Deep learning presents promising solutions for the restoration and preservation of visual arts, including old color photographs or paintings, which are prone to degradation over time, enabling the vibrant imagery to be effectively revived and maintained. In this paper, we propose a methodology for restoring visual arts based on deep learning techniques purely trained on synthetic data, thereby involving the generation of a dataset that incorporates respective defects, the training of a respective defect segmentation model, and the inpainting using predicted segmentation maps. Through qualitative and quantitative analysis, we demonstrate the potential of our approach in addressing the scarcity of ground truth data and effectively restoring old visual arts by synthetic training on specific defects observed in historical artworks.
Tabletop scene synthesis is an important but underexplored area in scene generation. Existing methods typically focus on larger furniture pieces, often neglecting the fine-grained details of smaller objects positioned on surfaces like tabletops. To address this gap, we propose the first method specifically designed for tabletop scene synthesis using a diffusion model. Our diffusion model, trained on a large dataset of tabletop scenes, achieves state-of-the-art performance compared to current scene generation methods.
In addressing the issue that the Thing of Interest (ToI) in a photograph appears significantly smaller than that being directly perceived from that in the physical world, image editing techniques have been developed to enlarge ToI while maintaining appearance of other areas. However, these methods can suffer from issues such as loss of spatial perception, distortions of objects, the lack of automation, etc. To address these issues, we propose a novel ToI enlarging method based on Seam Carving method. The proposed method is composed of seam insertion and deletion processing, and introduces wide area seam energy mechanism, which considers impact of inserted or deleted seams on adjacent areas, in addition to preservation to salient objects, image structure, gradient and mask energies. Moreover, the proposed method introduces the Segment Anything Model to automatically generate masks representing the ToI. To validate the effectiveness of the proposed method, we conducted two subjective evaluation experiments in this study. The experimental results demonstrate that the proposed method can produce resulting images that reflect perceptual impressions, and can preserve area other than ToI.
Japanese Sign Language (JSL) and Manually Coded Japanese (MCJ) are the two main forms of sign language used in Japan. The former differs significantly from spoken Japanese, while the latter closely aligns with spoken syntax. Pidgin Sign Japanese (PSJ) is an intermediate form that heavily relies on nonmanual signals such as facial expressions and head movements for grammatical nuances. Current sign language recognition (SLR) systems predominantly focus on MCJ, neglecting the challenging properties of PSJ. This paper proposes an annotation assistance tool designed to automate the annotation of non-manual and spatial elements in PSJ. Our tool significantly reduces the manual effort required for annotation by using state-of-the-art methods for tracking human pose, hand, and face landmarks, along with recognizing facial action units (FAUs). Validation on a preliminary dataset of 30 videos containing over 90 instances of nonmanual elements demonstrated a $\mathbf{4 0 \%}$ reduction in annotation time, highlighting our proposal’s efficiency and effectiveness in handling the complexities of PSJ.
Individuals facing challenging and threatening situations significantly strain their minds and bodies. As a result, they may experience emotional, physical, or psychological stress. However, people perceive stress differently depending on how long or how severe their exposition to traumatic events is. Prolonged stress can cause significant and often inexplicable damage to both the body and mind. Because of that, early detection of human stress levels has become a prominent means of early diagnosis. Therefore, the major goal of this study is to investigate how human stress detection using Electroencephalography (EEG) which has been applied to EEG channel selection using Mutual Information (MI) with Minimum Redundancy Maximum Relevance (mRMR) (Mi$m R M R$) and the ensemble learning algorithm mainly using the method of bagging, boosting and stacking works in classifying different stress levels. On the other hand, the features were extracted using time-domain, frequency domain and timefrequency domain analysis containing relevant features related to stress data. Based on the experiment results, the ensemble learning method bagging performs better compared to the boosting method and can classify the data with the highest accuracy, highest F1 score, and highest precision, of 88 percent and highest precision of 94 percent. Hence, this shows the capability of the ensemble learning algorithms to classify stress EEG data that has been selected using (Mi-mRMR) algorithms as a means of early stress detection and diagnosis model.
