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David Martínez-Carpena

Publications and source records attributed to David Martínez-Carpena.

2 recordsLinked to original sources

Sketchable infinity categories

A sketch is a category equipped with specified collections of cones and cocones. Its models are functors to the category of sets that send the distinguished cones and cocones to limit cones and colimit cocones, respectively. Sketches provide a categorical formalization of theories, interpreting logical operations in terms of limits and colimits. Gabriel and Ulmer showed that categories of models of sketches involving only cones (called limit sketches) are precisely the locally presentable categories, while Lair extended this correspondence to sketches including both cones and cocones, thereby characterizing accessible categories. In this article, we discuss a homotopy-coherent generalization of sketches in the context of $\infty$-categories and prove that presentable $\infty$-categories are the $\infty$-categories of models of limit sketches, whereas accessible $\infty$-categories arise as the $\infty$-categories of models of arbitrary sketches. As illustrations, we make the corresponding sketches explicit for a wide range of $\infty$-categories, including complete Segal spaces, $\infty$-operads, $A_\infty$-algebras, $E_\infty$-algebras, spectra, and higher sheaves.

math.AT↗

Image color consistency in datasets: the Smooth-TPS3D method

Image color consistency is the key problem in digital imaging consistency when creating datasets. Here, we propose an improved 3D Thin-Plate Splines (TPS3D) color correction method to be used, in conjunction with color charts (i.e. Macbeth ColorChecker) or other machine-readable patterns, to achieve image consistency by post-processing. Also, we benchmark our method against its former implementation and the alternative methods reported to date with an augmented dataset based on the Gehler's ColorChecker dataset. Benchmark includes how corrected images resemble the ground-truth images and how fast these implementations are. Results demonstrate that the TPS3D is the best candidate to achieve image consistency. Furthermore, our Smooth-TPS3D method shows equivalent results compared to the original method and reduced the 11-15% of ill-conditioned scenarios which the previous method failed to less than 1%. Moreover, we demonstrate that the Smooth-TPS method is 20% faster than the original method. Finally, we discuss how different methods offer different compromises between quality, correction accuracy and computational load.

cs.CV↗