arXiv subjects

Matheus A. Gadelha

Publications and source records attributed to Matheus A. Gadelha.

2 recordsLinked to original sources

arXiv2025

DepthScape: Authoring 2.5D Designs via Depth Estimation, Semantic Understanding, and Geometry Extraction

2.5D effects, such as occlusion and perspective foreshortening, enhance visual dynamics and realism by incorporating 3D depth cues into 2D designs. However, creating such effects remains challenging and labor-intensive due to the complexity of depth perception. We introduce DepthScape, a human-AI collaborative system that facilitates 2.5D effect creation by directly placing design elements into 3D reconstructions. Using monocular depth reconstruction, DepthScape transforms images into 3D reconstructions where visual contents are placed to automatically achieve realistic occlusion and perspective foreshortening. To further simplify 3D placement through a 2D viewport, DepthScape uses a vision-language model to analyze source images and extract key visual components as content anchors for direct manipulation editing. We evaluate DepthScape with nine participants of varying design backgrounds, confirming the effectiveness of our creation pipeline. We also test on 100 professional stock images to assess robustness, and conduct an expert evaluation that confirms the quality of DepthScape's results.

cs.HC↗

arXiv2017

Texture Fuzzy Segmentation using Skew Divergence Adaptive Affinity Functions

Digital image segmentation is the process of assigning distinct labels to different objects in a digital image, and the fuzzy segmentation algorithm has been successfully used in the segmentation of images from a wide variety of sources. However, the traditional fuzzy segmentation algorithm fails to segment objects that are characterized by textures whose patterns cannot be successfully described by simple statistics computed over a very restricted area. In this paper, we propose an extension of the fuzzy segmentation algorithm that uses adaptive textural affinity functions to perform the segmentation of such objects on bidimensional images. The adaptive affinity functions compute their appropriate neighborhood size as they compute the texture descriptors surrounding the seed spels (spatial elements), according to the characteristics of the texture being processed. The algorithm then segments the image with an appropriate neighborhood for each object. We performed experiments on mosaic images that were composed using images from the Brodatz database, and compared our results with the ones produced by a recently published texture segmentation algorithm, showing the applicability of our method.

cs.CV↗