Search Results (77)

In recent decades, multiple approaches have emerged to assess architectural visual character, including fractal dimension analysis, visual equilibrium calculations, and visual preference surveys. However, the relationships among these methods and their alignment with subjective perception remain unclear. This study applies all three techniques to sample mosques in Riyadh, Saudi Arabia, to evaluate their validity and interconnections. Findings reveal a within-sample tendency toward low visual complexity, with fractal dimensions ranging from 1.2 to 1.547. Within this small, exploratory sample of five large main-road mosques in Riyadh, correlations between computed visual equilibrium and survey results provide preliminary, sample-specific convergent-validity evidence for Larrosa’s visual-forces method, rather than general validation. Within this sample, traditional façades with separate minarets tended to score as more visually balanced than more contemporary compositions. This triangulated approach offers an exploratory framework for architectural visual assessment that integrates objective metrics with human perception. Full article

With the rapid advancement of technology in recent years, signatures on contracts and documents have increasingly shifted from traditional handwritten forms on paper to digital handwritten signatures executed on devices (hereafter referred to as digital tablets). This transition introduces new challenges for forensic document examination due to the differences in writing instruments. According to the European Network of Forensic Science Institutes (ENFSI), a Digital Capture Signature (DCS) refers to data points captured during the writing process on digital devices such as tablets, smartphones, or signature pads. In addition to retaining the visual image of the signature, DCS provides more information previously unavailable, including pen pressure, stroke order, and writing speed. These features possess potential forensic value and warrant further study and evaluation. This study employs three devices—Samsung Galaxy Tab S10, Apple iPad Pro, and Apple iPad Mini—together with their respective styluses as experimental tools. Using custom-developed handwriting capture software for both Android and iOS platforms, we simulated signature-writing scenarios common in the financial and insurance industries. Thirty participants were asked to provide samples of horizontal Chinese, English, and number writings (FUJ-IRB NO: C113187), which were subsequently normalized and segmented into characters. For analysis, we adopted distance-based time-series alignment algorithms (FastDTW and SC-DTW) to match writing data across different instances (intra- and inter-writer). The accumulated distances between corresponding data points, such as coordinates and pressure, were used to assess handwriting stability and to study the differences between same-writer and different-writer samples. The findings indicate that preprocessing through character centroid alignment, followed by the analysis, substantially reduces the average accumulated distance of handwriting. This procedure quantifies the stability of an individual’s handwriting and enables differentiation between same-writer and different-writer scenarios based on the distribution of DCS distances. Furthermore, the use of styluses provides more precise distinctions between same- and different-writer samples compared with direct finger-based writing. In the context of rapid advancements in artificial intelligence and emerging technologies, this preliminary study aims to contribute foundational insights into the forensic application of digital signature examination. Full article

Automobile license plate image generation represents a pivotal technology for the development of intelligent transportation systems. However, existing methods are constrained by their inability to simultaneously preserve geometric structure and radiometric properties of both license plates and characters. To overcome this limitation, we propose a novel framework for generating geometrically and radiometrically consistent license plate images. The proposed radiometric enhancement framework integrates two specialized modules, which are precise geometric rectification and radiometric property learning. The precise geometric rectification module exploits the perspective transformation consistency between character regions and license plate boundaries. By employing a feature matching algorithm based on character endpoint correspondence, this module achieves precise plate rectification, thereby establishing a geometric foundation for maintaining character structural integrity in generated images. The radiometric property learning module implements a precise character inpainting strategy with fluctuation compensation inpainting to reconstruct background regions, followed by a character-wise style transfer approach to ensure both geometric and radiometric consistency with realistic automobile license plates. Furthermore, we introduce a physical validation and evaluation method to quantitatively assess image quality. Comprehensive evaluation on real-world datasets demonstrate that our method achieves superior performance, with a peak signal-to-noise ratio (PSNR) of 13.83 dB and a structural similarity index measure (SSIM) of 0.57, representing significant improvements over comparative methods in preserving both structural integrity and radiometric properties. This framework effectively enhances the visual fidelity and reliability of generated automobile license plate images, thereby providing high-quality data for intelligent transportation recognition systems while advancing license plate image generation technology. Full article

This study examined how beginners benefit from ‘salience’ in learning two types of visually similar Chinese characters: those with identical strokes (e.g., 人 and 入) and those differing by an additional stroke (e.g., 日 and 白), while identifying the role of color coding and presentation style. A total of 183 non-tonal native speakers with no prior experience of Chinese characters participated in the study. In a 2 × 2 × 2 experimental design, the study assessed the influence of color coding (with vs. without), presentation style (single vs. paired characters), and stroke similarity (identical vs. different) on learning. Results showed (1) Characters with stroke differences were learned more easily than identical-stroke characters; (2) Simultaneous character presentation enhanced discrimination of subtle stroke differences, but (3) Color coding slowed down reaction times, suggesting visual overload. These findings demonstrate that perceptual similarity—not just complexity—impacts character learning difficulty. Pedagogically, the results support using paired character presentation while cautioning against excessive visual enhancements. The study provides empirical evidence for optimizing Chinese character instruction by balancing discriminability and cognitive load in beginning learners. Full article

Ancient Chinese scripts—including oracle bone carvings, bronze inscriptions, stone steles, Dunhuang scrolls, and bamboo slips—are rich in historical value but often degraded due to centuries of erosion, damage, and stylistic variability. These issues severely hinder manual transcription and render conventional OCR techniques inadequate, as they are typically trained on modern printed or handwritten text and lack interpretability. To tackle these challenges, we propose FAIR-Net, a hybrid architecture that combines the unsupervised feature learning capacity of a deep autoencoder with the semantic transparency of a fuzzy rule-based classifier. In FAIR-Net, the deep autoencoder first compresses high-resolution character images into low-dimensional, noise-robust embeddings. These embeddings are then passed into a Fuzzy Neural Network (FNN), whose hidden layer leverages Fuzzy C-Means (FCM) clustering to model soft membership degrees and generate human-readable fuzzy rules. The output layer uses Iteratively Reweighted Least Squares Estimation (IRLSE) combined with a Softmax function to produce probabilistic predictions, with all weights constrained as linear mappings to maintain model transparency. We evaluate FAIR-Net on CASIA-HWDB1.0, HWDB1.1, and ICDAR 2013 CompetitionDB, where it achieves a recognition accuracy of 97.91%, significantly outperforming baseline CNNs (p < 0.01, Cohen’s d > 0.8) while maintaining the tightest confidence interval (96.88–98.94%) and lowest standard deviation (±1.03%). Additionally, FAIR-Net reduces inference time to 25 s, improving processing efficiency by 41.9% over AlexNet and up to 98.9% over CNN-Fujitsu, while preserving >97.5% accuracy across evaluations. To further assess generalization to historical scripts, FAIR-Net was tested on the Ancient Chinese Character Dataset (9233 classes; 979,907 images), achieving 83.25% accuracy—slightly higher than ResNet101 but 2.49% lower than SwinT-v2-small—while reducing training time by over 5.5× compared to transformer-based baselines. Fuzzy rule visualization confirms enhanced robustness to glyph ambiguities and erosion. Overall, FAIR-Net provides a practical, interpretable, and highly efficient solution for the digitization and preservation of ancient Chinese character corpora. Full article

Numbers present persistent challenges in interpreting, yet cognitive mechanisms underlying notation-specific processing remain underexplored. While eye-tracking studies in visually-assisted simultaneous interpreting have advanced number research, they predominantly examine Arabic numerals in non-Chinese contexts—neglecting notation diversity increasingly prevalent in computer-assisted interpreting systems where Automatic Speech Recognition outputs vary across languages. Addressing these gaps, this study investigated how number notation (Arabic digits vs. Chinese character numbers) affects trainee interpreters’ cognitive effort and performance in Chinese-to-English sight translation. Employing a mixed-methods design, we measured global (task-level) and local (number-specific) eye movements alongside expert assessments, output analysis, and subjective assessments. Results show that Chinese character numbers demand significantly greater cognitive effort than Arabic digits, evidenced by more and longer fixations, more extensive saccadic movements, and a larger eye-voice span. Concurrently, sight translation quality decreased markedly with Chinese character numbers, with more processing attempts yet lower accuracy and fluency. Subjective workload ratings confirmed higher mental, physical, and temporal demands in Task 2. These findings reveal an effort-quality paradox where greater cognitive investment in processing complex notations leads to poorer outcomes, and highlight the urgent need for notation-specific training strategies and adaptive technologies in multilingual communication. Full article

This paper presents the design and evaluation of a multisensory tangible game device aimed at promoting pre-Braille literacy in children with varying visual abilities, including those who are blind, partially sighted, and sighted. The prototype integrates tactile, auditory, and visual elements to provide an inclusive and engaging learning experience. The device combines educational content with game-based learning, allowing users to interact with Braille characters through touch while receiving auditory feedback and visual cues. A focus group evaluation was conducted to assess the prototype’s effectiveness, engagement, and educational value. Results indicated that the device successfully captured users’ attention, with 83% recognizing its potential as a valuable educational tool for teaching pre-Braille literacy, 92% of participants reporting high engagement, and 75% of participants agreeing with the serious game approach. Feedback also highlighted areas for improvement, including the need for clearer tactile differentiation and more adaptive learning features. This study demonstrates the potential of combining multisensory feedback and serious gaming to enhance literacy education in children with visual impairments and provides insights into the further development of inclusive educational technologies. Full article

Detecting manipulated document images is essential for verifying the authenticity of official records and preventing document forgery. However, forgery artifacts are often subtle and localized in fine-grained regions, such as text boundaries or character outlines, where visual symmetry and structural regularity are typically expected. These manipulations can disrupt the inherent symmetry of document layouts, making the detection of such inconsistencies crucial for forgery identification. Conventional CNN-based models face limitations in capturing such edge-level asymmetric features, as edge-related information tends to weaken through repeated convolution and pooling operations. To address this issue, this study proposes an edge-focused method composed of two components: the Edge Attention (EA) layer and the Edge Concatenation (EC) layer. The EA layer dynamically identifies channels that are highly responsive to edge features in the input feature map and applies learnable weights to emphasize them, enhancing the representation of boundary-related information, thereby emphasizing structurally significant boundaries. Subsequently, the EC layer extracts edge maps from the input image using the Sobel filter and concatenates them with the original feature maps along the channel dimension, allowing the model to explicitly incorporate edge information. To evaluate the effectiveness and compatibility of the proposed method, it was initially applied to a simple CNN architecture to isolate its impact. Subsequently, it was integrated into various widely used models, including DenseNet121, ResNet50, Vision Transformer (ViT), and a CAE-SVM-based document forgery detection model. Experiments were conducted on the DocTamper, Receipt, and MIDV-2020 datasets to assess classification accuracy and F1-score using both original and forged text images. Across all model architectures and datasets, the proposed EA–EC method consistently improved model performance, particularly by increasing sensitivity to asymmetric manipulations around text boundaries. These results demonstrate that the proposed edge-focused approach is not only effective but also highly adaptable, serving as a lightweight and modular extension that can be easily incorporated into existing deep learning-based document forgery detection frameworks. By reinforcing attention to structural inconsistencies often missed by standard convolutional networks, the proposed method provides a practical solution for enhancing the robustness and generalizability of forgery detection systems. Full article

Background/Objectives: Sentence stress as part of linguistic prosody plays an important role for verbal communication. It emphasizes particularly important words in a phrase and is reflected by acoustic cues such as the voice fundamental frequency. However, visual cues, especially facial movements, are also important for sentence stress perception. Since cochlear implant (CI) recipients are limited in their use of acoustic prosody cues, the question arises as to what extent they are able to exploit visual features. Methods: Virtual characters were used to provide highly realistic but controllable stimuli for investigating sentence stress in groups of experienced CI recipients and typical-hearing (TH) peers. In addition to the proportion of correctly identified stressed words, task load was assessed via reaction times (RTs) and task-evoked pupil dilation (TEPD), and visual attention was estimated via eye tracking. Experiment 1 considered congruent combinations of auditory and visual cues, while Experiment 2 presented incongruent stimuli. Results: In Experiment 1, CI users and TH participants performed similarly in the congruent audiovisual condition, while the former were better at using visual cues. RTs were generally faster in the AV condition, whereas TEPD revealed a more detailed picture, with TH subjects showing greater pupil dilation in the visual condition. The incongruent stimuli in Experiment 2 showed that modality use varied individually among CI recipients, while TH participants relied primarily on auditory cues. Conclusions: Visual cues are generally useful for perceiving sentence stress. As a group, CI users are better at using facial cues than their TH peers. However, CI users show individual differences in the reliability of the various cues. Full article

The East–West Mountain Region (EWMR) of Wuhan is a vital natural and cultdural asset, characterized by its scenic nature landscapes and rich historical and cultural heritage. This study aims to address the problems of landscape character degradation and weakened public preferences caused by rapid urbanization and proposes a research framework integrating landscape character assessment and public preferences. Initially, we utilize K-means cluster analysis to identify landscape character types based on six landscape elements, resulting in a landscape character map with 20 types. Subsequently, we employ emotion analysis based on Natural Language Processing (NLP) techniques to analyze user-generated content (UGC) from Weibo check-in data to establish perception characteristic indicators reflecting public preferences. Finally, we quantitatively identify the environmental factors influencing public preferences through the SoIVES model and compare and integrate the landscape character map with the public emotion value map. The results show that (1) public preferences hotspots are concentrated in three types: (a) urban construction-driven types, including areas dominated by commercial service functions and those characterized by mixed-function residential areas; (b) natural terrain-dominated types with well-developed supporting facilities; and (c) hybrid transition types predominated by educational and scientific research land uses. These areas generally feature a high degree of functional diversity and good transportation accessibility. (2) Landscapes eliciting stronger emotional responses integrate moderate slopes, multifunctional spaces, and robust public services, whereas areas with weaker responses are characterized by single-function use or excessive urbanization. (3) The emotional variations within categories could be influenced by (a) functional hybridity through enhanced environmental exploration; (b) spatial usage frequency through place attachment formation; and (c) visual harmony through cognitive overload prevention. These findings provide critical insights for formulating zoning optimization plans aimed at the refined conservation and utilization of urban landscape resources, as well as offering guidance for improving landscape planning and management in the EWMR. Full article

This study investigates the impact of hydroxyapatite (HA) nanoparticles (NPs) on the cellular responses of poly(L-lactide-co-ε-caprolactone) (PLCL) scaffolds in bone tissue engineering applications. Three types of PLCL scaffolds were fabricated, varying in HANPs content. Saos-2 osteoblast-like cells (OBs) and THP-1-derived osteoclast-like cells (OCs) were co-cultured on the scaffolds, and cell proliferation was assessed using the MTS assay. The amount of double-stranded DNA (dsDNA) was quantified to evaluate cell proliferation. Expression levels of OBs and OCs markers were analyzed via quantitative polymerase chain reaction (qPCR) and the production of Collagen type I was visualized using confocal microscopy. Additionally, enzymatic activity of alkaline phosphatase (ALP) and tartrate-resistant acid phosphatase (TRAP or ACP5) was measured to assess OB and OC function, respectively. Interestingly, despite the scaffold’s structured character supporting the growth of the Saos-2 OBs and THP-1-derived OCs coculture, the incorporation of HANPs did not significantly enhance cellular responses compared to scaffolds without HANPs, except for collagen type I production. These findings suggest the need for further investigation into the potential benefits of HANPs in bone tissue engineering applications. Nevertheless, our study contributes valuable insights into optimizing biomaterial design for bone tissue regeneration, with implications for drug screening and material testing protocols. Full article

Historic and cultural heritage districts, as physical carriers of a city’s cultural identity, have become key issues in urban development. Architectural color, as a core visual element of district character, is an important symbol of regional identity recognition. However, further exploration is needed regarding how to integrate architectural color quantification metrics and evaluation techniques into the urban characteristics management framework. In this paper, taking Tianjin’s historic cultural heritage districts as a case study, street view data were utilized, and deep learning along with clustering analysis methods were employed to extract architectural colors. Based on the “point-line-surface” protection strategy, a multi-scale architectural color identification and evaluation method spanning “buildings-streets-districts” was established. This methodology enables the recognition of dominant building colors in heritage zones at the district scale and the assessment of street color harmony and richness at the street scale. By analyzing these two levels, this research interprets the role of architectural color as a visual attribute in defining urban character and enhancing urban distinctiveness. It provides technical support for refining urban characteristics management systems and achieving precise control over the preservation and development of distinctive urban features. Full article

Moving away from a static concept for the landscape that surrounds us, in this research article, we approach the visual landscape as a dynamic concept. Moreover, we attempt to provide an interconnection between the domains of landscape and cartography by designing maps that are particularly suitable for characterizing the visible landscape and are potentially meaningful for overall landscape evaluation. Thus, the present work mainly focuses on the consecutive computation of vistas along highways, incorporating actual landscape composition—as the landscape is perceived from an egocentric perspective by observers moving along highway routes in peri-urban landscapes. To this end, we developed an integrated method and a Python (version 2.7.16) tool, named “RouteLAND”, for implementing an algorithmic geoprocessing procedure; through this geoprocessing tool, sequences of composite dynamic geospatial analyses and geometric calculations are automatically implemented. The final outputs are interactive web maps, whereby the segments of highway routes are characterized according to the dominant element of the visible landscape by employing (spatial) aggregation techniques. The developed geoprocessing tool and the generated interactive map provide a cartographic exploratory tool for summarizing the landscape character of highways in any peri-urban landscape, while hypothetically moving in a vehicle. In addition, RouteLAND can potentially aid in the assessment of existing or future highways’ scenic level and in the sustainable design of new highways based on the minimization of intrusive artificial structures’ vistas; in this sense, RouteLAND can serve as a valuable tool for landscape evaluation and sustainable spatial planning and development. Full article

The automatic identification and three-dimensional reconstruction of house plans has emerged as a significant research direction in intelligent building and smart city applications. Three-dimensional models reconstructed from two-dimensional floor plans provide more intuitive visualization for building safety assessments and spatial suitability evaluations. To address the limitations of existing public datasets—including low quality, inaccurate annotations, and poor alignment with residential architecture characteristics—this study constructs a high-quality vector dataset of raster house plans. We collected and meticulously annotated over 5000 high-quality floor plans representative of urban housing typologies, covering the majority of common residential layouts in the region. For architectural element recognition, we propose a key point-based detection approach for walls, doors, windows, and scale indicators. To improve wall localization accuracy, we introduce CPN-Floor, a method that achieves precise key point detection of house plan primitives. By generating and filtering candidate primitives through axial alignment rules and geometric constraints, followed by post-processing to refine the positions of walls, doors, and windows, our approach achieves over 87% precision and 88% recall, with positional errors within 1% of the floor plan’s dimensions. Scale recognition combines YOLOv8 with Shi–Tomasi corner detection to identify measurement endpoints, while leveraging the pre-trained multimodal OFA-OCR model for digital character recognition. This integrated solution achieves scale calculation accuracy exceeding 95%. We design and implement a house model recognition and 3D reconstruction system based on the WebGL framework and use the front-end MVC design pattern to interact with the data and views of the house model. We also develop a high-performance house model recognition and reconstruction system to support the rendering of reconstructed walls, doors, and windows; user interaction with the reconstructed house model; and the history of the house model operations, such as forward and backward functions. Full article

People are naturally drawn to symmetrical faces, as symmetry is often associated with attractiveness. In contrast to human faces, animated characters often emphasize certain geometric features, exaggerating them while maintaining symmetry and enhancing their visual appeal. This study investigated the impact of geometric parameters of facial features on fixation duration and explored 60 facial samples across two races (American, Japanese) and two conditions (animated, real). Relevant length, angle, and area parameters were extracted from the eyebrows, eyes, ears, nose, and chin regions of the facial samples. Using an eye-tracking experiment design, fixation duration (FD) and fixation count (FC) were extracted from 10 s gaze stimuli. Sixty participants (32 males and 28 females) took part. The results showed that, compared to Japanese animation, American animation typically induced a longer FD and higher FC on features like the eyes (p < 0.001), nose (p < 0.001), ears (p < 0.01), and chin (p < 0.01). Compared to real faces, animated characters typically attracted a longer FD and higher FC on areas such as the eyebrows (p < 0.001), eyes (p < 0.001), and ears (p < 0.001), while the nose (p < 0.001) and chin (p < 0.001) attracted a shorter FD and lower FC. Additionally, a correlation analysis between FD and geometric features showed a high positive correlation in the geometric features of the eyes, nose, and chin for both American and Japanese animated faces. The geometric features of the nose in real American and Japanese faces showed a high negative correlation coefficient. These findings highlight notable differences in FD and FC across different races and facial conditions, suggesting that facial geometric features may play a role in shaping gaze patterns and contributing to the objective quantitative assessment of FD. These insights are critical for optimizing animated character design and enhancing engagement in cross-cultural media and digital interfaces. Full article