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A Lightweight Multi-Metric No-Reference Image Quality Assessment Framework for UAV Imaging PatchPoison: Poisoning Multi-View Datasets to Degrade 3D Reconstruction 3DRealHead: Few-Shot Detailed Head Avatar GeoLink: A 3D-Aware Framework Towards Better Generalization in Cross-View Geo-Localization Towards Patient-Specific Deformable Registration in Laparoscopic Surgery Neural 3D Reconstruction of Planetary Surfaces from Descent-Phase Wide-Angle Imagery A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models DroneScan-YOLO: Redundancy-Aware Lightweight Detection for Tiny Objects in UAV Imagery See&Say: Vision Language Guided Safe Zone Detection for Autonomous Package Delivery Drones PAT-VCM: Plug-and-Play Auxiliary Tokens for Video Coding for Machines Bias at the End of the Score Deep Spatially-Regularized and Superpixel-Based Diffusion Learning for Unsupervised Hyperspectral Image Clustering The Spectrascapes Dataset: Street-view imagery beyond the visible captured using a mobile platform Why MLLMs Struggle to Determine Object Orientations Towards Successful Implementation of Automated Raveling Detection: Effects of Training Data Size, Illumination Difference, and Spatial Shift Right Regions, Wrong Labels: Semantic Label Flips in Segmentation under Correlation Shift SSD-GS: Scattering and Shadow Decomposition for Relightable 3D Gaussian Splatting SEDTalker: Emotion-Aware 3D Facial Animation Using Frame-Level Speech Emotion Diarization MSGS: Multispectral 3D Gaussian Splatting Multi-Agent Object Detection Framework Based on Raspberry Pi YOLO Detector and Slack-Ollama Natural Language Interface UniBlendNet: Unified Global, Multi-Scale, and Region-Adaptive Modeling for Ambient Lighting Normalization A Multimodal Clinically Informed Coarse-to-Fine Framework for Longitudinal CT Registration in Proton Therapy Why Multimodal In-Context Learning Lags Behind? 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Which Parts Determine the Impression of the Font?
Masaya Ueda, Akisato Kimura, Seiichi Uchida · 2021-03-26 · via cs.CV updates on arXiv.org

Various fonts give different impressions, such as legible, rough, and comic-text.This paper aims to analyze the correlation between the local shapes, or parts, and the impression of fonts. By focusing on local shapes instead of the whole letter shape, we can realize letter-shape independent and more general analysis. The analysis is performed by newly combining SIFT and DeepSets, to extract an arbitrary number of essential parts from a particular font and aggregate them to infer the font impressions by nonlinear regression. Our qualitative and quantitative analyses prove that (1)fonts with similar parts have similar impressions, (2)many impressions, such as legible and rough, largely depend on specific parts, (3)several impressions are very irrelevant to parts.

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