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Image enhancement is a basic requirement for any computer vision application for further processing of an image. A common limitation with most of the existing methods, when applied to nearly invisible images, is the loss of color details during the enhancement process. So, a fuzzy c-means clustering-based method for image enhancement is proposed which enhances the perceptually invisible image along with preserving its color and naturalness. In this method, the image pixels are grouped into different clusters and are assigned membership values to those clusters. Based on this membership value, its intensity level is modified in the spatial domain. Modification of the gray levels proportional to the membership values leads to the stretching of the image histogram, similar in shape, to the original histogram. The process results in a very small shift in the mean intensity which preserves the color and brightness-related information of the image. The method enhances the image contrast and maintains the naturalness without introducing any artifacts. The simulation results on standard datasets reflect that the proposed algorithm is superior to many state-of-the-art and traditional methods for perceptually invisible images.

Composite ghost is a common phenomenon that widely exists in dynamic scene image mosaic and significantly affects the naturalness of mosaic. To remove the ghost effectively and produce visually natural mosaic, we propose a novel image mosaic method by jointly identifying composite ghost and eliminating ghost regions without distorting, splitting, and duplicating objects. Specifically, our main contributions are three-fold: First, we propose the motion-aware composite ghost identification to localize the potential composite ghosts in the mosaic region (i.e, overlapping area between two images to be stitched) by detecting the salient-moving objects in two stitched images. Second, we design the object-aware alternative region selection strategy to produce ghostless regions that can replace the localized composite ghosts while avoiding object distortion, object separation, and object repetition. Third, we realize the image interpolation-based composite ghost elimination that can generate natural stitched image by eliminating the composite ghost of the initial blending result with the selected image source. We validate the proposed method on challenging datasets and show that our method outperform the state-of-the-art methods.

With intrinsic non-uniform illumination variations in laparoscopic images, such images often suffer from insufficient lighting and low visibility. This in turn may cause incorrect targeting, surgical risk, and extended operating time during laparoscopic surgery. Although various methods for nature image enhancement have been proposed in past decades, surgical laparoscopic image enhancement still needs improving due to issues including naturalness, blurred texture, color cast, and extensive computational time. To address these problems, this paper proposes a distributed Retinex optimization method for laparoscopic image enhancement using refined information fusion. The proposed method minimizes a distributed optimization model with l 2 -norm regularization terms, resulting in a low computational complexity. By integrating the refined image information into the optimization process, our method significantly enhances dark regions while preserving naturalness and texture structures. Experimental results show that our optimization algorithm is more effective than conventional enhancement algorithms in terms of vision augmentation, performance index, and computation time.

Several image recoloring methods have been proposed to compensate for the loss of contrast caused by color vision deficiency (CVD). However, these methods only work for dichromacy (a case in which one of the three types of cone cells loses its function completely), while the majority of CVD is anomalous trichromacy (another case in which one of the three types of cone cells partially loses its function). In this paper, a novel degree-adaptable recoloring algorithm is presented, which recolors images by minimizing an objective function constrained by contrast enhancement and naturalness preservation. To assess the effectiveness of the proposed method, a quantitative evaluation using common metrics and subjective studies involving 14 volunteers with varying degrees of CVD are conducted. The results of the evaluation experiment show that the proposed personalized recoloring method outperforms the state-of-the-art methods, achieving desirable contrast enhancement adapted to different degrees of CVD while preserving naturalness as much as possible.

Assessing the impact of dysarthria on a patient's ability to communicate should be an integral part of patient management. However, due to the high demands on reliable quantification of communication limitations, hardly any formal clinical tests with approved psychometric properties have been developed so far. This study investigates a web-based assessment of communication impairment in dysarthria, named KommPaS. The test comprises measures of intelligibility, naturalness, perceived listener effort and communication efficiency, as well as a total score that integrates these parameters. The approach is characterized by a quasi-random access to a large inventory of test materials and to a large group of naive listeners, recruited via crowdsourcing. As part of a larger research program to establish the clinical applicability of this new approach, the present paper focuses on two psychometric issues, namely specificity and sensitivity (study 1) and retest-reliability (study 2). Study 1: KommPaS was administered to 54 healthy adults and 100 adult persons with dysarthria (PWD). Non-parametric criterion-based norms (specificity: 0.95) were used to derive a standard metric for each of the four component variables, and corresponding sensitivity values for the presence of dysarthria were identified. Overall classification accuracy of the total score was determined using a ROC analysis. The resulting cutscores showed a high accuracy in the separation of PWD from healthy speakers for the naturalness and the total score. Study 2: A sub-group of 20 PWD enrolled in study 1 were administered a second KommPaS examination. ICC analyses revealed good to excellent retest reliabilities for all parameters.

When considering the human skeleton as a hierarchical structure (i.e. an upside-down tree structure), human motion can be treated as the rotation movements of joints around their parent joints, such that a motion sequence can be represented by the joint angle changing processes (referred to as motion patterns) of the joints between its start and end frames. With this motion representation mode, we convert motion synthesis to a motion pattern generation task, given a start-end frame pair. Conforming to such a motion synthesis scheme, we propose a coupled space learning model within which we enforce the sparse representation of the start-end frame pair on the motion frame dictionary to reconstruct its corresponding motion patterns well with a motion pattern dictionary. In addition, as motion naturalness is an important evaluation metric of motions, we propose to use the likelihood of a motion with a pre-estimated motion probability distribution to measure its naturalness and add the motion naturalness measurement into the coupled space learning model to constrain the generated motion patterns to reconstruct highly natural motions. The superior performances of both accuracy and naturalness of the synthesized motions demonstrate the effectiveness of the coupled space learning with the motion likelihood constraint.

Fan subtitle or fansub is assumed to be substandard in terms of translation accuracy and naturalness. However, it is not truly known whether fansub translation is really poor in quality or that it can be accurate and natural. This qualitative research analyzes the naturalness and accuracy found in the fansub of an episode of a TV series “Supernatural”, in which the utterances of Dean and Sam Winchester are analyzed using Newmark’s theories on naturalness and accuracy in translation. The translation can be classified into several different categories, which are natural and accurate, unnatural and inaccurate, unnatural but accurate, and could be more natural but accurate. The findings of the research showed that the overall translation cannot be said as completely natural and accurate because it still contains inaccuracies and unnaturalness in its translation, but doesnot significantly change the original messages of the text.Keywords: Translation, Accuracy, Naturalness, Fansub, Supernatural

Voice user interfaces (VUIs) have exploded in popularity over the past 3 years. However, there has been little research on the reply methods that VUIs can adopt to communicate with people. In this paper, we designed 2 studies with 20 participants to explore the influence of reply methods on user experience in 2 kinds of scenarios (applicational scenarios and giving a command) when using a VUI. We explored the performance of different reply methods (fact-only, rational, and emotional) at different times and in different scenarios. In addition, we examined whether there were gender differences when evaluating a reply and different preferences for different reply methods. A “Wizard of Oz” method was used in the experiments to simulate real scenarios for communication between the participants and the VUI. We divided a reply into three parts (fact + judgment + strategy) and constructed three kinds of reply methods. In the experiments, we used quantitative scoring (five aspects: affection, confidence, naturalness, social distance, and satisfaction), preference selection and an interview to measure the participants’ user experience. The results indicated that the participants were inclined to prefer the reply methods (rational and emotional) that offered judgments and strategies in our experiment script, and the emotional style received the highest evaluation. In addition, we found that male participants tended to have a higher evaluation of VUIs’ replies for all three reply methods in applicational scenarios and when giving a command than female participants in our studies. In general, these results may contribute to the design of VUI replies.

We take an institutional perspective to examine how innovation thrives under conditions of resistance. Specifically, we conceive illicit innovation as a process of successive institutionalization of a new practice in the face of contrary law. In the German federal state of Bavaria, the global movement of craft-beer brewing collides with a regional jurisdiction that prohibits precisely these brewing practices and instead protects the traditional institution of purity-brewing (Reinheitsgebot). Grounded on an embedded qualitative case study of brewers and industry representatives, we build a theory of institutional folding of new norms and practices over established ones. This way, creative brewers have succeeded in legitimizing new practices of naturalness-brewing (Natürlichkeitsgebot). Whereas the legal resistance has stimulated brewers to create an original counter-institution, the illicit innovation has also begun to change the institutional context of the beer industry in Bavaria.

Many hardware and software advancements have been made to improve image quality in smartphones, but unsuitable lighting conditions are still a significant impediment to image quality. To counter this problem, we present an image enhancement pipeline comprising synthetic multi-image exposure fusion and contrast enhancement robust to different lighting conditions. In this paper, we propose a novel technique of generating synthetic multi-exposure images by applying gamma correction to an input image using different values according to its luminosity for generating multiple intermediate images, which are then transformed into a final synthetic image by applying contrast enhancement. We observed that our proposed contrast enhancement technique focuses on specific regions of an image resulting in varying exposure, colors, and details for generating synthetic images. Visual and statistical analysis shows that our method performs better in various lighting scenarios and achieves better statistical naturalness and discrete entropy scores than state-of-the-art methods.

Taking advantage of the naturalness hypothesis for code, recent development, and research has focused on applying machine learning (ML) techniques originally developed for natural language processing (NLP) to drive a new wave of tools and applications aimed specifically for software engineering (SE) tasks. This drive to apply ML and deep learning (DL) has been animated by the large-scale availability of software development data (e.g., source code, code comments, code review comments, commit data, and so on) available from open source platforms such as GitHub and Bitbucket.

Recent research has discovered new applications for object tracking and identification by simulating the colour distribution of a homogeneous region. The colour distribution of an object is resilient when it is subjected to partial occlusion, scaling, and distortion. When rotated in depth, it may remain relatively stable in other applications. The challenging task in image recoloring is the identification of the dichromatic color appearance, which is remaining as a significant requirement in many recoloring imaging sectors. This research study provides three different vision descriptions for image recoloring methods, each with its own unique twist. The descriptions of protanopia, deuteranopia, and tritanopia may be incorporated and evaluated using parametric, machine learning, and reinforcement learning techniques, among others. Through the use of different image recoloring techniques, it has been shown that the supervised learning method outperforms other conventional methods based on performance measures such as naturalness index and feature similarity index (FSIM).

Across varying marketplace contexts (e.g., grocery stores, restaurants, e-commerce) managers display products with and without packaging, seemingly arbitrarily. Does displaying a product packaged as opposed to unpackaged influence consumers’ product responses? Six controlled experiments and an Instagram study address this question. We focus primarily on food products but show our results extend to non-food products that are natural (i.e., originate from plants, animals, or humans). We propose that, in addition to its physical function, packaging acts as a symbolic barrier that separates the product from nature, decreasing perceived product naturalness and leading to less favorable product responses. Consistent with our theorizing, the negative effects of packaging attenuate when product information or retail signage highlights the product’s connection to nature and are contingent on the importance of product naturalness. Our findings have implications for strategic use of packaging in physical and digital merchandising and sustainability initiatives aimed at reducing packaging. Supplementary Information The online version contains supplementary material available at 10.1007/s11747-021-00800-3.

This research proposes a novel approach to improve the performance of the very deep super-resolution (VDSR) method in single image super-resolution (SISR). In the literature, VDSR is one of the most popular CNN-based super-resolution approaches and this study aims to improve the performance of the VDSR network structure's output by using the natural image quality evaluator (NIQE). New coefficients have been proposed to improve performance when combining the residual image obtained from the VDSR method and the original low-resolution image. The NIQE parameter is defined as an optimization parameter with these proposed coefficients, and it is aimed to be minimized in each SISR application. The particle swarm optimization method was used to determine the optimal coefficients for improving SISR output in this work. In the last stage, the HR image was obtained by equalizing the histograms of the low-resolution image with the image output that was optimized according to the NIQE parameter. The outputs of the proposed approach were compared with other SISR methods in the literature. It has been observed that effective use of NIQE assessment, which is one of the no-reference image quality assessment (NR-IQA) methods, increases the output image quality in SR application.

Background To date, the process of adaptation in the setting of aesthetic medicine has not been investigated. The combination of complex advanced feedback in the current intense social media milieu, in conjunction with easily accessible and effective aesthetic treatments, has produced pockets of overtreated patients and over-zealous practitioners. We examine whether aesthetic assessments of attractiveness and what appears natural can be distorted by the cognitive process of adaptation. Methods Forty-eight female participants were exposed to photographs of female faces in whom lip fullness had been strongly under- or over-exaggerated, while remaining within the bounds of natural appearing lips. Before and after evaluation of the exaggerated images, participants were asked to rate an alternative set of faces in terms of attractiveness (reflecting direct assessment of effective beauty impression) and naturalness (reflecting indirect assessment of beauty norms). The evaluation set consisted of six base faces that had been digitally altered to create a systematically varying 11 step set of lip sizes from extremely thin, to the original version, to very full. Results Second-order polynomial fits indicated clear shifts of the subjects' facial aesthetic assessments towards the specific lip fullness of the adaptors. In contrast, such adaptions were not found for ratings of face naturalness. In contrast to research demonstrating mathematical foundations and unchanging rules governing perceptions of beauty, we show that simple viewing of exaggerated feature morphologies can rapidly result in recalibration of a person's assessment of attractiveness. Conclusion This paper provides evidence that facial attractiveness is fluid, and that there are psychological mechanisms that cause an aesthetic bias. Over-exposure to exaggerated features can lead to significant changes to a person's ideas of attractiveness.

What makes a colored image, e.g. an abstract painting or a landscape, look pleasing? We hypothesized that a preference for complex color compositions, such as paintings and images of natural scenes, might be related to how natural the colors are perceived. We tested this possibility with two experiments in which the degree of naturalness of images was manipulated by rotating their color gamut rigidly in the color space CIELAB. This changed just the hue composition, but preserved saturation and lightness. In the first experiment we obtained individual scaling curves for perceived naturalness and for preference as a function of the angle of gamut rotation for a small set of images. The naturalness and preference scaling curves were found to be largely similar and their maxima were close to the original image. In the second experiment, we tested whether this effect generalized to a larger set of images. We used a simultaneous 5AFC procedure where in each trial participants had to select the most natural or the most preferred image from five different rotations of the color gamut. The results confirmed the first experiment and showed that, in general, the images perceived as the more natural tend to be the ones that are preferred. Together these results show that perceived naturalness and preference are indeed perceptually closely related and may be driven by related mechanisms.

People with colour vision deficiency (CVD) may have difficulty recognising or discriminating colours. To enhance their visual details, some methods have been developed to enhance colour contrast. Recently, researchers realized the importance of preserving naturalness in the processed images to avoid decreasing the quality of vision (QOV) and state-of-the-art image recolouring methods succeeded in realising both contrast enhancement and naturalness preservation by utilising optimisation model. However, the high computational cost of these methods prevents their wide application in daily lives. Moreover, these methods cannot automatically resolve the trade-off between contrast enhancement and naturalness preservation. This paper proposes Optimized Dichromacy Projection (ODP) as a novel high-speed image recolouring technique for dichromacy compensation. ODP achieves both contrast enhancement and naturalness preservation by computing an optimized projection from the colour gamut of normal vision to the colour gamut of dichromacy and can automatically adapt the weight of contrast enhancement and naturalness preservation to the property of individual images. Qualitative, quantitative, and subjective evaluation experiments show that the proposed method can generate images of qualities competitive to the state-of-the-art methods while drastically improving computation time.

This research is aimed at improving community environment where Chinese children engage themselves in daily play, communication, learning, fitness and other activities. At present, the natural environment that is truly suitable for children’s activities is lacking in Chinese communities, and there is no clear construction standard. In view of this, this research adopts the literature research method to study the relevant literature in South Korea, China and other countries, and integrates the child-friendly city action plan, the nature education theory for young children and the construction standard of Korean forest kindergarten. Through comparative analysis, this research attempts to unfold the current situation and existing problems of the natural environment for children"s activities in Chinese communities and reveal the analytical indexes suitable for community construction, so as to provide a basis for the construction of the Chinese community environment. The research findings and content summary are as follows: First, in terms of recreation, the environment shall satisfy young children with fun activities, provide rich construction areas, and set up facilities for young children to enjoy adventurous climbing or water. Second, in terms of naturalness, a quiet and diverse ecological environment shall be created, and natural materials as well as natural elements can be used as much as possible, besides, local materials shall be adopted in order to save energy. Third, in terms of education, an artistic and participatory environment shall be created to help children improve their aesthetic ability, and meanwhile help them experience the magic of life and establish a correct outlook on life. Fourth, in terms of sociality, space for children to interact with others shall be provided to help them quickly adapt to the environment and get a sense of belonging. Fifth, in terms of functionality, the characteristics of children"s physical and mental development shall be fully considered and the environment must be added with fun and diversity. Sixth, in terms of safety, maintaining hygienic environment, using environment-friendly materials, regularly inspecting and maintaining facilities, setting up barrier-free passages and facilities to cope with emergencies can add up to give children a sense of physical and psychological safety. It is hoped that the 6 elements, namely, recreation, naturalness, education, sociality, functionality, and safety, together with the 36 analysis indexes, will provide guidance and suggestions for the construction of the natural environment for children in Chinese communities.

Background Processed foods are typically praised/revered for their convenience, palatability, and novelty; however, their healthfulness has increasingly come under scrutiny. Classification systems that categorise foods according to their “level of processing” have been used to predict diet quality and health outcomes and inform dietary guidelines and product development. However, the classification criteria used are ambiguous, inconsistent and often give less weight to existing scientific evidence on nutrition and food processing effects; critical analysis of these criteria creates conflict amongst researchers. Scope and approach We examine the underlying basis of food classification systems and provide a critical analysis of their purpose, scientific basis, and distinguishing features by thematic analysis of the category definitions. Key findings and conclusions These classification systems were mostly created to study the relationship between industrial products and health. There is no consensus on what factors determine the level of food processing. We identified four defining themes underlying the classification systems: 1. Extent of change (from natural state); 2. Nature of change (properties, adding ingredients); 3. Place of processing (where/by whom); and 4. Purpose of processing (why, essential/cosmetic). The classification systems embody socio-cultural elements and subjective terms, including home cooking and naturalness. Hence, “processing” is a chaotic conception, not only concerned with technical processes. Most classification systems do not include quantitative measures but, instead, imply correlation between “processing” and nutrition. The concept of “whole food” and the role of the food matrix in relation to healthy diets needs further clarification; the risk assessment/management of food additives also needs debate.

When adding a photo onto a graphic design, professional graphic designers often adjust its colors based on some target colors obtained from the brand or product to make the entire design more memorable to audiences and establish a consistent brand identity. However, adjusting the colors of a photo in the context of a graphic design is a difficult task, with two major challenges: (1) Locality: The color is often adjusted locally to preserve the semantics and atmosphere of the original image; and (2) Naturalness: The modified region needs to be carefully chosen and recolored to obtain a semantically valid and visually natural result. To address these challenges, we propose a learning-based approach to photo color adjustment for graphic designs, which maps an input photo along with the target colors to a recolored result. Our method decomposes the color adjustment process into two successive stages: modifiable region selection and target color propagation. The first stage aims to solve the core, challenging problem of which local image region(s) should be adjusted, which requires not only a common sense of colors appearing in our visual world but also understanding of subtle visual design heuristics. To this end, we capitalize on both natural photos and graphic designs to train a region selection network, which detects the most likely regions to be adjusted to the target colors. The second stage trains a recoloring network to naturally propagate the target colors in the detected regions. Through extensive experiments and a user study, we demonstrate the effectiveness of our selective region-based photo recoloring framework.

Artificial intelligence (AI) offers fuzzy set theory (FST) as one of the popular AI agents and decision making tools for digital image processing to increase the robustness of vision-based applications. FST is capable to handle uncertainties while enhancing image quality and preserving its naturalness. This article proposes a novel Adaptive Interval Type-2 Fuzzy Filter (AIT2FF) as an AI agent for preserving the naturalness of nonuniformly illuminated images. The proposed AIT2FF estimates coarse illumination of the input image, which is further processed to obtain the reflectance and refined coarse illumination for the composition of enhanced image. The estimated coarse illumination preserves the naturalness by eliminating uncertainties due to grayness ambiguities in homogeneous regions and spatial ambiguities at edges. The effectiveness of the proposed filter has been presented quantitatively in terms of lightness order error (LOE) and naturalness image quality evaluator (NIQE) score on images from the high dynamic range dataset and images captured with commercial digital cameras. The qualitative comparison visualizes that the enhanced images obtained using the proposed filter maintains visual realism and naturalness. The applications of the proposed filter have also been presented for image dehazing, vehicle tracking, and gradients estimation. Moreover, the detection results for dehazed images have been compared with state-of-the-art methods on real-world task driven testing set from REalistic Single Image DEhazing- $\beta$ dataset. The comparisons demonstrate that the enhanced images obtained using the proposed AIT2FF approach are better and outperform others in terms of LOE and NIQE measures. Impact Statement —The objective of vision-based applications is to automate the human visual perception. This requires a preprocessed input image with fine details. The image details to achieve human vision suffer from uncertainties in homogeneous regions and at edges. This result in an unnatural preprocessed image and degrades the performance. For example: Noisy images are not good to train deep learning models. AI offers one of the best tools, i.e., fuzzy set theory, which is capable to handle uncertainties. This article proposes an adaptive interval Type-2 fuzzy filter for elimination of uncertainties. The proposed filter enhances the image details and maintains its natural appearance. The elimination of uncertainties using proposed filter drops the error value from 2.573 to 1.929 for nonuniformly illuminated images. The proposed approach is able to boost the performance of various vision-based applications like object detection, vehicle tracking, security, medical diagnostics, etc.

The analysis of the studying of the problem of identifying and evaluating of typical and rare landscapes was carried out, which showed that quite a lot of knowledge has been accumulated in this area of the identification of such landscapes and the criteria for their assessment. It is established that insufficient attention is paid to the classification and mapping of landscapes, the use of mathematical methods for their assessment. A scientific concept is proposed for the study of typical and rare landscapes, which contains four levels of their study (country, regional, subregional, local), the scale of work corresponding to the level, landscape classification units, criteria and indicators for their allocation are determined. So, at the country level, the criterion for assessing typical landscapes is the degree of representativeness, depending on bioclimatic factors and the large size of the landscape contour, at the regional (provincial) – the contour size of the dominant landscapes of the province and the index of landscape diversity, at the subregional – the intensity of anthropogenic transformation, at the local – characteristic value of attractiveness. The main criteria for assessing rare landscapes at the country level are the small size of the contour and the criteria of uniqueness, at the regional (provincial) indicator of occurrence, at the subregional – naturalness, at local – vulnerability. A geographic information database has been created, a methodology has been developed for identifying typical and rare landscapes and their typology. An assessment of landscape diversity was made and the map showing that typical landscapes are characterised by a high and medium level of diversity, and rare landscapes are characterised by medium and low was created. An assessment of the occurrence of rare landscapes was carried out, which made it possible to distinguish three groups – low, medium, and high occurrence; their list and a map of occurrence were created. It has been determined that unique landscapes, which are part of a group with low occurrence, require special protection. A direct correlation has been established between the level of landscape diversity and occurrence. The proposed criteria for identifying and assessing typical and rare landscapes can be used in organising or transforming nature protected areas, in the design of their passports and conservation obligations.

Tampering with nature has been shown to be a strong, and sometimes even the strongest, predictor of the risk perception and acceptance of various technologies and behaviors, including environmental technologies, such as geoengineering. It is therefore helpful to understand what tampering with nature is as a construct, to which factors it relates, and when a technology or behavior is perceived as such. By means of a systematic review, we show that very little systematic research has been conducted on tampering with nature. Because tampering with nature has not yet been clearly defined, no systematic operationalization of tampering with nature has been used in the current literature. We show that tampering with nature is often used interchangeably with other constructs, such as naturalness. Based on the literature, we suggest that tampering with nature is related to and possibly influenced by three other constructs, which are naturalness, morality, and controllability. We discuss the influence of tampering with nature on the acceptance and risk perception of various technologies and behaviors and make suggestions for future research needs in order to better understand this construct.

In this paper, a new no-reference image quality assessment (NR-IQA) metric is proposed. This method is based on the probability coefficients and modified entropy. The probability coefficients are used to capture the significant features and represent the important dynamics of an image. Moreover, it is used to quantify the possible loss of naturalness in a distorted image. The proposed approach has been tested on synthetic images with blurring effects and under noisy conditions. It is observed that the proposed focus measure is invariant to contrast and noisy conditions. The effectuality of the proposed approach is tested on various experimentations carried out on real-time images. The performance of proposed focus measure is better as compared to the existing image focus metrics. It also shows a good consistency and a better response under blurring effects and noises at different levels. Moreover, it exhibits a good working range, unimodality and monotonicity properties and hence satisfying the conditions of a good focus measure. Further, the various experiments on image datasets such as LIVE, TID2008 and CSIQ have shown the effectiveness of proposed NR-IQA metric. It is also observed that the presented approach has good correlation with the human opinion score as compared to the existing methods.

High dynamic range (HDR) images can present realistic scenes with improved detail in high- and low-brightness regions. Due to their limitations in application, HDR images are often processed by tone-mapping operators (TMOs) to be backward-compatible with traditional devices. Hence, for a better visual experience, a TMO should naturally preserve the original scene information with image quality as a guideline. With such motivation, we propose a new quality-driven TMO (QdTMO) by using a pseudo-exposure scheme and a local optimal exposed image (LOEI) fusion network. First, a pseudo-exposure scheme is designed to generate richly detailed LOEIs for different brightness regions of the image. Then, scene information in the original HDR image can be naturally preserved by a specifically designed LOEIs fusion network. Finally, the exposure residual energy and brightness naturalness are used to optimize the pseudo-exposure scheme and LOEI fusion for better quality in the resulting tone-mapped image. Experimental results demonstrate that tone-mapped images generated by the proposed QdTMO have better performance than existing TMOs in both subjective and objective evaluations.

In the visual decoding domain, visually reconstructing presented images given the corresponding human brain activity monitored by functional magnetic resonance imaging (fMRI) is difficult, especially when reconstructing viewed natural images. Visual reconstruction is a conditional image generation on fMRI data and thus generative adversarial network (GAN) for natural image generation is recently introduced for this task. Although GAN-based methods have greatly improved, the fidelity and naturalness of reconstruction are still unsatisfactory due to the small number of fMRI data samples and the instability of GAN training. In this study, we proposed a new GAN-based Bayesian visual reconstruction method (GAN-BVRM) that includes a classifier to decode categories from fMRI data, a pre-trained conditional generator to generate natural images of specified categories, and a set of encoding models and evaluator to evaluate generated images. GAN-BVRM employs the pre-trained generator of the prevailing BigGAN to generate masses of natural images, and selects the images that best matches with the corresponding brain activity through the encoding models as the reconstruction of the image stimuli. In this process, the semantic and detailed contents of reconstruction are controlled by decoded categories and encoding models, respectively. GAN-BVRM used the Bayesian manner to avoid contradiction between naturalness and fidelity from current GAN-based methods and thus can improve the advantages of GAN. Experimental results revealed that GAN-BVRM improves the fidelity and naturalness, that is, the reconstruction is natural and similar to the presented image stimuli., Comment: brain decoding; visual reconstruction; generative adversarial network (GAN); Bayesian framework. under review in Neuroscience of Elsevier

For images with insufficient visibility, image processing is required. Retinex theory is often implemented for image contrast enhancement for images with this characteristic. This paper proposes a multi-scale retinex-based contrast enhancement method using the illumination component; this method could preserve the naturalness of color images. In the proposed method, illumination was first modified using an illumination modification factor; second, the image was enhanced via adaptive gamma correction. Finally, through the combination of the illumination components of the input image and the adaptive gamma correction image, we ensured the visibility and the naturalness of the output image. To confirm the effectiveness of the proposed method, we compared it with existing contrast enhancement methods. For the experiments, we employed discrete entropy, lightness order error, and mean chrominance error to perform the numerical evaluation. The results indicated that our method was better than a majority of existing methods. Moreover, with regard to the visual evaluation, the naturalness of the image obtained via the proposed method was superior to that of images obtained using other methods.

2 Abstract: The study related to human machine interaction has been done intensively to improve the ways human communication with the machine. The means of communicating with computers at the moment are limited to keyboards, mice, light pen, trackball, keypads, etc. These devices have grown to be familiar but inherently limit the speed and naturalness with which we interact with the computer. This paper proposes to use image processing and gesture recognition to simulate an environment where performing technical activities will become more like "real- world". The system proposes a real time human hand gesture recognition based system which interacts with the machine and processes the image. The classification of the gesture is based on the direction and gesture of the movement of the human hand. The motion of the hand will be captured and then the keyboard press actions are processed. Depending on this the image will move accordingly left, right, zoom in and zoom out.

Current software tools for computer generation of Persian calligraphy can be mostly described as conventional fonts and typesetting software, which basically neglect the ‘variations’ of real calligraphy performed by hand, in terms of personalization to different calligraphers’ styles, as well as their statistical characteristics. In this paper, we address the problem of natural-looking Persian calligraphy synthesis via a machine learning based approach, at the level of subwords. Given images of samples written by a calligrapher, we train a parametric model to imitate the style. The core idea is to make use of templates (fonts) as a source of background knowledge, and learn a probabilistic mapping from them to personal styles of calligraphers, which is posed as transformation of attributed graphs using neural networks with sliding windows. This can be understood as adding ‘naturalness’ to a Persian calligraphy font, in essence. We report both objective and subjective evaluations, including the model performance in writer (calligrapher) identification task and Visual Turing Test. The results of the latter suggest that humans are unable to distinguish the calligraphy synthesized by our approach from real calligraphy in many cases.

The present research suggests a new perspective to investigate the downstream effect of the production method on consumer responses. The authors propose that the higher behavioral intentions (e.g.,...

Most existing blind image quality assessment (BIQA) methods belong to supervised methods, which always need a large number of image samples and expensive subjective scores for training a quality prediction model. In this paper, we focus our attention on the unsupervised BIQA methods and put forward a novel unsupervised approach. The main idea of our method is to quantify the image quality degradation through measuring the structure, naturalness, and the perception quality variations of the distorted image from the pristine natural images. In specific, the structure variation is captured by the deviations of the image phase congruency and gradients distributions. The naturalness variation is characterized through the distributions variations of the locally mean subtracted and contrast normalized (MSCN) coefficients and the products of pairs of the adjacent MSCN coefficients. Compared with existing unsupervised methods, we initiatively introduce the perception quality measurement into the construction of unsupervised BIQA method, which is conducted by characterizing the prediction discrepancy between the image and its brain prediction based on the free-energy principle in the newly revealed brain theory. After feature extraction, we learn a pristine multivariate Gaussian (MVG) model with the extracted features from a set of pristine natural images. The quality of a new image is finally defined as the distance between its MVG model and the learned pristine MVG model. The extensive experiments conducted on LIVE, TID2013, CSIQ, Toyama, CID2013, and the Waterloo Exploration databases demonstrate that the proposed method achieves comparative prediction performance with the state-of-the-art BIQA methods.

During the process of watching 3D synthesised video (3D-SV) and switching viewpoints, there is a case of asymmetric distortion, the left(right) viewpoint is a synthesised video generated by rendering technique, and the right(left) viewpoint is a real video taken by the camera. How to accurately estimate the quality of 3D-SV with binocular asymmetric distortions is a new and challenging problem. Aiming at this problem, a blind quality assessment method for 3D-SV with binocular asymmetric distortions is proposed. Firstly, the local edge deformations of synthesised videos at different scales are measured by calculating their standard deviations. Secondly, the global naturalness of synthesised videos is computed by analysing their natural statistical characteristics. Thirdly, a strategy for fusing left and right quality scores is proposed, which considers their texture information in different directions. Finally, the random forest is used to obtain an objective quality score. The experimental results show the superiority of the proposed method on asymmetry 3D-SV database.

Wildlife corridors aim to promote species' persistence by connecting habitat patches across fragmented landscapes. Their implementation is limited by patterns of land ownership and complicated by differences in the jurisdictional and regulatory authorities under which lands are managed. Terrestrial corridor conservation requires coordination across jurisdictions and sectors subject to site-specific overlapping sources of legal authority. Mapping spatial patterns of legal authority concurrent with habitat condition can illustrate opportunities to build or leverage capacity for connectivity conservation. Streamside areas provide pragmatic opportunities to leverage existing policy mechanisms for riverine and terrestrial habitat connectivity across boundaries. Conservation planners and practitioners can make use of these opportunities by harmonizing actions for multiple conservation outcomes. We formulated an integrative, data-driven method for mapping multiple sources of legal authority weighted by capacity for coordinating terrestrial habitat conservation along streams. We generated a map of capacity to coordinate streamside corridor protections across a wildlife habitat gap to demonstrate this approach. We combined values representing coordination capacity and naturalness to generate an integrated legal-ecological resistance map for connectivity modeling. We then computed least-cost corridors across the integrated map, masking the terrestrial landscape to focus on streamside areas. Streamside least-cost corridors in the integrated, local-scale model diverged (∼25 km) from national-scale least-cost corridors based on naturalness. Spatial categories comparing legal- and naturalness-based resistance values by stream reach highlighted potential locations for building or leveraging existing capacity through spatial coordination of policy mechanisms or restoration actions. Agencies or nongovernmental organizations intending to restore or maintain habitat connectivity across fragmented landscapes can use this approach to inform spatial prioritization and build coordination capacity. Article impact statement: Combined mapping of legal authority and habitat condition reveals capacity to coordinate actions along streams for clean water and wildlife.Mapeo de la Autoridad Legal para los Corredores Terrestres de Conservación a lo Largo de Ríos Stahl et al. Resumen Los corredores de fauna buscan promover la persistencia de las especies al conectar los fragmentos de hábitat a lo largo de paisajes fragmentados. Su implementación está limitada por los patrones de propiedad de tierras y se complica con las diferencias entre las autoridades jurisdiccionales y regulatorias que las administran. La conservación por corredores terrestres requiere de coordinación entre las jurisdicciones y los sectores sujetos a fuentes de autoridad legal que se traslapan y que son específicas del sitio. El mapeo de los patrones espaciales de la autoridad legal simultánea a la condición del hábitat puede ilustrar oportunidades para construir o hacer uso de la capacidad para la conservación por conectividad. Las áreas adyacentes a los cauces fluviales proporcionan oportunidades prácticas para hacer uso de los mecanismos políticos existentes para la conectividad de hábitats ribereño y terrestre a través de las fronteras. Los planificadores y practicantes de la conservación pueden usar estas oportunidades al armonizar las acciones para múltiples resultados de conservación. Formulamos un método integrativo orientado por los datos para mapear las múltiples fuentes de autoridad legal ponderadas por la capacidad para coordinar la conservación de hábitats terrestres a lo largo de ríos. Generamos un mapa de la capacidad para coordinar los corredores de protección a lo largo de los vacíos en los hábitats de fauna para demostrar esta estrategia. Combinamos los valores por medio de la representación de la capacidad de coordinación y la naturalidad para generar un mapa de resistencia legal y ecológica para el modelado de la conectividad. Después, computamos los corredores de menor costo en todo el mapa integrado, enmascarando el paisaje terrestre para enfocarnos en las áreas adyacentes al cauce fluvial. Los corredores de menor costo adyacentes a los cauces dentro del modelo integrado de escala local difirieron (∼25 km) de los corredores de menor costo basados en la naturalidad a escala nacional. Las categorías espaciales que compararon los valores de resistencia basada en la legalidad y en la naturalidad por alcance del río resaltaron las localidades potenciales para la construcción o el uso de la capacidad existente por medio de la coordinación espacial de los mecanismos de política o de las acciones de restauración. Las agencias y organizaciones no gubernamentales con la intención de restaurar o mantener la conectividad del hábitat en un paisaje fragmentado pueden utilizar esta estrategia para informar la priorización espacial y construir la capacidad de coordinación.野生动物廊道旨在通过连接破碎景观中的栖息地斑块来提高物种的续存, 但其建设受限于土地所有制模式, 还会因管理土地的管辖及监管机构不同而更加复杂。陆地廊道保护需要多个司法管辖区和各地受到多种法定权威管制的部门之间的协调。将法定权威的空间格局与栖息地条件的分布地图相叠加, 将有利于开展廊道连接度保护的能力建设及利用。河流沿岸地区为现有的跨境河流及陆地栖息地连接度的相关政策机制的应用提供了实践机会。保护规划者和实施者可以利用这些机会协调多方行动以取得各方面的保护成效。我们制定了一个综合的、基于数据驱动的方法, 可以将协调河流沿岸陆地栖息地保护的能力作为权重来绘制多个法定权威来源的地图。为了演示这个方法, 我们绘制了一张协调跨越野生动物栖息地空缺地带的河流沿岸廊道保护力地图。我们将代表协调能力和自然特性的数值相结合, 生成了一张用于连接度建模的法律-生态抵抗力综合地图。随后, 我们忽略陆地景观, 将重点放在河流沿岸地区, 计算了整个综合地图的最低成本廊道。结果显示, 由局部尺度的综合模型得到的河流沿岸最低成本廊道与基于自然特性的国家尺度最低成本廊道偏离约 25 公里。河流沿岸基于法律和自然特性抵抗力比较的空间分类进一步展示了可以通过政策机制或恢复行动的空间协调来建设和利用现有能力的潜在地点。计划恢复或维持破碎景观之间的栖息地连接度的机构或非政府组织可以应用这种方法确定优先保护的空间并建立协调力。【翻译: 胡怡思; 审校: 聂永刚】.

In recent works, the illumination estimation based on Retinex is utilized to enhance the image details. However, to enhance the image using illumination estimation without affecting the naturalness with non-uniform illumination is very difficult. The usual algorithms used for illumination estimation failed to include these constraints such as sharp edges on illumination boundaries, spatial smoothness, and the limited range of illumination while preserving the naturalness. In this paper, an illumination estimation algorithm using normalized $$L_2$$ -Norm with the joint edge-preservation filter is proposed. The proposed algorithm efficiently estimates illumination and fulfils all the above-mentioned constraints. The normalized $$L_2$$ -Norm is used to approximate the illumination to overcome the block effect in patch-wise illumination. Then, the overall structure is refined by using the joint edge-preservation filter. Experimental results show the quality of the smoothness of illumination beyond the edges and ensure the range of the estimated illumination in comparison with the other state-of-the-art algorithms.

Depth image based rendering (DIBR) has been widely used to generate different virtual viewpoints of the same scene from the new perspective. However, DIBR tends to introduce annoying artifacts including blurring, discontinuity, blocking, and stretching, etc. . Thus, to improve DIBR performance, it is important to accurately measure the visual quality of synthesized views. In this paper, we propose a novel and effective no reference (NR) quality assessment method for 3D synthesized views by local variation and global change (LVGC). More specifically, we firstly compute the Gaussian derivatives for the input image to extract structure and chromatic features. Then, we use the local binary pattern (LBP) operator to encode the structure and chromatic feature maps, which are used to calculate quality-aware features to measure the local structural and chromatic distortion. Besides, we extract luminance features by global change to evaluate the naturalness of 3D synthesized views. With these extracted features, we utilize random forest regression (RFR) to train the quality prediction model from visual features to human ratings. Experimental results on three public benchmark databases demonstrate the effectiveness of our method on estimating visual quality of 3D synthesized views.

The concept of “naturalness” can be associated with conservation status, resilience, and biodiversity. Its most common definition relates to the degree to which a resource is similar to its original state. Hence, we developed a naturalness assessment method for the Brazilian Araucaria forest. We used data collected within 145 systematically distributed plots over an area of ∼56 000 km2. We selected five indicators to compose a unified naturalness index: (i) evidence of human activities inside the forest stand; (ii) abundance of naturalness-indicator species; (iii) standard deviation of diameter at breast height (Sdbh); (iv) species diversity of the understory–natural regeneration layer; and (v) forest stand landscape metrics. We then calculated the Euclidean distance between the vector generated from the indicators of an ordinary forest stand and the vector generated from a theoretical reference forest (TRF) with maximum naturalness. The reduced Sdbh reflected the stands’ diminished structural diversity as result of historical logging and other ongoing human activities. Most stands presented average naturalness compared with the TRF. Besides the lack of data on undisturbed forests to thoroughly evaluate the naturalness index, evidence suggested that it summarized relevant forest attributes to the extent that protected areas presented greater naturalness than nonprotected areas.

The result of the blood chemistry tests is usually presented in the form of a table written in abbreviations, numbers, and units. Unfortunately, the young doctors often require the time and experience for interpreting the blood chemistry tests into a textual representation, which is easy to read and understand. Therefore, this research aimed at developing a system (BTISys) that can generate the textual representation in the Indonesian language automatically based on the blood chemistry test. BTISys generates the representation using Natural Language Generation (NLG) approach based on the r-template method. The reliability of BTISys is measured by considering the naturalness of generated textual representation. The naturalness can be observed by three criteria, such as readability, clarity, and general appropriateness. The reliability of BTISys is quite good to generate the textual representation automatically. It can be seen from the readability, clarity, and general appropriateness, which reach 73 %, 70 %, and 60 % respectively, that implies the naturalness of generated textual representation.

The purpose of this study is to analyze the Japanese version and the two Russian versions of the Japanese Manga written by Nakazawa Keiji, Hadashi no Gen (Barefoot Gen), based on Descriptive Translation Studies, and find out tendencies of the translation. Manga, the Japanese comic, is one of the materials for knowing Japanese culture, especially subculture along with Anime; therefore, Manga serves as an attractive learning material for foreign learners of Japanese. The present researcher used the three-phase methodology proposed by Toury (1995). In the first stage, the author situated a translated text within the target culture system; in the second stage, compared the Source Text and the Target Text for shifts, identifying relationships between ‘coupled pairs’ of the Source Text and Target Text segments; in the third stage, attempted to draw some generalizations regarding the translation strategies employed. The result showed that the new translation of Russian versions adopted a Source Text-oriented translation strategy in pages structure of Manga; that is, it emphasized Japanese culture. Henceforth, research findings enable identifying translation strategy’s naturalness (RT1 / RT2) by quantitatively analyzes of linguistic expressions and structure translation of pages. Key words: Japanese, Russian, Manga, Descriptive Translation Studies.

Many river systems in Europe have altered morphology and deteriorated ecosystems due to human interference. We demonstrate how conflicting interests of nature, society and economics in the Dutch–German Ems-Dollard system complicate achieving the nature restoration targeted by the EU Water Framework Directive. This article provides a multidisciplinary perspective on the natural characteristics of a water system and the practical implementation of regulation and policy in a transboundary setting. Important shortcomings of EU and national laws and directives are the static constraints for protection of demarcated habitats under EU directives, which do not do justice to natural hydro-morphodynamic processes.

Travelling speed plays an essential role in the overall user experience while navigating inside a virtual environment. Researchers have used various travelling speed that matches the user speed profile in order to give a natural walking experience. However, predicting a user’s instantaneous walking speed can be challenging when there is no continuous input from the user. Target selection-based techniques are those where the user selects the target to reach there automatically. These techniques also lack naturalness due to their low interaction fidelity. In this work, we have proposed a mathematical model that can dynamically compute the instantaneous natural walking speed while moving from one point to another in a virtual environment. We formulated our model with the help of user studies.

For many consumers, it is important that food products be natural. Naturalness is a perceived property of food, but in the present study, we demonstrate that an objectively defined Food Naturalness Index (FNI) predicts perceived naturalness with a high degree of accuracy. A sample of 179 participants ranked 28 snacks, ranging from least natural to the most natural. Correlations on aggregated and individual levels suggest that perceived naturalness is strongly associated with the FNI. The food industry could therefore use the FNI to predict perceived naturalness during the product development phase of snacks, and it might also be a promising tool for regulating the use of naturalness claims in food marketing., Food Quality and Preference, 94, ISSN:0950-3293, ISSN:1873-6343

This article describes the design of KommPaS, a web-based tool for the clinical assessment of communication impairment in persons with dysarthria. KommPaS (the German acronym for Communication-related Parameters in Speech Disorders) allows clinicians to recruit laypersons via crowdsourcing for the evaluation of samples of dysarthric speech with regard to communication relevant parameters, that is, intelligibility, naturalness, perceived listener effort, and efficiency (intelligible speech units per unit time). Moreover, a communication total score describing the KommPaS profile elevation, i.e., the arithmetic mean of the normalized KommPaS scores, is provided. Based on considerations regarding the theoretical underpinnings and methodological constraints of a clinical tool for the assessment of these parameters, the article describes how each theoretically and methodologically motivated feature is translated into design principles and how these principles are implemented in a web application. The paper reports efficiency data and details the data privacy and data security provisions that are essential in such an approach.

This paper introduces an evaluation of the amount of data required by the Tacotron2 speech synthesis model in order to achieve a good quality output synthesis. We evaluate the capabilities of the model to adapt to new speakers in very limited data scenarios. We use three Romanian speakers for which we gathered at most 5 minutes of speech, and use this data to fine tune a large pre-trained model over a few training epochs. We look at the performance of the system by evaluating the intelligibility, naturalness and speaker similarity measures, as well as performing an analysis of the trade-off between speech quality and overfitting of the network.The results show that the Tacotron2 network can replicate the identity of a speaker from as little as one speech sample. Also it inherently learns individual grapheme representations, such that if the training data is carefully selected to present all the common graphemes in the language, the adaptation data requirements can be significantly lowered.

Computer generated speech has improved drastically due to advancements in voice synthesis using deep learning techniques. The latest speech synthesizers achieve such high level of naturalness that humans have difficulty distinguishing real speech from computer generated speech. These technologies allow any person to train a synthesizer with a target voice, creating a model that is able to reproduce someone’s voice with high fidelity. This technology can be used in several legit commercial applications (e.g. call centres) as well as criminal activities, such as the impersonation of someone’s voice.In this paper, we analyze how synthetic speech is generated and propose deep learning methodologies to detect such synthesized utterances. Using a large dataset containing both synthetic and real speech, we analyzed the performance of the latest deep learning models in the classification of such utterances. Our proposed model achieves up to 92.00% accuracy in detecting unseen synthetic speech, which is a significant improvement from human performance (65.7%).

In this paper, we propose a method to reproduce the reflection of a real scene on a virtual object using only the images captured by a camera attached to the front of a mobile device. Since it is not possible to acquire the entire scene using only the front camera, the area surrounding the front camera image is extrapolated to obtain a sufficient scene for reflection. Image transformation using deep neural networks is used for extrapolation, and high-quality and stable extrapolation is realized by using the extrapolation results in the previous frame. As a result of experiment that evaluated the quality of extrapolation and reproduction of reflection, we confirmed that both extrapolated images and reproduced reflection looked natural. We also conducted an experiment to evaluate users' impression of the reflection. The results showed that the proposed method was effective for participants who knew about AR in naturalness of reflection and material perception.

The marriage of Artificial Intelligence (AI) techniques to problems surrounding the generation, maintenance, and use of source code has come to the fore in recent years as an important AI application area1. A large chunk of this recent attention can be attributed to contemporaneous advancements in Natural Language Processing (NLP) techniques and sub-fields. The naturalness hypothesis, which states that "software is a form of human communication" and that code exhibits patterns that are similar to (human) natural languages (Devanbu, 2015; Hindle, Barr, Gabel, Su, & Devanbu, 2016), has allowed for the application of many of these NLP advances to code-centric usecases. This development has contributed to a spate of work in the community --- much of it captured in a survey by Allamanis, Barr, Devanbu, and Sutton (2018) that focuses on classifying these approaches by the type of probabilistic model applied to source code. This increase in the variety of AI techniques applied to source code has found various manifestations in the industry at large. Code and software form the backbone that underpins almost all modern technical advancements: it is thus natural that breakthroughs in this area should reflect in the emergence of real world deployments.

The scene-preferring portion of the human ventral visual stream, known as the parahippocampal place area (PPA), responds to scenes and landmark objects, which tend to be large in real-world size, fixed in location, and inanimate. However, the PPA also exhibits preferences for low-level contour statistics, including rectilinearity and cardinal orientations, that are not directly predicted by theories of scene- and landmark-selectivity. It is unknown whether these divergent findings of both low- and high-level selectivity in the PPA can be explained by a unified computational theory. To address this issue, we fit feedforward computational models of visual feature coding to the image-evoked fMRI responses of the PPA, and we performed a series of high-throughput experiments on these models. Our findings show that feedforward models of the PPA exhibit emergent selectivity across multiple levels of complexity, giving rise to seemingly high-level preferences for scenes and for objects that are large, spatially fixed, and inanimate/manmade while simultaneously yielding low-level preferences for rectilinear shapes and cardinal orientations. These results reconcile disparate theories of PPA function in a unified model of feedforward feature coding, and they demonstrate how multifaceted selectivity profiles naturally emerge from the feedforward computations of visual cortex and the natural statistics of images.SIGNIFICANCE STATEMENTVisual neuroscientists characterize cortical selectivity by identifying stimuli that drive regional responses. A perplexing finding is that many higher-order visual regions exhibit selectivity profiles spanning multiple levels of complexity: they respond to highly complex categories, such as scenes and landmarks, but also to surprisingly simplistic features, such as specific contour orientations. Using large-scale computational analyses and human brain imaging, we show how multifaceted selectivity in scene-preferring cortex can emerge from the feedforward, hierarchical coding of visual features. Our work reconciles seemingly divergent findings of selectivity in scene-preferring cortex and suggests that surprisingly simple feedforward feature representations may be central to the category-selective organization of the human visual system.

In this article, we examine the influence of scene articulation on transparent layer constancy. We argue that the term articulation may be understood as an aspect of the more general concept naturalness of a stimulus that relates to the degree of enrichment compared with a minimal stimulus and to the extent to which a stimulus contains regularities that are typically found in natural scenes. We conducted two matching experiments, in which we used strongly reduced scenes and operationalized articulation by the number of background reflectances (numerosity). The results of the first experiment show that higher numerosity actually leads to an increase in transparent layer constancy when reflectances are randomly drawn from a fixed population. However, this advantage disappears if the spatial mean and the variation of the subset colors are controlled as in our second experiment. Furthermore, our results suggest that the mechanism underlying transparent layer constancy leads to a rather stable compromise between two matching criteria, namely, proximal identity and constant filter properties according to our perceptual model. For filters with an additive component, which appear more or less hazy, we observed improved recovered filter properties and correspondingly higher degrees of transparent layer constancy, suggesting an additional mechanism in this type of filter.