민경하 교수

We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep neural network is widely employed for extracting features and recognizing emotions from various biosignals including EEG signals. A decision from a single CNN-based emotion recognizing module shows improved accuracy than the conventional handcrafted feature-based modules. To further improve the accuracy of the CNN-based modules, we devise a multi-column structured model, whose decision is produce

Visual stimuli from photographs and artworks raise corresponding emotional responses. It is a long process to prove whether the emotions that arise from photographs and artworks are different or not. We answer this question by employing electroencephalogram (EEG)-based biosignals and a deep convolutional neural network (CNN)-based emotion recognition model. We employ Russell's emotion model, which matches emotion keywords such as happy, calm or sad to a coordinate system whose axes are valence a

We propose a framework that synthesizes artistic landscape sketches using a diffusion model-based approach. Furthermore, we suggest a three-channel perspective map (3CPM) that mimics the artistic skill used by real artists. We employ Stable Diffusion, which leads us to use ControlNet to process 3CPM in Stable Diffusion. Additionally, we adopt the Low Rank Adaptation (LoRA) method to fine-tune our framework, thereby enhancing the quality of sketch and resolving the color-remaining problem, which

Research on physical activity and health is actively being conducted. In the Korea National Health and Nutrition Examination Survey (KNHANES), the Global Physical Activity Questionnaire (GPAQ) was newly introduced in 2014. The purpose of this study was to investigate the levels of physical activity and related factors in Koreans who were assessed through the GPAQ by dividing the physical activity by occupation, leisure, and transport domain. This study used data from the KNHANES (2014KNHANES ( -

We present a novel active contour-based two-pass approach to extract smooth feature regions from a triangular mesh. In the first pass, an active contour formulated in level-set surfaces is devised to extract feature regions with rough boundaries. In the second pass, the rough boundary curve is smoothed by minimizing internal energy, which is derived from its curvature. The separation of the extraction and smoothing process enables us to extract feature regions with smooth boundaries from a trian

Visual contents such as movies and animation evoke various human emotions. We examine an argument that the emotion from the visual contents may vary according to the contrast control of the scenes contained in the contents. We sample three emotions including positive, neutral and negative to prove our argument. We also sample several scenes of these emotions from visual contents and control the contrast of the scenes. We manipulate the contrast of the scenes and measure the change of valence and

We present a diffusion model-based approach that applies the artistic style of an artist or an art movement to a portrait photograph. Learning the style from the artworks of an artist or an art movement requires a training dataset composed of a lot of samples. We resolve this limitation by combining Contrastive Language Image Pretraining (CLIP) encoder and diffusion model, since the CLIP encoder extracts the features from an input portrait in a very effective way. Our framework includes three in

We present a legorization framework that produces a LEGO model from user-specified 3D mesh model. Our framework is composed of two stages: voxelization and legorization. In the voxelization, input 3D mesh is converted to a voxel model. To preserve the shape of the 3D mesh, we devise a silhouette fitting process for the initial voxel model. For legorization, we propose three objectives: stability, aesthetics and efficiency. These objectives are expressed in a tiling equation, which builds a LEGO

Electroencephalogram (EEG) biosignals are widely used to measure human emotional reactions. The recent progress of deep learning-based classification models has improved the accuracy of emotion recognition in EEG signals. We apply a deep learning-based emotion recognition model from EEG biosignals to prove that illustrated surgical images reduce the negative emotional reactions that the photographic surgical images generate. The strong negative emotional reactions caused by surgical images, whic