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Line drawing colorization is a crucial step in the image creation process, yet traditional manual coloring demands considerable time and effort from skilled artists. While deep learning advancements have enabled colorization through various user prompts and text inputs, these approaches still often require some degree of human intervention. The challenge lies in finding fully automated methods that can achieve high-quality results without manual assistance, maintaining both efficiency and artistic integrity. In this paper, we propose a region assisted for reference-based line drawing colorization, which uses a more stable diffusion model to automatically colorize line drawing and introduces a skeleton map as an additional guide to reduce the bleeding problem encountered during colorization and improve the quality of the generated images. To further improve the model’s ability to capture the colors from reference images and enhance the overall quality of the colorized output, we adopt a two-stage training strategy. In the first stage, a pre-trained model designed to capture cartoon-like features was trained on a large-scale dataset. The second stage involved fine-tuning the model on a smaller, specialized dataset. Additionally, we have created a paired dataset of fashion line drawings and illustrations, which can be utilized in the fashion design industry. The effectiveness of our reference-based automatic coloring approach was validated through extensive qualitative and quantitative experiments, demonstrating its robustness and adaptability across various contexts. Our code will be released at ( https://github.com/jackmeme1/colorization ).

People affected by rarer forms of dementia often have a long and difficult experience obtaining a diagnosis and appropriate support, impacting family, employment and social relationships, quality of life and wellbeing. For this population progressive cognitive symptoms affect skills other than memory and disproportionately occur under the age of 65 years, often resulting in misdiagnosis and lack of appropriate care pathways. The objective of this study will be to better understand the subjective experience of the time period from first noticing symptoms to obtaining a formal diagnosis, through to accessing support, and onward to the present time. Through the concurrent use of line drawings and video-recorded interviews we will collect the stories of people living with different rarer dementias and/or family members who are care partners in Canada and the United Kingdom. Narrative and visual analysis will be used in parallel to methodologically explore how line drawing and verbal discourse interact and inform each other to construct knowledge, and how the use of drawing lines might enrich research interviews and increase accessibility of research participation. This novel research approach may also have implications for clinical interviewing, support services, and public engagement. To the best of our knowledge, this is the first study to retrospectively explore over time the experiences of people affected by rarer forms of dementia from initial symptoms—to diagnosis—to accessing support—to the present, using visual and verbal methodologies.

Affective/cognitive engineering investigations typically require the quantitative assessment of object perception. Recent research has suggested that certain perceptions of object categorization can be derived from human eye fixation and that color images and line drawings induce similar neural activities. Line drawings contain less information than color images; therefore, line drawings are expected to simplify the investigations of object perception. The psychological potential field (PPF), which is a psychological feature, is an image feature of line drawings. On the basis of the PPF, the possibility that the general human perception of object categorization can be assessed from the similarity to fixation maps (FMs) generated from human eye fixations has been reported. However, this may be due to chance because image features other than the PPF have not been compared with FMs. This study examines the potential and effectiveness of the PPF by comparing its performance with that of other image features in terms of the similarity to FMs. The results show that the PPF shows the ideal performance for assessing the perception of object categorization. In particular, the PPF effectively distinguishes between animal and nonanimal targets; however, real-time assessment is difficult.

In this article the authors describe the use of time line drawings in sensitive-topic narrative interviews. They present time line drawings as a means of inviting participants to enter into a reflective space and engage their stories with a depth that might not happen without such a representational activity. The authors discuss three examples of research participant drawings.

Background Poor performance on conjunctional or relational memory binding tasks is proposed as a neurocognitive marker for Alzheimer's Disease. However, it remains unclear i) how relational and conjunctional memory binding is affected by healthy ageing and ii) whether memory binding performance relates to advanced functions of everyday life underpinned by such memory constructs. Method Younger (n = 41, 18–39 years, mean‐age = 23.56) and older (n = 41, 62–82 years, mean‐age = 73.78) adults completed reconstruction tasks measuring non‐spatial memory binding for integrated (conjunctive) or paired (relational) features. During encoding, relational trials involved three shape‐colour pairs, while conjunctive trials involved three coloured shapes. Immediately after, participants reconstructed feature pairs or integrated objects by choosing from a set of four coloured bubbles and a set of four line drawings of shapes and their corresponding colours. Scores reflected recognition of individual features and recall of feature pairings. Based on recent recommendations (Parra et al., 2024), participants were classified as weak or strong binders by contrasting scores drawn from the binding conditions relative to the single feature conditions using a 0.50 (∼25th percentile) cut‐off for the former. Participants also completed the Details of Function of Everyday Life (DoFEL) questionnaire. Result ANCOVA revealed younger adults performed more accurately than older adults across all conditions (p < .001, η2 = .074). Individual features were more accurately recalled than paired/integrated features (p < .001, η2 = .398). The only significant interaction was that of age‐by‐binding type (p < .001, η2 = .031), which showed older adults were less accurate for paired/integrated features compared to younger adults (p < .001), while no age difference was observed for individual feature accuracy (p = .469), suggesting an age‐related binding decline. Relative to strong binders (n = 66), weak binders (n = 16) reported more functional impairments on the DoFEL (Mann‐Whitney test: p = .002, r = ‐0.46). Conclusion Our results indicate that when ageing is associated with a selective decrease in memory binding abilities, further assessments should be carried out as such subtle memory impairments may underpin impairments in advanced functions of everyday life, thus pointing to an increased risk of being embarked on pathological ageing trajectories.

An intrinsic part of seeing objects is seeing how similar or different they are relative to one another. This experience requires that objects be mentally represented in a common format over which such comparisons can be carried out. What is that representational format? Objects could be compared in terms of their superficial features (e.g., degree of pixel-by-pixel overlap), but a more intriguing possibility is that they are compared on the basis of a deeper structure. One especially promising candidate that has enjoyed success in the computer vision literature is the shape skeleton —a geometric transformation that represents objects according to their inferred underlying organization. Despite several hints that shape skeletons are computed in human vision, it remains unclear how much they actually matter for subsequent performance. Here, we explore the possibility that shape skeletons help mediate the ability to extract visual similarity. Observers completed a same/different task in which two shapes could vary either in their skeletal structure (without changing superficial features such as size, orientation, and internal angular separation) or in large surface-level ways (without changing overall skeletal organization). Discrimination was better for skeletally dissimilar shapes: observers had difficulty appreciating even surprisingly large differences when those differences did not reorganize the underlying skeletons. This pattern also generalized beyond line drawings to 3-D volumes whose skeletons were less readily inferable from the shapes’ visible contours. These results show how shape skeletons may influence the perception of similarity—and more generally, how they have important consequences for downstream visual processing.

Humans can categorize complex natural scenes quickly and accurately. Which scene properties enable people to do this with such apparent ease? We extracted structural properties of contours (orientation, length, curvature) and contour junctions (types and angles) from line drawings of natural scenes. All of these properties contain information about scene categories that can be exploited computationally. However, when we compared error patterns from computational scene categorization with those from a six-alternative forced-choice scene-categorization experiment, we found that only junctions and curvature made significant contributions to human behavior. To further test the critical role of these properties, we perturbed junctions in line drawings by randomly shifting contours and found a significant decrease in human categorization accuracy. We conclude that scene categorization by humans relies on curvature as well as the same nonaccidental junction properties used for object recognition. These properties correspond to the visual features represented in area V2.

Why are some images more likely to be remembered than others? Previous work focused on the influence of global, low-level visual features as well as image content on memorability. To better understand the role of local, shape-based contours, we here investigate the memorability of photographs and line drawings of scenes. We find that the memorability of photographs and line drawings of the same scenes is correlated. We quantitatively measure the role of contour properties and their spatial relationships for scene memorability using a Random Forest analysis. To determine whether this relationship is merely correlational or if manipulating these contour properties causes images to be remembered better or worse, we split each line drawing into two half-images, one with high and the other with low predicted memorability according to the trained Random Forest model. In a new memorability experiment, we find that the half-images predicted to be more memorable were indeed remembered better, confirming a causal role of shape-based contour features, and, in particular, T junctions in scene memorability. We performed a categorization experiment on half-images to test for differential access to scene content. We found that half-images predicted to be more memorable were categorized more accurately. However, categorization accuracy for individual images was not correlated with their memorability. These results demonstrate that we can measure the contributions of individual contour properties to scene memorability and verify their causal involvement with targeted image manipulations, thereby bridging the gap between low-level features and scene semantics in our understanding of memorability.

Several studies have shown that blind people, including those with congenital blindness, can use raised-line drawings, both for “reading” tactile graphics and for drawing unassisted. However, research on drawings produced by blind people has mainly been qualitative. The current experimental study was designed to investigate the under-researched issue of the size of drawings created by people with blindness. Participants ( N = 59) varied in their visual status. Adventitiously blind people had previous visual experience and might use visual representations (e.g., when visualising objects in imagery/working memory). Congenitally blind people did not have any visual experience. The participant’s task was to draw from memory common objects that vary in size in the real world. The findings revealed that both groups of participants produced larger drawings of objects that have larger actual sizes. This means that the size of familiar objects is a property of blind people’s mental representations, regardless of their visual status. Our research also sheds light on the nature of the phenomenon of canonical size. Since we have found the canonical size effect in a group of people who are blind from birth, the assumption of the visual nature of this phenomenon – caused by the ocular-centric biases present in studies on drawing performance – should be revised.

Early in life and without special training, human beings discern resemblance between abstract visual stimuli, such as drawings, and the real-world objects they represent. We used this capacity for visual abstraction as a tool for evaluating deep neural networks (DNNs) as models of human visual perception. Contrasting five contemporary DNNs, we evaluated how well each explains human similarity judgments among line drawings of recognizable and novel objects. For object sketches, human judgments were dominated by semantic category information; DNN representations contributed little additional information. In contrast, such features explained significant unique variance perceived similarity of abstract drawings. In both cases, a vision transformer trained to blend representations of images and their natural language descriptions showed the greatest ability to explain human perceptual similarity—an observation consistent with contemporary views of semantic representation and processing in the human mind and brain. Together, the results suggest that the building blocks of visual similarity may arise within systems that learn to use visual information, not for specific classification, but in service of generating semantic representations of objects.