Computerized measures of visual complexity

Neurocognitive dynamics and behavioral differences of symmetry and asymmetry processing in working memory: insights from fNIRS

Symmetry is a ubiquitous property of the visual world. It facilitates cognitive processing and fosters aesthetic appeal. Despite its importance to aesthetic experience and perceptual prominence, the integration of symmetry in working memory remains underexplored. In our study, participants engaged in a novel working memory task involving both symmetrical and asymmetrical stimuli, while their brain activity was monitored using functional Near Infrared Spectroscopy (fNIRS). The study revealed that symmetry significantly enhances memory performance. Symmetry significantly improves task performance, with symmetrical stimuli leading to higher accuracy and faster recall than asymmetrical ones, especially under high cognitive load. This effect varies with the type of symmetry, with diagonal symmetry being the most effective. Neuroimaging data showed distinct brain activation patterns when participants processed symmetrical stimuli, particularly in the memory-straining condition. Significant differences in brain activity were observed in various brain regions, with lateral occipital, posterior parietal, medial and dorsolateral prefrontal cortices reacting to symmetry with decreased oxygenated hemoglobin (HbO), while in left orbitofrontal (HbO) and right ventrolateral prefrontal cortex (HbO and HbR) hemoglobin concentration increased. Overall, our findings highlight the complex, region-specific brain activation patterns in response to visual symmetry, emphasizing the nuanced role of symmetry in cognitive processing during memory tasks and their potential implication for creative thinking.

Structural stability and thermodynamics of artistic composition

Inspired by the way that digital artists zoom out of the canvas to assess the visual impact of their works, we introduce a conceptually simple yet effective metric for quantifying the clarity of digital images. This metric contrasts original images with progressively "melted" counterparts, produced by randomly flipping adjacent pixel pairs. It measures the presence of stable structures, assigning the value zero to completely uniform or random images and finite values for those with discernible patterns. This metric respects the color diversity of the original image and withstands image compression and color quantization. Its suitability for diverse image analysis problems is demonstrated through its effective evaluation of textural images, the identification of structural transitions in physical systems like the Potts model, and its consistency with color theory in digital arts. This allows us to demonstrate that color in visual art functions as a state variable, akin to the spin configuration in magnets, driving artistic designs to transition between states with distinct clarity. When combined with the Shannon entropy, which quantifies color diversity, the structural stability metric can serve as a navigation tool for artists to explore pathways on the complex structural information landscape toward the completion of their artwork. As a practical demonstration, we apply our metric to refine and optimize an emote design for a video game. The structural stability metric emerges as a versatile tool for extracting nuanced structural information from digital images, which may enhance decision-making and data analysis across scientific and creative domains.

The Effects of Layout Order on Interface Complexity: An Eye-Tracking Study for Dashboard Design

This study investigated the effect of layout order on the complexity of the dashboard interface based on screen-based eye trackers. By simplifying and abstracting dashboard interfaces and incorporating subjective ratings (symmetry and unity calculations), we successfully manipulated the levels of complexity and layout order of the interface materials. Using four types of eye movement data (total fixation count, total gaze duration, scanning paths, and hotspot maps) and behavioral data, we compared participants' visual search behavior on interfaces with different layout orders and complexity levels. Experiment 1 revealed a significant interaction between layout order and interface complexity, with participants performing significantly better in the high-level layout order condition. Experiment 2 confirmed that the position of the core chart plays a crucial role in users' visual search behavior and that the optimal layout order for the dashboard is to place the core chart on the left side of the interface's horizontal axis, with partial symmetry in the no-core chart areas. This study highlights the effectiveness of eye-tracking techniques in user interface design research and provides valuable insights into optimizing dashboard interface design. Designers should adopt the design principle of "order is more" in addition to "less is more" and consider designing the core chart in the left-center position.

Modular GAN: positron emission tomography image reconstruction using two generative adversarial networks

Introduction

The reconstruction of PET images involves converting sinograms, which represent the measured counts of radioactive emissions using detector rings encircling the patient, into meaningful images. However, the quality of PET data acquisition is impacted by physical factors, photon count statistics and detector characteristics, which affect the signal-to-noise ratio, resolution and quantitative accuracy of the resulting images. To address these influences, correction methods have been developed to mitigate each of these issues separately. Recently, generative adversarial networks (GANs) based on machine learning have shown promise in learning the complex mapping between acquired PET data and reconstructed tomographic images. This study aims to investigate the properties of training images that contribute to GAN performance when non-clinical images are used for training. Additionally, we describe a method to correct common PET imaging artefacts without relying on patient-specific anatomical images.

Methods

The modular GAN framework includes two GANs. Module 1, resembling Pix2pix architecture, is trained on non-clinical sinogram-image pairs. Training data are optimised by considering image properties defined by metrics. The second module utilises adaptive instance normalisation and style embedding to enhance the quality of images from Module 1. Additional perceptual and patch-based loss functions are employed in training both modules. The performance of the new framework was compared with that of existing methods, (filtered backprojection (FBP) and ordered subset expectation maximisation (OSEM) without and with point spread function (OSEM-PSF)) with respect to correction for attenuation, patient motion and noise in simulated, NEMA phantom and human imaging data. Evaluation metrics included structural similarity (SSIM), peak-signal-to-noise ratio (PSNR), relative root mean squared error (rRMSE) for simulated data, and contrast-to-noise ratio (CNR) for NEMA phantom and human data.

Results

For simulated test data, the performance of the proposed framework was both qualitatively and quantitatively superior to that of FBP and OSEM. In the presence of noise, Module 1 generated images with a SSIM of 0.48 and higher. These images exhibited coarse structures that were subsequently refined by Module 2, yielding images with an SSIM higher than 0.71 (at least 22% higher than OSEM). The proposed method was robust against noise and motion. For NEMA phantoms, it achieved higher CNR values than OSEM. For human images, the CNR in brain regions was significantly higher than that of FBP and OSEM (p < 0.05, paired t-test). The CNR of images reconstructed with OSEM-PSF was similar to those reconstructed using the proposed method.

Conclusion

The proposed image reconstruction method can produce PET images with artefact correction.

Fractal contours: Order, chaos, and art

Over the recent decades, a variety of indices, such as the fractal dimension, Hurst exponent, or Betti numbers, have been used to characterize structural or topological properties of art via a singular parameter, which could then help to classify artworks. A single fractal dimension, in particular, has been commonly interpreted as characteristic of the entire image, such as an abstract painting, whether binary, gray-scale, or in color, and whether self-similar or not. There is now ample evidence, however, that fractal exponents obtained using the standard box-counting are strongly dependent on the details of the method adopted, and on fitting straight lines to the entire scaling plots, which are typically nonlinear. Here, we propose a more discriminating approach with the aim of obtaining robust scaling plots and extracting relevant information encoded in them without any fitting routines. To this goal, we carefully average over all possible grid locations at each scale, rendering scaling plots independent of any particular choice of grids and, crucially, of the orientation of images. We then calculate the derivatives of the scaling plots, so that an image is described by a continuous function, its fractal contour, rather than a single scaling exponent valid over a limited range of scales. We test this method on synthetic examples, ordered and random, then on images of algorithmically defined fractals, and finally, examine selected abstract paintings and prints by acknowledged masters of modern art.

Social signals predict contemporary art prices better than visual features, particularly in emerging markets

What determines the price of an artwork? This article leverages a comprehensive and novel dataset on art auctions of contemporary artists to examine the impact of social and visual features on the valuation of artworks across global markets. Our findings indicate that social signals allow us to predict the price of artwork exceptionally well, even approaching the professionals' prediction accuracy, while the visual features play a marginal role. This pattern is especially pronounced in emerging markets, supporting the idea that social signals become more critical when it is more difficult to assess the quality. These results strongly support that the value of artwork is largely shaped by social factors, particularly in emerging markets where a stronger preference for "buying an artist" than "buying an artwork." Additionally, our study shows that it is possible to boost experts' performance, highlighting the potential benefits of human-machine models in uncertain or rapidly changing markets, where expert knowledge is limited.

Modeling the Visual Landscape: A Review on Approaches, Methods and Techniques

Modeling the perception and evaluation of landscapes from the human perspective is a desirable goal for several scientific domains and applications. Human vision is the dominant sense, and human eyes are the sensors for apperceiving the environmental stimuli of our surroundings. Therefore, exploring the experimental recording and measurement of the visual landscape can reveal crucial aspects about human visual perception responses while viewing the natural or man-made landscapes. Landscape evaluation (or assessment) is another dimension that refers mainly to preferences of the visual landscape, involving human cognition as well, in ways that are often unpredictable. Yet, landscape can be approached by both egocentric (i.e., human view) and exocentric (i.e., bird's eye view) perspectives. The overarching approach of this review article lies in systematically presenting the different ways for modeling and quantifying the two 'modalities' of human perception and evaluation, under the two geometric perspectives, suggesting integrative approaches on these two 'diverging' dualities. To this end, several pertinent traditions/approaches, sensor-based experimental methods and techniques (e.g., eye tracking, fMRI, and EEG), and metrics are adduced and described. Essentially, this review article acts as a 'guide-map' for the delineation of the different activities related to landscape experience and/or management and to the valid or potentially suitable types of stimuli, sensors techniques, and metrics for each activity. Throughout our work, two main research directions are identified: (1) one that attempts to transfer the visual landscape experience/management from the one perspective to the other (and vice versa); (2) another one that aims to anticipate the visual perception of different landscapes and establish connections between perceptual processes and landscape preferences. As it appears, the research in the field is rapidly growing. In our opinion, it can be greatly advanced and enriched using integrative, interdisciplinary approaches in order to better understand the concepts and the mechanisms by which the visual landscape, as a complex set of stimuli, influences visual perception, potentially leading to more elaborate outcomes such as the anticipation of landscape preferences. As an effect, such approaches can support a rigorous, evidence-based, and socially just framework towards landscape management, protection, and decision making, based on a wide spectrum of well-suited and advanced sensor-based technologies.

Responses to Raven matrices: Governed by visual complexity and centrality

Raven matrices are widely considered a pure test of cognitive abilities. Previous research has examined the extent to which cognitive strategies are predictive of the number of correct responses to Raven items. This study examined whether response times can be explained directly from the centrality and visual complexity of the matrix cells (edge density and perceived complexity). A total of 159 participants completed a 12-item version of the Raven Advanced Progressive Matrices. In addition to item number (an index of item difficulty), the findings demonstrated a positive correlation between the visual complexity of Raven items and both the mean response time and the number of fixations on the matrix (a strong correlate of response time). Moreover, more centrally placed cells as well as more complex cells received more fixations. It is concluded that response times on Raven matrices are impacted by low-level stimulus attributes, namely, visual complexity and eccentricity.

How do drivers mitigate the effects of naturalistic visual complexity? : On attentional strategies and their implications under a change blindness protocol

How do the limits of high-level visual processing affect human performance in naturalistic, dynamic settings of (multimodal) interaction where observers can draw on experience to strategically adapt attention to familiar forms of complexity? In this backdrop, we investigate change detection in a driving context to study attentional allocation aimed at overcoming environmental complexity and temporal load. Results indicate that visuospatial complexity substantially increases change blindness but also that participants effectively respond to this load by increasing their focus on safety-relevant events, by adjusting their driving, and by avoiding non-productive forms of attentional elaboration, thereby also controlling "looked-but-failed-to-see" errors. Furthermore, analyses of gaze patterns reveal that drivers occasionally, but effectively, limit attentional monitoring and lingering for irrelevant changes. Overall, the experimental outcomes reveal how drivers exhibit effective attentional compensation in highly complex situations. Our findings uncover implications for driving education and development of driving skill-testing methods, as well as for human-factors guided development of AI-based driving assistance systems.

Does prior knowledge increase or decrease perceived visual complexity of texture images?

Previous research has shown that the perceived visual complexity of an image is correlated with understandability of the image. It was considered that prior knowledge of the contents of an image makes images easier to understand, and thus reduces perceived visual complexity. In the present study, we examined the effect of prior knowledge on perceived visual complexity of texture images. We designed an experiment in which participants observed and rated four texture images with different levels of complexity and understandability; one group of participants received prior knowledge in the form of verbal cues about the names of the target stimuli while the other group did not receive any information regarding image content. We found that the effect of prior knowledge on visual complexity perception varied for the different images. For an image with low initial complexity, if cued information about the image is three-dimensional or dynamic, prior knowledge does not decrease but instead increases the perceived visual complexity. Moreover, cues that increase perceived visual complexity can be verbal rather than visual cues.

Statistical image properties predict aesthetic ratings in abstract paintings created by neural style transfer

Artificial intelligence has emerged as a powerful computational tool to create artworks. One application is Neural Style Transfer, which allows to transfer the style of one image, such as a painting, onto the content of another image, such as a photograph. In the present study, we ask how Neural Style Transfer affects objective image properties and how beholders perceive the novel (style-transferred) stimuli. In order to focus on the subjective perception of artistic style, we minimized the confounding effect of cognitive processing by eliminating all representational content from the input images. To this aim, we transferred the styles of 25 diverse abstract paintings onto 150 colored random-phase patterns with six different Fourier spectral slopes. This procedure resulted in 150 style-transferred stimuli. We then computed eight statistical image properties (complexity, self-similarity, edge-orientation entropy, variances of neural network features, and color statistics) for each image. In a rating study, we asked participants to evaluate the images along three aesthetic dimensions (Pleasing, Harmonious, and Interesting). Results demonstrate that not only objective image properties, but also subjective aesthetic preferences transferred from the original artworks onto the style-transferred images. The image properties of the style-transferred images explain 50 – 69% of the variance in the ratings. In the multidimensional space of statistical image properties, participants considered style-transferred images to be more Pleasing and Interesting if they were closer to a “sweet spot” where traditional Western paintings (JenAesthetics dataset) are represented. We conclude that NST is a useful tool to create novel artistic stimuli that preserve the image properties of the input style images. In the novel stimuli, we found a strong relationship between statistical image properties and subjective ratings, suggesting a prominent role of perceptual processing in the aesthetic evaluation of abstract images.

Visual complexity of egg patterns predicts egg rejection according to Weber's law

Visual complexity is ubiquitous in nature. Drivers of complexity include selection in coevolutionary arms races between antagonists. However, the causes and consequences of biological complexity and its perception are largely understudied, partly because complexity is difficult to quantify. Here, we address this by studying egg pattern complexity and its perception in hosts (tawny-flanked prinia Prinia subflava), which visually recognize and reject mimetic eggs of their virulent brood parasite (cuckoo finch Anomalospiza imberbis). Using field data and an optimization algorithm, we compute a complexity metric which predicts rejection of experimentally placed conspecific eggs in prinia nests. Real cuckoo finch eggs exhibit significantly lower pattern complexity than prinia eggs, suggesting that high complexity benefits hosts because it distinguishes host eggs from parasitic eggs. We show that prinias perceive complexity differences according to Weber's law of proportional processing (i.e. relative, rather than absolute, differences between stimuli are processed in discrimination, such that two eggs with simple patterns are more easily discriminable than two with complex patterns). This may influence coevolutionary trajectories of hosts and parasites. The new methods presented for quantifying complexity and its perception can help us to understand selection pressures driving the evolution of complexity and its consequences for species interactions.

A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction

Pedestrian-friendly cities are a recent global trend due to the various urbanization problems. Since humans are greatly influenced by sight while walking, this study identified the physical and visual characteristics of the street environment that affect pedestrian satisfaction. In this study, vast amounts of visual data were collected and analyzed using computer vision techniques. Furthermore, these data were analyzed through a machine learning prediction model and SHAP algorithm. As a result, every visual feature of the streetscape, for example, the visible area and urban design quality, had a greater effect on pedestrian satisfaction than any physical features. Therefore, to build a street with high pedestrian satisfaction, the perspective of pedestrians must be considered, and wide sidewalks, fewer lanes, and the proper arrangement of street furniture are required. In conclusion, visually, low enclosure, adequate complexity, and large green areas combine to create a highly satisfying pedestrian walkway. Through this study, we could suggest an approach from a visual perspective for the pedestrian environment of the street and see the possibility of using computer vision techniques.

Does Amount of Information Support Aesthetic Values?

Obtaining information from the world is important for survival. The brain, therefore, has special mechanisms to extract as much information as possible from sensory stimuli. Hence, given its importance, the amount of available information may underlie aesthetic values. Such information-based aesthetic values would be significant because they would compete with others to drive decision-making. In this article, we ask, "What is the evidence that amount of information support aesthetic values?" An important concept in the measurement of informational volume is entropy. Research on aesthetic values has thus used Shannon entropy to evaluate the contribution of quantity of information. We review here the concepts of information and aesthetic values, and research on the visual and auditory systems to probe whether the brain uses entropy or other relevant measures, specially, Fisher information, in aesthetic decisions. We conclude that information measures contribute to these decisions in two ways: first, the absolute quantity of information can modulate aesthetic preferences for certain sensory patterns. However, the preference for volume of information is highly individualized, with information-measures competing with organizing principles, such as rhythm and symmetry. In addition, people tend to be resistant to too much entropy, but not necessarily, high amounts of Fisher information. We show that this resistance may stem in part from the distribution of amount of information in natural sensory stimuli. Second, the measurement of entropic-like quantities over time reveal that they can modulate aesthetic decisions by varying degrees of surprise given temporally integrated expectations. We propose that amount of information underpins complex aesthetic values, possibly informing the brain on the allocation of resources or the situational appropriateness of some cognitive models.

Picture naming in bilingual and monolingual Chinese speakers: Capturing similarity and variability

Picture-naming latency differs across languages in bilingual speakers. We compared the effects of key psycholinguistic variables on picture naming among two groups of Chinese bilingual speakers and Mandarin monolingual speakers. First, we asked bilingual and monolingual speakers to estimate the age of acquisition, familiarity, visual complexity, name agreement, and imageability of a set of object and action pictures in Mandarin and Cantonese. Next, we recruited 60 Cantonese-English speakers, 50 Mandarin-Cantonese bilingual speakers, and 30 monolingual speakers who named the object and action pictures in Cantonese and Mandarin, respectively. We observed variability in the effects of item-level characteristics among groups, suggesting an interaction between item-level and individual-level characteristics as predicted. This variability was higher in bilingual speakers who spoke similar languages (Mandarin-Cantonese) in comparison to those speaking more distant languages (Cantonese-English). Our results suggest that monolingual norms and bilingual norms capture the same amount of variability; however, grammatical class interactions with other variables are explained differentially by the bilingual and monolingual norms. We discuss the implications of our findings in terms of norming studies for timed picture naming and effects of bilingualism on language processing.

Curious Objects: How Visual Complexity Guides Attention and Engagement

Some things look more complex than others. For example, a crenulate and richly organized leaf may seem more complex than a plain stone. What is the nature of this experience-and why do we have it in the first place? Here, we explore how object complexity serves as an efficiently extracted visual signal that the object merits further exploration. We algorithmically generated a library of geometric shapes and determined their complexity by computing the cumulative surprisal of their internal skeletons-essentially quantifying the "amount of information" within each shape-and then used this approach to ask new questions about the perception of complexity. Experiments 1-3 asked what kind of mental process extracts visual complexity: a slow, deliberate, reflective process (as when we decide that an object is expensive or popular) or a fast, effortless, and automatic process (as when we see that an object is big or blue)? We placed simple and complex objects in visual search arrays and discovered that complex objects were easier to find among simple distractors than simple objects are among complex distractors-a classic search asymmetry indicating that complexity is prioritized in visual processing. Next, we explored the function of complexity: Why do we represent object complexity in the first place? Experiments 4-5 asked subjects to study serially presented objects in a self-paced manner (for a later memory test); subjects dwelled longer on complex objects than simple objects-even when object shape was completely task-irrelevant-suggesting a connection between visual complexity and exploratory engagement. Finally, Experiment 6 connected these implicit measures of complexity to explicit judgments. Collectively, these findings suggest that visual complexity is extracted efficiently and automatically, and even arouses a kind of "perceptual curiosity" about objects that encourages subsequent attentional engagement.

The Relationship of Symmetry, Complexity, and Shape in Mobile Interface Aesthetics, from an Emotional Perspective—A Case Study of the Smartwatch

Products with interactive interfaces can be seen everywhere, and product interface design aesthetics is a topic that has begun to receive wide attention. Consumers’ perceptions of product interfaces come from their own emotions, and emotion plays a significant role in product interface design aesthetics. In other words, it must meet the users’ emotional and aesthetic requirements. Therefore, we need to better understand the aesthetic design criteria and how they stimulate specific emotional responses. This study takes the dial interface of smartwatches as its experimental sample and explores how the interaction effects of the screen shape (square and round) and the symmetry type and the complexity type of the interface design influence the users’ emotional arousal and valence. In addition, it analyzes the effects of the symmetry type, the complexity type, and the screen shape on the users’ arousal and valence. The results show that the attributes of interface design aesthetics (symmetry-asymmetry, complexity-simplicity, and square-round) affect the users’ emotional responses. Moreover, the interface shape is one of the important factors in the emotional response to an interface design. This paper, based on previous research, provides vital theoretical support for the relevant literature on interface design aesthetics and the users’ emotional state. In addition, it may provide a reference for designers and developers who wish to develop and implement emotional user interfaces that are designed to more effectively appeal to their emotions.

Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques

Food consumption patterns have undergone changes that in recent years have resulted in serious health problems. Studies based on the evaluation of the nutritional status have determined that the adoption of a food pattern-based primarily on a Mediterranean diet (MD) has a preventive role, as well as the ability to mitigate the negative effects of certain pathologies. A group of more than 500 adults aged over 40 years from our cohort in Northwestern Spain was surveyed. Under our experimental design, 10 experiments were run with four different machine-learning algorithms and the predictive factors most relevant to the adherence of a MD were identified. A feature selection approach was explored and under a null hypothesis test, it was concluded that only 16 measures were of relevance, suggesting the strength of this observational study. Our findings indicate that the following factors have the highest predictive value in terms of the degree of adherence to the MD: basal metabolic rate, mini nutritional assessment questionnaire total score, weight, height, bone density, waist-hip ratio, smoking habits, age, EDI-OD, circumference of the arm, activity metabolism, subscapular skinfold, subscapular circumference in cm, circumference of the waist, circumference of the calf and brachial area.

Familiarity acts as a reduction in objective complexity

High-complexity stimuli are thought to place extra demands on working memory when processing and manipulating such stimuli; however, operational definitions of complexity are not well established, nor are the measures that would demonstrate such effects. Here, we argue that complexity is a relative quantity that is affected by preexisting experience. Experiment 1 compared cued-recall performance for Chinese and English speakers when the stimuli involved Chinese features that varied in the number of strokes or involved Ethiopic features unfamiliar to both groups. Chinese pseudocharacters (two radicals) had half the strokes of Chinese pseudowords (two characters). The response terms were English words familiar to both groups. English speakers performed equivalently with the Ethiopic and pseudocharacters, but much worse on the pseudowords. In contrast, Chinese speakers performed equivalently with pseudowords or pseudocharacters, but worse with Ethiopic cues. Experiment 2 showed that the lack of a complexity effect for Chinese speakers was not due to greater ease of rehearsal of pseudowords compared with pseudocharacters. Experiment 3 ruled out that Chinese speakers are just better at learning paired associates involving Mandarin by demonstrating that while complexity did not affect them, other features of the stimuli did. Taken together, it appears that complexity is not an absolute property based on the number of visual elements, but rather a relative property affected by one's prior knowledge.

Predicting human complexity perception of real-world scenes

Perceptual load is a well-established determinant of attentional engagement in a task. So far, perceptual load has typically been manipulated by increasing either the number of task-relevant items or the perceptual processing demand (e.g. conjunction versus feature tasks). The tasks used often involved rather simple visual displays (e.g. letters or single objects). How can perceptual load be operationalized for richer, real-world images? A promising proxy is the visual complexity of an image. However, current predictive models for visual complexity have limited applicability to diverse real-world images. Here we modelled visual complexity using a deep convolutional neural network (CNN) trained to learn perceived ratings of visual complexity. We presented 53 observers with 4000 images from the PASCAL VOC dataset, obtaining 75 020 2-alternative forced choice paired comparisons across observers. Image visual complexity scores were obtained using the TrueSkill algorithm. A CNN with weights pre-trained on an object recognition task predicted complexity ratings with r = 0.83. By contrast, feature-based models used in the literature, working on image statistics such as entropy, edge density and JPEG compression ratio, only achieved r = 0.70. Thus, our model offers a promising method to quantify the perceptual load of real-world scenes through visual complexity.

Comparison of Outlier-Tolerant Models for Measuring Visual Complexity

Providing the visual complexity of an image in terms of impact or aesthetic preference can be of great applicability in areas such as psychology or marketing. To this end, certain areas such as Computer Vision have focused on identifying features and computational models that allow for satisfactory results. This paper studies the application of recent ML models using input images evaluated by humans and characterized by features related to visual complexity. According to the experiments carried out, it was confirmed that one of these methods, Correlation by Genetic Search (CGS), based on the search for minimum sets of features that maximize the correlation of the model with respect to the input data, predicted human ratings of image visual complexity better than any other model referenced to date in terms of correlation, RMSE or minimum number of features required by the model. In addition, the variability of these terms were studied eliminating images considered as outliers in previous studies, observing the robustness of the method when selecting the most important variables to make the prediction.

Visual processing speed in hemianopia patients secondary to acquired brain injury: a new assessment methodology

Background

There is a clinical need to identify diagnostic parameters that objectively quantify and monitor the effective visual ability of patients with homonymous visual field defects (HVFDs). Visual processing speed (VPS) is an objective measure of visual ability. It is the reaction time (RT) needed to correctly search and/or reach for a visual stimulus. VPS depends on six main brain processing systems: auditory-cognitive, attentional, working memory, visuocognitive, visuomotor, and executive. We designed a new assessment methodology capable of activating these six systems and measuring RTs to determine the VPS of patients with HVFDs.

Methods

New software was designed for assessing subject visual stimulus search and reach times (S-RT and R-RT respectively), measured in seconds. Thirty-two different everyday visual stimuli were divided in four complexity groups that were presented along 8 radial visual field positions at three different eccentricities (10^o, 20^o, and 30^o). Thus, for each HVFD and control subject, 96 S- and R-RT measures related to VPS were registered. Three additional variables were measured to gather objective data on the validity of the test: eye-hand coordination mistakes (ehcM), eye-hand coordination accuracy (ehcA), and degrees of head movement (dHM, measured by a head-tracker system). HVFD patients and healthy controls (30 each) matched by age and gender were included. Each subject was assessed in a single visit. VPS measurements for HFVD patients and control subjects were compared for the complete test, for each stimulus complexity group, and for each eccentricity.

Results

VPS was significantly slower (p < 0.0001) in the HVFD group for the complete test, each stimulus complexity group, and each eccentricity. For the complete test, the VPS of the HVFD patients was 73.0% slower than controls. They also had 335.6% more ehcMs, 41.3% worse ehcA, and 189.0% more dHMs than the controls.

Conclusions

Measurement of VPS by this new assessment methodology could be an effective tool for objectively quantifying the visual ability of HVFD patients. Future research should evaluate the effectiveness of this novel method for measuring the impact that any specific neurovisual rehabilitation program has for these patients.

Algorithm Selection for Edge Detection in Satellite Images by Neutrosophic WASPAS Method

Nowadays, integrated land management is generally governed by the principles of sustainability. Land use management usually is grounded in satellite image information. The detection and monitoring of areas of interest in satellite images is a difficult task. We propose a new methodology for the adaptive selection of edge detection algorithms using visual features of satellite images and the multi-criteria decision-making (MCDM) method. It is not trivial to select the most appropriate method for the chosen satellite images as there is no proper algorithm for all cases as it depends on many factors, like acquisition and content of the raster images, visual features of real-world images, and humans’ visual perception. The edge detection algorithms were ranked according to their suitability for the appropriate satellite images using the neutrosophic weighted aggregated sum product assessment (WASPAS) method. The results obtained using the created methodology were verified with results acquired in an alternative way—using the edge detection algorithms for specific images. This methodology facilitates the selection of a proper edge detector for the chosen image content.

Visual complexity modelling based on image features fusion of multiple kernels

Humans' perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind. For these reasons, creating accurate computational models of visual complexity is a demanding task. Building upon on previous work in the field (Forsythe et al., 2011; Machado et al., 2015) we explore the use of Machine Learning techniques to create computational models of visual complexity. For that purpose, we use a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf's law. In an initial stage, a comparative analysis of representative state-of-the-art Machine Learning approaches is performed. Subsequently, we conduct an exhaustive outlier analysis. We analyze the impact of removing the extreme outliers, concluding that Feature Selection Multiple Kernel Learning obtains the best results, yielding an average correlation to humans' perception of complexity of 0.71 with only twenty-two features. These results outperform the current state-of-the-art, showing the potential of this technique for regression.

Fragmented ambiguous objects: Stimuli with stable low-level features for object recognition tasks

Visual object recognition is a complex skill that relies on the interaction of many spatially distinct and specialized visual areas in the human brain. One tool that can help us better understand these specializations and interactions is a set of visual stimuli that do not differ along low-level dimensions (e.g., orientation, contrast) but do differ along high-level dimensions, such as whether a real-world object can be detected. The present work creates a set of line segment-based images that are matched for luminance, contrast, and orientation distribution (both for single elements and for pair-wise combinations) but result in a range of object and non-object percepts. Image generation started with images of isolated objects taken from publicly available databases and then progressed through 3-stages: a computer algorithm generating 718 candidate images, expert observers selecting 217 for further consideration, and naïve observers performing final ratings. This process identified a set of 100 images that all have the same low-level properties but cover a range of recognizability (proportion of naïve observers (N = 120) who indicated that the stimulus "contained a known object") and semantic stability (consistency across the categories of living, non-living/manipulable, and non-living/non-manipulable when the same observers named "known" objects). Stimuli are available at https://github.com/caolman/FAOT.git.

The Complex Affective Scene Set (COMPASS): Solving the Social Content Problem in Affective Visual Stimulus Sets

Social information, including faces and human bodies, holds special status in visual perception generally, and in visual processing of complex arrays such as real-world scenes specifically. To date, unbalanced representation of social compared with nonsocial information in affective stimulus sets has limited the clear determination of effects as attributable to, or independent of, social content. We present the Complex Affective Scene Set (COMPASS), a set of 150 social and 150 nonsocial naturalistic affective scenes that are balanced across valence and arousal dimensions. Participants (n = 847) rated valence and arousal for each scene. The normative ratings for the 300 images together, and separately by social content, show the canonical boomerang shape that confirms coverage of much of the affective circumplex. COMPASS adds uniquely to existing visual stimulus sets by balancing social content across affect dimensions, thereby eliminating a potentially major confound across affect categories (i.e., combinations of valence and arousal). The robust special status of social information persisted even after balancing of affect categories and was observed in slower rating response times for social versus nonsocial stimuli. The COMPASS images also match the complexity of real-world environments by incorporating stimulus competition within each scene. Together, these attributes facilitate the use of the stimulus set in particular for disambiguating the effects of affect and social content for a range of research questions and populations.

Representations of naturalistic stimulus complexity in early and associative visual and auditory cortices

The complexity of sensory stimuli has an important role in perception and cognition. However, its neural representation is not well understood. Here, we characterize the representations of naturalistic visual and auditory stimulus complexity in early and associative visual and auditory cortices. This is realized by means of encoding and decoding analyses of two fMRI datasets in the visual and auditory modalities. Our results implicate most early and some associative sensory areas in representing the complexity of naturalistic sensory stimuli. For example, parahippocampal place area, which was previously shown to represent scene features, is shown to also represent scene complexity. Similarly, posterior regions of superior temporal gyrus and superior temporal sulcus, which were previously shown to represent syntactic (language) complexity, are shown to also represent music (auditory) complexity. Furthermore, our results suggest the existence of gradients in sensitivity to naturalistic sensory stimulus complexity in these areas.

Visual Complexity and Affect: Ratings Reflect More Than Meets the Eye

Pictorial stimuli can vary on many dimensions, several aspects of which are captured by the term 'visual complexity.' Visual complexity can be described as, "a picture of a few objects, colors, or structures would be less complex than a very colorful picture of many objects that is composed of several components." Prior studies have reported a relationship between affect and visual complexity, where complex pictures are rated as more pleasant and arousing. However, a relationship in the opposite direction, an effect of affect on visual complexity, is also possible; emotional arousal and valence are known to influence selective attention and visual processing. In a series of experiments, we found that ratings of visual complexity correlated with affective ratings, and independently also with computational measures of visual complexity. These computational measures did not correlate with affect, suggesting that complexity ratings are separately related to distinct factors. We investigated the relationship between affect and ratings of visual complexity, finding an 'arousal-complexity bias' to be a robust phenomenon. Moreover, we found this bias could be attenuated when explicitly indicated but did not correlate with inter-individual difference measures of affective processing, and was largely unrelated to cognitive and eyetracking measures. Taken together, the arousal-complexity bias seems to be caused by a relationship between arousal and visual processing as it has been described for the greater vividness of arousing pictures. The described arousal-complexity bias is also of relevance from an experimental perspective because visual complexity is often considered a variable to control for when using pictorial stimuli.

Predicting perceived visual complexity of abstract patterns using computational measures: The influence of mirror symmetry on complexity perception

Visual complexity is relevant for many areas ranging from improving usability of technical displays or websites up to understanding aesthetic experiences. Therefore, many attempts have been made to relate objective properties of images to perceived complexity in artworks and other images. It has been argued that visual complexity is a multidimensional construct mainly consisting of two dimensions: A quantitative dimension that increases complexity through number of elements, and a structural dimension representing order negatively related to complexity. The objective of this work is to study human perception of visual complexity utilizing two large independent sets of abstract patterns. A wide range of computational measures of complexity was calculated, further combined using linear models as well as machine learning (random forests), and compared with data from human evaluations. Our results confirm the adequacy of existing two-factor models of perceived visual complexity consisting of a quantitative and a structural factor (in our case mirror symmetry) for both of our stimulus sets. In addition, a non-linear transformation of mirror symmetry giving more influence to small deviations from symmetry greatly increased explained variance. Thus, we again demonstrate the multidimensional nature of human complexity perception and present comprehensive quantitative models of the visual complexity of abstract patterns, which might be useful for future experiments and applications.

Revisiting Rossion and Pourtois with new ratings for automated complexity, familiarity, beauty, and encounter

Differences between norm ratings collected when participants are asked to consider more than one picture characteristic are contrasted with the traditional methodological approaches of collecting ratings separately for image constructs. We present data that suggest that reporting normative data, based on methodological procedures that ask participants to consider multiple image constructs simultaneously, could potentially confounded norm data. We provide data for two new image constructs, beauty and the extent to which participants encountered the stimuli in their everyday lives. Analysis of this data suggests that familiarity and encounter are tapping different image constructs. The extent to which an observer encounters an object predicts human judgments of visual complexity. Encountering an image was also found to be an important predictor of beauty, but familiarity with that image was not. Taken together, these results suggest that continuing to collect complexity measures from human judgments is a pointless exercise. Automated measures are more reliable and valid measures, which are demonstrated here as predicting human preferences.