. In this study, we investigated a simple color calibration method for retroreflective materials with a 3D shape under the condition that they are observed in the same direction as the light source. Three types of retroreflective materials with different reflective properties were used. First, to measure the reflective properties of each reflective material, the reflective material was fixed to a flat plate and rotated, while the reflected light was measured in the same direction as the light source. It was then confirmed that the reflected light intensity varied smoothly with angular change, and appropriate measurement angles were investigated based on the AIC criterion, aiming to interpolate the reflectance characteristics from a small number of measurement angles. Color calibration was performed based on the reflectance characteristics obtained from the derived measurement angles, and the experiments showed that good color calibration was possible with fewer measurements.

This study focuses on the analysis of the cross-modal effects between sight (color) and smell (fragrance). While most previous researches have studied the harmony of color and fragrance using small-field colors such as patches and display stimuli, this study analyzes harmony in lighting environments. In our experiments, we focused on the finding that emotional states manifest themselves in responses in the orbitofrontal cortex and used near-infrared spectroscopy to evaluate orbitofrontal responses. Five different aromas were prepared for fragmentation. Initially, the observers were asked to select the most pleasant and least unpleasant aromas. The two selected aromas were soaked in non-fat cotton cloth and placed in a light-shielding brown bottle, which was used as a scent stimulus. For the lighting environment, 36 different lighting colors were designed using a luminaire consisting of 14 LEDs. The results of the experiment showed that the lighting color that the observers judged to be harmonious by actually smelling the fragrance (sensory color) activated the orbitofrontal cortex more than the lighting color that they judged to be harmonious by recalling the name of the fragrance (imagery color).

To investigate the influence of intrinsically photosensitive retinal ganglion cells (ipRGCs) on color discrimination, it is necessary to create two metameric light stimuli (metameric ipRGC stimuli) with the same amount of cone and rod stimulation, but different amounts of ipRGC stimulation. However, since the spectral sensitivity functions of cones and rods overlap with those of ipRGCs in a wavelength band, it has been difficult to independently control the amount of stimulation of ipRGCs only. In this study, we first propose a method for calculating metameric ipRGC stimulation based on the orthogonal basis functions of human photoreceptor cells. Then, we clarify the controllable range of metameric ipRGC stimulation within a color gamut. Finally, to investigate the color discrimination by metameric ipRGC stimuli, we conduct subjective evaluation experiments on 24 chromaticity coordinates using a multispectral projector. The results reveal a correlation between differences in the amount of ipRGC stimulation and differences in color appearance, indicating that ipRGCs may influence color discrimination.

This paper proposes an objective glossiness index for objects in halftone color images. In the proposed index, we consider the characteristics of the human visual system (HVS) and associate the image's structure distortion and statistical information. According to the difference in the number of strategies adopted by the HVS in judging the difference between images, it is divided into single and multi-strategy modeling. In this study, we advocate multiple strategies to determine glossy or non-glossy quality. We assumed that HVS used different visual mechanisms to evaluate glossy and non-glossy objects. For non-glossy images, the image structure dominated, so the HVS tried to use structural information to judge distortion (a strategy based on structural distortion detection). For glossy images, the glossy appearance dominated; thus, the HVS tried to search for the glossiness difference (an appearance-based strategy). Herein, we present an index for glossiness assessment that attempts to explicitly model the structural dissimilarity and appearance distortion. We used the contrast sensitivity function to account for the mechanism of halftone images when viewed by the human eye. We estimated the structure distortion for the first strategy by using local luminance and contrast masking; meanwhile, local statistics changing in the spatial frequency components for skewness and standard deviation were used to estimate the appearance distortion for the second strategy. Experimental results showed that these two mixed-distortion measurement strategies performed well in consistency with the subjective ratings of glossiness in halftone color images.

The achievement of molecular orientation control by rubbing and pressing poly(3-alkylthiophene)s is a powerful technique to improve the performance of organic electronic devices. We report here that the rubbing and pressing of blackish-brown 3-methoxythiophene oligomer powders yield layer and tablet samples with gold tones, respectively. Specular reflectivity, colorimetric, and X-ray diffraction measurements reveal that this gold tone is caused by an increase in the ratio of edge-on lamellar crystallites to face-on ones, which is promoted by rubbing/pressing. In contrast to the 3-alkylthiophene polymer, which develops a dominant face-on lamellar structure, rubbing of the 3-methoxythiophene oligomer increases the relative amount of edge-on lamellar crystallites to face-on lamellar ones. Furthermore, gold tone development in the tablet samples is limited to the near-surface area, despite the fact that pressure is also applied to the tablet bulk. These specific chemical events are explained by considering the repulsive interactions between the 3-methoxythiophene backbone and the functional groups on the surface of the substrate employed during the rubbing/pressing processes. Despite the lower applied pressure, gold tone development by rubbing is accompanied by a higher reflective property than by pressing because of the formation of larger relative amounts and sizes of edge-on lamellar crystallites, which are responsible for the gold tone.

While transmittance is a physically measurable quantity, people perceive it as the translucency of object surfaces. However, transmittance does not always match translucency. We measured the physical properties of object surfaces, including physical transmittance, and analyzed the relationship between the physical properties and translucency. We prepared 107 samples of flat objects that primarily consisted of resin for the experiment. We visually evaluated the perpetual gloss using a magnitude estimation method. We conducted multiple measurements of physical properties such as transmittance, haze, distinctness of image, and gloss unit. Then we constructed a prediction model for evaluating the perceptual gloss using the abovementioned physical properties and translucency through multiple regression analysis. As a result, the prediction accuracy is found to be improved by combining various physical quantities with simple regression using transmittance.

Intrinsically photosensitive retinal ganglion cells (ipRGCs) affect pupillary light reflex and circadian rhythm regulation. Recent studies have reported that they affect visual perception, particularly brightness perception, and their effect on color perception has also been gradually reported. In this study, we performed a color-matching task on a display device to verify the effect of ipRGC on color perception. Over a year, one participant performed 310 color matching sessions, day and night, by central and peripheral vision. Three blue colors with high ipRGC absorption and three red colors with low ipRGC absorption were used in one session. The color matching results suggest that non-image-forming and image-forming functions interact with col-or perception. We built a regression model in which ipRGC acts on the LMS. We found that the models constructed for each hue explained the experimental results well.

Although a typical display consists of red-green-blue (RGB) subpixels, displays with various subpixel layouts have been used owing to their various advantages such as luminous efficiency. The purpose of this study is to verify the effects of display MTF on subjective spatial resolution by creating new subpixel layouts. We designed BRGRB and BRGRB525 subpixel layouts with slightly higher and much higher MTF than RGB subpixel layouts, and conducted visual evaluation experiments with RGB, PenTile RGBG and the two new subpixel layouts at 20 and 30 cycles per degree. It was verified that subjective spatial resolution generally follows the large and small relationship of display MTF. Additional experiments showed that the integral of the product of the contrast sensitivity function and MTF was highly correlated with the subjective spatial resolution.

To differentiate from competitors, it has become increasingly important to design products based on the 'Kansei value,' which impresses and inspires consumers. However, the perceptual indices of products are generally designed qualitatively, which is not only time-consuming but also costly. Therefore, in recent years, studies have been conducted to discuss the relationship between some perceptions by applying multivariate analysis and machine learning to the adjectives related to perception and affective response. Nonetheless, determining the structure that expresses the relationship of the adjectives is obtained by trial and error, based on the hypothesis of the researchers. Therefore, we aimed to investigate a method for mechanically exploring some semi-optimal structures without a hypothesis of the model structure by applying a genetic algorithm to the construction of adjective relationships using structural equation modeling. In this study, we prepared a four-layered model according to the human perception process for eight categories of material samples and constructed a relationship from a total of 20 words perceived from each sample. Consequently, we could construct a perceptual model that can be interpreted quantitatively and semantically using the proposed method. The advantage of this technique is that it can be used to construct important structures that might be overlooked.

<bold>Abstract</bold> With the proliferation of smartphones and social networking services, the opportunities for individuals to take photographs have increased exponentially. In a previous study, the perceived gloss of an object was reduced by its representing as a digital image and compared with a real object. It is also known that image editing, such as lossy image compression, can reduce the glossiness of an image. Therefore, the glossiness of real objects may be easily changed in digital images; thus, a method for appropriately editing the gloss in digital images is required for post-processing. In this study, we propose a gloss appearance editing method for various material objects in a single digital image. The proposed method consists of three steps: color space conversion, gloss detection, and gloss editing. The relationship between the proposed method and the respective reflection models of inhomogeneous objects, metallic objects, and translucent objects was analyzed. Consequently, we determined that the gloss editing of the proposed method is equivalent to editing the specular reflection component of an inhomogeneous object, the grazing reflection component of a metallic object, and the specular reflection component of a translucent object. We applied the proposed method to test images including objects of various materials and confirmed its effectiveness through a subjective evaluation by visual inspection and an objective evaluation using image statistics.

Solution-cast coating films of perchlorate-doped oligo(3-methoxythiophene) exhibited a gold-like luster similar to that of metallic gold despite the involvement of no metals. However, the development mechanism of the luster remains ambiguous. To understand the mechanism, we performed scanning electron microscopic analysis, variable-angle spectral reflectance measurements, and ellipsometry measurements on ClO4−-doped oligo(3-methoxythiophene) cast film with a gold-like luster. The results revealed that the lustrous color of the film was not induced by the submicron-sized regular structures (structural color), nor by the high-density free electrons (reflective response based on Drude model), but by the large optical constants (refractive index and extinction coefficient) of the film, as speculated previously.

In this study, we investigated the physical factors required to accurately reproduce the Milky Way in star fields in a planetarium using three evaluation indices: Faithfulness, preference, and depth feeling. Psychometric experiments were conducted by manipulating three different physical factors (transmittance, representation size and star density) of the stars projected on a dome screen as experimental stimuli. The three evaluation indices were rated by observers for 12 different reproductions of the Milky Way. By analyzing the experimental results, we developed a common model to estimate the scores for each evaluation index by changing the coefficients of the three physical factors. Our proposed model has good accuracy, and each evaluation index can be represented by transmittance, representation size and star density. The weighting values indicate that density reproduction was the pivotal factor for the majority of observers. In contrast, the observers were not affected by the size of the stars in the projected Milky Way.

We propose a perception-based collation method for color halftone images using convolutional neural network (CNN). First, we acquire color difference images based on S-CIELAB between each pair of output images from different OSs or printer drivers. Then, patches are extracted from the color difference images, and a given number of patches are trained by using VGG16 for learning the results by visual comparison. We evaluate the collation accuracy of the proposed method compared with the conventional method.

In this study, we present a novel method for removing smoke from videos based on a single image sequence. Smoke is a significant artifact in images or videos because it can reduce the visibility in disaster scenes. Our proposed method for removing smoke involves two main processes: (1) the development of a smoke imaging model and (2) smoke removal using spatio-temporal pixel compensation. First, we model the optical phenomena in natural scenes including smoke, which is called a smoke imaging model. Our smoke imaging model is developed by extending conventional haze imaging models. We then remove the smoke from a video in a frame-by-frame manner based on the smoke imaging model. Next, we refine the appearance of the smoke-free video by spatio-temporal pixel compensation, where we align the smoke-free frames using the corresponding pixels. To obtain the corresponding pixels, we use SIFT and color features with distance constraints. Finally, in order to obtain a clear video, we refine the pixel values based on the spatio-temporal weightings of the corresponding pixels in the smoke-free frames. We used simulated and actual smoke videos in our validation experiments. The experimental results demonstrated that our method can obtain effective smoke removal results from dynamic scenes. We also quantitatively assessed our method based on a temporal coherence measure.

Genuine leather has numerous applications, such as car interiors and clothing, owing to its excellent quality. However, due to the high cost of genuine leather, animal rights issues, and environmental effects of leather processing, artificial leather has increasingly replaced genuine leather. The materials and processing methods used for artificial and genuine leathers are different, resulting in a variety of impressions andshitsukan, that is, the sense of quality of the object. In this study, we focused on the perceptual "authenticity," "luxury," and "quaintness," which are important components needed to achieve theshitsukanof leather used in various products and examined the quantification of qualitativeshitsukanusing a perception model. We hypothesized thatshitsukancan be recognized from the common potential impressions perceived from the properties of a material. Therefore, we developed a method for evaluatingshitsukanusing representative words for impressions that we perceive in objects and measured their image properties. The physical and psychophysical properties were obtained using several measurements. Multi-angle measuring devices were developed for the measurement experiments. Moreover, several subjective evaluation experiments were conducted to estimate the representative impressions that were potentially perceived in leather. Subsequently, we estimated two independent impressions, "surface shape" and "impression of stateliness" from the properties of leather. In addition, "authenticity," "luxury," and "quaintness," were quantified using simple equations basis the impressions.

Traditionally, the appearance of an object in an image is edited to elicit a preferred perception. However, the editing method might be arbitrary and might not consider the human perception mechanism. In this study, the authors explored image-based leather "authenticity" editing using an estimation model that considers a perception mechanism derived in their previous work. They created leather rendered images by emphasizing or suppressing image properties corresponding to the "authenticity." Subsequently, they performed two subjective experiments, one using fully edited images and another using partially edited images whose specular reflection intensity was constant. Participants observed the leather rendered images and evaluated the differences in the perception of "authenticity." The authors found that the "authenticity" perception could be changed by manipulating the intensity of specular reflection and the texture (grain and surface irregularity) in the images. The results of this study could be used to tune the properties of images to make them more appealing. (C) 2020 Society for Imaging Science and Technology.

The authors discuss the spectral estimation of multiple light sources from image data in a complex illumination environment. An approach is proposed to effectively estimate illuminant spectra and the corresponding light sources based on highlight areas that appear on dielectric object surfaces. First, the authors develop a highlight detection method using two types of convolution filters with Gaussian distributions, center-surround and low-pass filters. This method is available even for white surfaces, and it is independent of object color and of viewing and incidence angles. Second, they present an algorithm for estimating the illuminant spectra from extracted highlight areas. Each specular highlight area has a spectral composition corresponding to only one light source among multiple light sources. The spectral image data are projected onto a two-dimensional subspace, where a linear cluster in pixel distribution is detected for each highlight area. Third, the relative positional relationship between highlight areas among different object surfaces is used to identify the light sources on each surface. The authors develop an algorithm based on probabilistic relaxation labeling. The light source for each highlight and the corresponding spectral-power distribution are determined from the iterative labeling process. Finally, the feasibility of the proposed approach is examined in an experiment using a real complex environment, where dielectric objects are illuminated by multiple light sources of light-emitting diode, fluorescence, and incandescence. (C) 2020 Society for Imaging Science and Technology.

<bold>Abstract</bold> In addition to colors and shapes, factors of material appearance such as glossiness, translucency, and roughness are important for reproducing the realistic feeling of an image. In general, these perceptual qualities are often degraded when reproduced as a digital color image. The authors have aimed to edit the material appearance of an image as measured by a general camera and reproduce it on a general display device. In their previous study, the authors found that the pupil diameter decreases slightly when observing the surface properties of an object and proposed an algorithm called “PuRet” for enhancing the material appearance based on the physiological models of the pupil and retina. However, to obtain an accurate reproduction, it was necessary to manually adjust two types of adaptation parameters in PuRet as related to the retinal response for each scene and the particular characteristics of the display device. This study realizes the management of the appearance of material objects on display devices by automatically deriving the optimum parameters in PuRet from captured RAW image data. The results indicate that the authors succeeded in estimating an adaptation parameter from the median value of the scene luminance as estimated from a RAW image. They also succeeded in estimating another adaptation parameter from the average value of the scene luminance and the luminance contrast value of the output display device. As a result of an experiment using an unknown display device that was not applied to derive the estimation model, it was confirmed that the proposed model works properly.

<bold>Abstract</bold> The Random spray Retinex (RSR) algorithm was developed by taking into consideration the mathematical description of Milano-Retinex. The RSR substituted random paths with random sprays. Mimicking some characteristics of the human visual system (HVS), this article proposes two variants of RSR adding a mechanism of region of interest (ROI). In the first proposed model, a cone distribution based on anatomical data is considered as ROI. In the second model, the visual resolution depending on the visual field based on the knowledge of visual information processing is considered as ROI. We have measured actual eye movements using an eye-tracking system. By using the eye-tracking data, we have simulated the HVS using test images. Results show an interesting qualitative computation of the appearance of the processed area around real gaze points.

Products can be promoted by improving their value using shitsukan, that is, feelings or impressions of the perception of their material appearance. Particularly, products made of leather are generally attractive to consumers. Although the number of products made from artificial leather has been increasing in recent years, the impression of their appearance is different from that of products made of genuine leather. This study investigates the impression elicited by leather and proposes a model to assess people’s feeling of its authenticity. We developed a measurement system and conducted subjective evaluation experiments on two groups of participants divided according to whether they were familiar with leather. The proposed evaluation model is based on a visual perception mechanism. We first investigated the correlation between characteristics of image samples and impression factors estimated by using factor analysis. Then, we confirmed the correlation among the impression factors and values of the feeling of authenticity. The R-squared value between subjective values of the feeling of authenticity and our proposed assessment values was approximately 0.8.

<p>It is known that the colors in our surroundings affect our emotions in the real-world environment. Previous studies investigated the relationship between environmental colors and emotions. In this study, we first discuss a psychophysical experiment showing that even if the surrounding colors are equivalent, the emotions recalled might differ depending on the situation. We then set up an environment in which a specific emotional state was triggered by conversation and investigated the lighting conditions that promote that emotional state through psychophysical experiments. We found no significant effect on positive emotional states such as happiness under a single uniform lighting condition generated by a recall color. In contrast, negative emotional states such as sadness were significantly promoted under the single uniform lighting condition. An additional experiment suggests that positive emotional states could be promoted by illumination with a spatial combination of a color recalled from the emotion and its opponent color.</p>