Aesthetic Assessment Research Articles

A coupled-lines system to determine the anteroposterior position of maxillary central incisors for smiling profile esthetics.

To develop a coupled-lines system to determine the anteroposterior position of maxillary central incisors (U1) for smiling profile esthetics. Thirty Asian females with ordinary and good facial harmony were selected as the study sample and the control sample, respectively. Three-dimensional facial images and 45°- and 90°-angled profiles were collected. The anteroposterior relationships between U1 and upper- and mid-facial soft tissue landmarks were measured. By morphing photos of the study sample, two artificial images were created to represent the well-balanced 45°- and 90°-angled profiles and were further processed with combined variations of soft tissue subnasale (SSn)-Glabella and the mid-point of facial axial points of the bilateral central incisor (mFA)-SSn distances. Esthetic assessments were performed on these images by layperson (n = 94) and orthodontist (n = 94) raters. Both upper- and mid-facial soft tissue landmarks were indispensable in assessing anteroposterior positions of U1 for well-balanced smiling profiles. As assessed in 45°- and 90°-angled profiles, the most esthetically sensitive parameters were mFA-Glabella and mFA-SSn distances. A coupled-lines system was constructed, comprising the Glabella and SSn vertical lines. In smiling profiles with optimal esthetics, the mFA point was at 2 to 5 mm posterior to the Glabella vertical and concomitantly 4 to 7 mm posterior to the SSn vertical, as perceived by orthodontists. Laypersons gave a wider range for mFA-Glabella distances, at 2 to 6 mm. The coupled-lines system could serve as a reliable reference for determining esthetically optimal anteroposterior positions of U1 for female facial profiles.

Vertical soft tissue augmentation to treat implant esthetic complications: A prospective clinical and volumetric case series.

Challenging implant esthetic complications are often characterized by implant malpositioning and interproximal attachment loss of the adjacent teeth. However, limited evidence is available on the treatment of these conditions. The aim of this study was to evaluate the clinical, volumetric, and patient-reported outcome following treatment of peri-implant soft tissue dehiscences (PSTDs) exhibiting interproximal attachment loss on adjacent teeth, performed through vertical soft tissue augmentation with implant submersion. Ten subjects with isolated PSTD in the anterior maxilla characterized by adjacent dentition exhibiting interproximal attachment loss were consecutively enrolled and treated with horizontal and vertical soft tissue augmentation, involving crown and abutment removal, two connective tissue grafts, and submerge healing. Clinical outcomes of interest included mean PSTD coverage, mean PSTD reduction, clinical attachment level (CAL) gain at the implant and adjacent sites and soft tissue phenotype modifications at 1 year. Optical scanning was used for assessing volumetric changes. Professional assessment of esthetic outcomes was performed using the Implant Dehiscence coverage Esthetic Score (IDES), while patient-reported esthetic assessment involved a 0-10 visual analogue scale. The mean PSTD depth reduction and mean PSTD coverage at 1 year were 2.25 mm, and 85.14%, respectively. A mean keratinized tissue width (KTW) gain of 1.15 mm was observed, while the mean gain in mucosal thickness (MT) was 1.58 mm. A mean CAL gain of 1.45 mm was obtained at the interproximal aspect of the adjacent dentition at 1 year. Greater linear dimensional (LD) changes were observed at the midfacial aspect of the implant compared to the interproximal sites. The mean final IDES was 6.90 points, while patient-reported esthetic evaluation was 8.83 points. The present study demonstrated that vertical soft tissue augmentation with a submerged healing is an effective treatment approach for the treatment of challenging PSTDs with adjacent dentition exhibiting interproximal attachment loss. This technique can be effective in resolution of esthetic complications in most cases, providing a substantial gain in interproximal attachment levels at the adjacent dentition.

Pre-Activating Semantic Information for Image Aesthetic Assessment

Automatic image aesthetic evaluation is an attractive and challenging visual task. Recently, methods based on convolutional neural networks have achieved remarkable performance. However, semantic information, an intuitive prerequisite for evaluating image aesthetics, has not received enough attention regarding its importance in previous methods. How to efficiently extract semantic information and make better use of it to assist the aesthetic evaluation task remains unsolved. In this article, we propose to utilize the self-supervised model Auto-Encoder to extract semantic information in the form of multi-task learning. Then, a fusing module is prepended at the bottleneck layer to explicitly combine semantic information with aesthetic information in a pre-activated manner. Specifically, we implement a customized pooling operation to pool the semantic features extracted by Auto-Encoder and apply a weak constraint between the pooled semantic features and aesthetic information to realize the combination. The following regressor can complete aesthetic evaluation based on the semantic–aesthetic combined features. In addition, to enable our model to adapt to arbitrary aspect ratios of images, another pooling strategy called spatial pyramid pooling is adopted to obtain the image features of a fixed length. Our method achieves competitive performance on the public image aesthetic evaluation benchmark. Especially on the most commonly used metric Spearman rank-order correlation coefficient, the proposed model achieved the best performance compared with some state-of-the-art methods. Extensive ablation studies and visualization experiments were conducted to demonstrate the effectiveness of our method.

IAACS: Image aesthetic assessment through color composition and space formation

BackgroundDetermining how an image is visually appealing is a complicated and subjective task. This motivates the use of a machine-learning model to evaluate image aesthetics automatically by matching the aesthetics of the general public. Although deep learning methods have successfully learned good visual features from images, correctly assessing the aesthetic quality of an image remains a challenge for deep learning. MethodsTo address this, we propose a novel multiview convolutional neural network to assess image aesthetics assessment through color composition and space formation (IAACS). Specifically, from different views of an image––including its key color components and their contributions, the image space formation, and the image itself––our network extracts the corresponding features through our proposed feature extraction module (FET) and the ImageNet weight-based classification model. ResultBy fusing the extracted features, our network produces an accurate prediction score distribution for image aesthetics. The experimental results show that we have achieved superior performance.

Building CNN-Based Models for Image Aesthetic Score Prediction Using an Ensemble

In this paper, we propose a framework that constructs two types of image aesthetic assessment (IAA) models with different CNN architectures and improves the performance of image aesthetic score (AS) prediction by the ensemble. Moreover, the attention regions of the models to the images are extracted to analyze the consistency with the subjects in the images. The experimental results verify that the proposed method is effective for improving the AS prediction. The average F1 of the ensemble improves 5.4% over the model of type A, and 33.1% over the model of type B. Moreover, it is found that the AS classification models trained on the XiheAA dataset seem to learn the latent photography principles, although it cannot be said that they learn the aesthetic sense.

Aesthetic Assessment of Packaging Design Based on Con-Transformer

Different from the traditional natural images' aesthetic assessment task, the aesthetic assessment of packaging design should not only pay attention to artistic beauty, but also pay attention to functional beauty, that is, the attraction of the packaging design to consumers. In this paper, the authors propose a con-transformer packaging design aesthetic assessment method, which takes advantage of convolutional operations and self-attention mechanisms for enhanced representation learning, resulting in an effective aesthetic assessment of the packaging design images. Specifically, con-transformer integrates convolution network branch and transformer network branch to extract local representation features and global representation features of the packaging design images respectively. Finally, the fused representation features are used for aesthetic assessment. Experimental results show that the proposed method can not only effectively assess the aesthetic of packaging design images, but also be applied to the aesthetic assessment of natural images.

User-Guided Personalized Image Aesthetic Assessment Based on Deep Reinforcement Learning

Personalized image aesthetic assessment (PIAA) has recently become a hot topic due to its usefulness in a wide variety of applications such as photography, film and television, e-commerce, fashion design and so on. This task is more seriously affected by subjective factors and samples provided by users. In order to acquire precise personalized aesthetic distribution by small amount of samples, we propose a novel user-guided personalized image aesthetic assessment framework. This framework leverages user interactions to retouch and rank images for aesthetic assessment based on deep reinforcement learning (DRL), and generates personalized aesthetic distribution that is more in line with the aesthetic preferences of different users. It mainly consists of two stages. In the first stage, personalized aesthetic ranking is generated by interactive image enhancement and manual ranking, meanwhile two policy networks will be trained. The images will be pushed to the user for manual retouching and simultaneously to the enhancement policy network. The enhancement network utilizes the manual retouching results as the optimization goals of DRL. After that, the ranking process performs the similar operations like the retouching mentioned before. These two networks will be trained iteratively and alternatively to help to complete the final personalized aesthetic assessment automatically. In the second stage, these modified images are labeled with aesthetic attributes by one style-specific classifier, and then the personalized aesthetic distribution is generated based on the multiple aesthetic attributes of these images, which conforms to the aesthetic preference of users better.

ОЦЕНКА ЭСТЕТИЧЕСКОГО РЕЗУЛЬТАТА ПРОТЕЗИРОВАНИЯ НА ОДИНОЧНОМ ДЕНТАЛЬНОМ ИМПЛАНТАТЕ

Objective: A comparative assessment of the aesthetic result of a single dental implant (DI) prosthetics using standard or custom abutments. Methods: A customized gingival former and a custom abutment were applied in patients of the main group, while patients of the control group received a standard gingival former and a stock abutment. The aesthetic assessment was carried out on photographs using the Pink Esthetic Score (PES) and White Esthetic Score (WES) scales. Results: Better aesthetic results were obtained in the main group compared to the control one. At subsequent stages of the study, a further aesthetical improvement was observed compared with the initial assessment result. Conclusion: The formation of a marginal gingiva matching the anatomical shape of the neck of the missing tooth enables to fabricate a customized abutment and a dental crown of the correct anatomical shape. The physiological dimensions and the correct eruption profile of the prosthetic crown contribute to the formation of interdental papillae where they were originally absent. The listed advantages of this method improve the RES and PES results of prosthetics on a single-unit DI. Keywords: Prosthetics on dental implants, crown on a single-unit dental implant, custom abutment, gingival papilla, pink aesthetics, white aesthetics.

A Theoretical Insight Into the Effect of Loss Function for Deep Semantic-Preserving Learning.

Good generalization performance is the fundamental goal of any machine learning algorithm. Using the uniform stability concept, this article theoretically proves that the choice of loss function impacts the generalization performance of a trained deep neural network (DNN). The adopted stability-based framework provides an effective tool for comparing the generalization error bound with respect to the utilized loss function. The main result of our analysis is that using an effective loss function makes stochastic gradient descent more stable which consequently leads to the tighter generalization error bound, and so better generalization performance. To validate our analysis, we study learning problems in which the classes are semantically correlated. To capture this semantic similarity of neighboring classes, we adopt the well-known semantics-preserving learning framework, namely label distribution learning (LDL). We propose two novel loss functions for the LDL framework and theoretically show that they provide stronger stability than the other widely used loss functions adopted for training DNNs. The experimental results on three applications with semantically correlated classes, including facial age estimation, head pose estimation, and image esthetic assessment, validate the theoretical insights gained by our analysis and demonstrate the usefulness of the proposed loss functions in practical applications.

Esthetic assessment of the ornamental forms of northern white cedar (Thuja occidentalis L.) and their use in garden and park compositions

Northern white cedar (Thuja occidentalis L.) is represented in the world by about 200 ornamental forms, 43 of which were discovered and described in landscaping and nurseries of Lviv. The first plantations in Lviv date back to the 1920s. They were introduced by the professor at the Medical Institute T. Vilchynskyi, who brought material for cuttings from a nursery in Kurnik (Poland). The decorative qualities of these cultivars were determined on the basis of characteristic features and evaluated in points: the shape and density of the crown, the color of the needles, the nature of branching, and the features of seed-bearing. These decorative qualities formed the basis for the construction of various elements of garden and park compositions with the participation of tapeworms, alleys, green walls, hedges, boskets, topiaries, and rock gardens. The highest number of points (12) was obtained by 46.4% of ornamental forms, 11 - 16.3%, 10 - 9.3%, and 8 -11.6%. Cultivars with a distinct regular crown shape received high evaluation points. The options for the spatial arrangement of individuals were proposed and the distance between seating positions was recommended. Taking into account the high plasticity of cultivars and the ability to form crowns, variants of clipped hedges and topiaries were recommended. Several models have been proposed based on the distribution of ornamental plant groups based on their symmetry, silhouettes, alignments, contrasts, colors, density, and sizes. Cultivars were selected for each variant, and their group arrangement was proposed.

Analysis of esthetic assessment scales in two surgical approaches for hypertrophic pyloric stenosis.

Hypertrophic pyloric stenosis (HPS) is a frequent pathology in neonates, with extramucosal pyloromyotomy being a healing surgery. It may be performed through a transverse subcostal incision (TSI) or a transumbilical incision (TUI). To compare complications, operating times, hospital stay, and esthetic results between both techniques. A retrospective, descriptive study of patients undergoing HPS surgery between January 2010 and January 2020 was carried out. Qualitative variables (sex and complications) were expressed as absolute frequency and percentage, whereas quantitative variables (age at surgery, operating time, hospital stay, and scar esthetic assessment scales: MVSS [Modified Vancouver Scar Scale] and P-SAS [Patient Scar Assessment Scale]) were expressed as median and interquartile range. 107 patients were analyzed: TSI (60.7%, n=65) vs. TUI (39.3%, n=42). Male patients: 89.2%, n=58 vs. 83.3%, n=35; age (days): 31 (24.5-39.5) vs. 34.5 (29.5-47.25); operating time (minutes): 41 (33.75-60) vs. 46 (38.5-60); and hospital stay (days): 2 (2-4) vs. 3 (2-3). Clavien-Dindo grade II complications were more frequent in the TUI Group (1.54%, n=1 vs. 23.81%, n=10; p<0.001), with most of them being wound infections. The opinion regarding the scar according to the MVSS scale was better in the TUI Group (1.5 [0-4] vs. 0 [0-2]; p=0.022). No significant differences were found in the P-SAS scale (10 (6-18) vs. 6 (6-9); p=0.060). TUI is preferred from an esthetic point of view, and even though surgical wound infections are more frequent, it is not associated with longer operating times, longer hospital stay, or severe complications.

Learning Personalized Image Aesthetics From Subjective and Objective Attributes

Due to the widespread popularity of social media, researchers have developed a strong interest in learning the personalized image aesthetics of online users. Personalized image aesthetics assessment (PIAA) aims to study the aesthetic preferences of individual users for images, which should be affected by the properties of both users and images. Existing PIAA approaches usually use the generic aesthetics learned from images as a prior model and adapt it to PIAA models through a small number of data annotated by individual users. However, the prior model merely learns the objective attributes of images, which is agnostic to the subjective attributes of users, complicating efficient learning of the personalized image aesthetics of individual users. Therefore, we propose a personalized image aesthetics assessment method that integrates the subjective attributes of users and objective attributes of images simultaneously. To characterize these two attributes jointly, an attribute extraction module is introduced to learn users’ personality traits and image aesthetic attributes. Then, an aesthetic prior model is built from numerous individual users’ annotated data, which leverages the personality traits of users and the aesthetic attributes of rated images as prior knowledge to model both the image aesthetic distribution and users’ residual scores relative to generic aesthetics simultaneously. Finally, a PIAA model is obtained by fine-tuning the aesthetic prior model with an individual user’s annotated data. Experiments demonstrate that the proposed method is superior to existing PIAA methods in learning individual users’ personalized image aesthetics.

Обобщение и условность: к становлению профессионализма в художественном образовании

The article analyses aspects of the problem of the professional development of the artist, related to the implementation of creativity. The harmonious interaction between the original intention of the artist, purposeful perception, aesthetic assessment of the depicted and consistent visual actions is emphasized. The subject of the author’s comprehension is generalization and conventionality.

Interaction-Matrix Based Personalized Image Aesthetics Assessment

Personalized image aesthetics assessment (IAA) aims to estimate aesthetic experiences subject to the preferences of individual users, contrary to generic IAA that estimates aesthetic experiences subject to average preferences. Most existing personalized IAA methods treat personalized aesthetic experiences as deviations from a generic aesthetic experience, and therefore, personalized IAA models are designed to build upon the prior knowledge on generic IAA. However, we propose that acquiring knowledge on generic IAA is not necessary for building a personalized IAA model. Instead of modeling personalized IAA on the basis of generic IAA, this work proposes to directly estimate personalized aesthetic experiences from the interactions between image contents and user preferences (i.e., preference-content interaction), where interaction-matrices representing preference-content interactions are constructed without needs for prior generic IAA knowledge. To this end, we construct interaction-matrices from content features constructed from pre-trained image classification features and latent preference features. To realize a robust interaction-matrix based personalized IAA model, we discuss in detail on different strategies for constructing interaction-matrices and estimating personalized aesthetic scores from the interaction-matrices. Besides the personalized IAA scenario, we further propose strategies to adapt the proposed personalized IAA model to different scenarios of generic IAA. Extensive experiments show that: 1) our method significantly outperforms 5 previous relevant personalized IAA methods on FLICKR-AES dataset, especially the methods that require generic IAA knowledge as the basis; 2) in terms of generic IAA, the proposed approach also outperforms 13 generic IAA methods on AVA dataset.

Composition-Guided Neural Network for Image Cropping Aesthetic Assessment

How to explore the interaction between image aesthetic rules and crops is the key to finding views with good composition. Besides, it is subjective to evaluate candidate crops, which mainly depends on aesthetic knowledge, but it is not an easy task for people without extensive photography experience. However, existing methods mostly find good views by extracting general aesthetic features of crops without fully exploring the aesthetic rules. Motivated by this, we innovatively propose a composition-guided image cropping aesthetic assessment network (CGICAANet) for efficiently finding good crops and optimizing the cropping operation. Specifically, we adopt a direct and comprehensive composition pattern module, which adaptively mines suitable compositions for the images and emphasizes the dominant position of visual elements to contribute to optimizing the best crops in an interpretable way. Moreover, we designed a multi-task loss function to train the model. Particularly, to explore the commonality between predicted crops and labels, the complete intersection-over-union loss is adopted thoroughly considering the overlap area, central point distance and the consistency of aspect ratios for crops concurrently. Therefore, the predicted best crop can preserve the visual elements and have better composition. Experimental results with lightweight MobileNetV2 and ShuffleNetV2 as backbone networks demonstrate that our method can obtain comparable or better performance in terms of efficiency and accuracy.

Deep Order-Preserving Learning With Adaptive Optimal Transport Distance.

We consider a framework for taking into consideration the relative importance (ordinality) of object labels in the process of learning a label predictor function. The commonly used loss functions are not well matched to this problem, as they exhibit deficiencies in capturing natural correlations of the labels and the corresponding data. We propose to incorporate such correlations into our learning algorithm using an optimal transport formulation. Our approach is to learn the ground metric, which is partly involved in forming the optimal transport distance, by leveraging ordinality as a general form of side information in its formulation. Based on this idea, we then develop a novel loss function for training deep neural networks. A highly efficient alternating learning method is then devised to alternatively optimise the ground metric and the deep model in an end-to-end learning manner. This scheme allows us to adaptively adjust the shape of the ground metric, and consequently the shape of the loss function for each application. We back up our approach by theoretical analysis and verify the performance of our proposed scheme by applying it to two learning tasks, i.e. chronological age estimation from the face and image aesthetic assessment. The numerical results on several benchmark datasets demonstrate the superiority of the proposed algorithm.

Research progress on asymmetry of mandibular ramus and condyle of different skeletal patterns

Asymmetry of the face and dental arch is a natural phenomenon. Symmetry can be defined as an equal or corresponding distribution at two extremes, poles or on both sides of the body, in the form of a distribution around a center or an axis. Previous studies have shown that different skeletal patterns have a significant effect on mandibular condyle morphology, and that the jaws are asymmetrical in function and movement, indicating that the left and right sides of the mandible develop differently. Therefore, Symmetry assessment is important in aesthetic assessment of any craniofacial region as well as in assessment of malocclusion.

Probabilistic Modeling of Image Aesthetic Assessment Toward Measuring Subjectivity

Probabilistic Modeling of Image Aesthetic Assessment Toward Measuring Subjectivity

Filastruder

The article is devoted to the development of an economically and technically efficient device for the processing of plastic waste arising from the 3D printing process. The prototypes of the developed filastruder model are considered and the advantages and disadvantages of existing analogues are considered. A technological map, a block diagram of the developed device and its control program code are considered. The ecological and aesthetic assessments of the device are given. Keywords electronics, 3D printing, filament, filastruder, block diagram, technology chart, prototype, model

Hard and soft tissue evaluation of alveolar ridge preservation compared to spontaneous healing: a retrospective clinical and volumetric analysis

The remodeling process following tooth extraction can be observed as horizontal and vertical bone reduction of the alveolar ridge. Preservation procedures such as alveolar ridge preservation (ARP) aim to maintain the 3D volume of the extraction site. This retrospective study analyzed differences in the hard and soft tissue changes in patients treated with either spontaneous healing or ARP. After tooth extraction, the patients were treated either by spontaneous socket healing (SH group) or with ARP using a xenograft and a resorbable membrane (ARP group). One week before and 6months after extraction, the patients underwent cone beam computed tomography. A volumetric analysis was performed by superimposing the digital models of the two time points. Intraoral radiography was performed after implant placement, upon prosthesis delivery, and at 1-year post-treatment. An esthetic assessment was conducted using the Pink Esthetic Score (PES). The patients' overall satisfaction with the implant restoration was investigated at 12months. Intragroup comparisons revealed significant differences between baseline and the 6-month follow-up in both groups at the measured locations (1mm, 3mm, and 5mm below the most coronal aspect of the alveolar ridge) showing a reduction of the horizontal width (P < 0.05). Additionally, after treatment, the horizontal width at 1mm was significantly different in the SH and ARP groups (P < 0.001), with mean changes of 2.03 ± 0.54mm and 0.86 ± 0.49mm, respectively. ARP was associated with an increased PES (11.6 ± 2.2) and a reduction in patients requiring additional grafting procedures in subsequent treatment phases (9% vs 26%; P = 0.11). In both groups, significant horizontal and vertical bone loss was observed after the extraction. ARP can reduce linear and volumetric shrinkage of the alveolar ridge, leading to improved outcomes. It can also simplify implant restoration.