Identifying centres of interest in paintings using alignment and edge detection: Case studies on works by Luc Tuymans

• URL: http://arxiv.org/abs/2101.00858v1
• Date: Mon, 4 Jan 2021 10:04:19 GMT
• Title: Identifying centres of interest in paintings using alignment and edge detection: Case studies on works by Luc Tuymans
• Authors: Sinem Aslan, Luc Steels
• Abstract summary: We set the first preliminary steps to algorithmically deconstruct some of the transformations that an artist applies to an original image in order to establish centres of interest. We introduce a comparative methodology that first cuts out the minimal segment from the original image on which the painting is based, then aligns the painting with this source, investigates micro-differences to identify centres of interest and attempts to understand their role.
• Score: 1.8855270809505869
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: What is the creative process through which an artist goes from an original image to a painting? Can we examine this process using techniques from computer vision and pattern recognition? Here we set the first preliminary steps to algorithmically deconstruct some of the transformations that an artist applies to an original image in order to establish centres of interest, which are focal areas of a painting that carry meaning. We introduce a comparative methodology that first cuts out the minimal segment from the original image on which the painting is based, then aligns the painting with this source, investigates micro-differences to identify centres of interest and attempts to understand their role. In this paper we focus exclusively on micro-differences with respect to edges. We believe that research into where and how artists create centres of interest in paintings is valuable for curators, art historians, viewers, and art educators, and might even help artists to understand and refine their own artistic method.

Related papers

• GalleryGPT: Analyzing Paintings with Large Multimodal Models [64.98398357569765]
  Artwork analysis is important and fundamental skill for art appreciation, which could enrich personal aesthetic sensibility and facilitate the critical thinking ability. Previous works for automatically analyzing artworks mainly focus on classification, retrieval, and other simple tasks, which is far from the goal of AI. We introduce a superior large multimodal model for painting analysis composing, dubbed GalleryGPT, which is slightly modified and fine-tuned based on LLaVA architecture.
  arXiv  Detail & Related papers  (2024-08-01T11:52:56Z)
• Learning to Evaluate the Artness of AI-generated Images [64.48229009396186]
  ArtScore is a metric designed to evaluate the degree to which an image resembles authentic artworks by artists. We employ pre-trained models for photo and artwork generation, resulting in a series of mixed models. This dataset is then employed to train a neural network that learns to estimate quantized artness levels of arbitrary images.
  arXiv  Detail & Related papers  (2023-05-08T17:58:27Z)
• Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings [99.9788496281408]
  We study how sketches can be used as a weak label to detect salient objects present in an image. To accomplish this, we introduce a photo-to-sketch generation model that aims to generate sequential sketch coordinates corresponding to a given visual photo. Tests prove our hypothesis and delineate how our sketch-based saliency detection model gives a competitive performance compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2023-03-20T23:46:46Z)
• Inversion-Based Style Transfer with Diffusion Models [78.93863016223858]
  Previous arbitrary example-guided artistic image generation methods often fail to control shape changes or convey elements. We propose an inversion-based style transfer method (InST), which can efficiently and accurately learn the key information of an image.
  arXiv  Detail & Related papers  (2022-11-23T18:44:25Z)
• A domain adaptive deep learning solution for scanpath prediction of paintings [66.46953851227454]
  This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. We introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention.
  arXiv  Detail & Related papers  (2022-09-22T22:27:08Z)
• Toward Modeling Creative Processes for Algorithmic Painting [12.602935529346063]
  The paper argues that creative processes often involve two important components: vague, high-level goals and exploratory processes for discovering new ideas. This paper sketches out possible computational mechanisms for imitating those elements of the painting process, including underspecified loss functions and iterative painting procedures with explicit task decompositions.
  arXiv  Detail & Related papers  (2022-05-03T16:33:45Z)
• Automatic Main Character Recognition for Photographic Studies [78.88882860340797]
  Main characters in images are the most important humans that catch the viewer's attention upon first look. Identifying the main character in images plays an important role in traditional photographic studies and media analysis. We propose a method for identifying the main characters using machine learning based human pose estimation.
  arXiv  Detail & Related papers  (2021-06-16T18:14:45Z)
• Generative Art Using Neural Visual Grammars and Dual Encoders [25.100664361601112]
  A novel algorithm for producing generative art is described. It allows a user to input a text string, and which in a creative response to this string, outputs an image.
  arXiv  Detail & Related papers  (2021-05-01T04:21:52Z)
• Automatic analysis of artistic paintings using information-based measures [1.25456674968456]
  We identify hidden patterns and relationships present in artistic paintings by analysing their complexity. We apply Normalized Compression (NC) and the Block Decomposition Method (BDM) to a dataset of 4,266 paintings from 91 authors. We define a fingerprint that describes critical information regarding the artists' style, their artistic influences, and shared techniques.
  arXiv  Detail & Related papers  (2021-02-02T21:40:30Z)
• Understanding Compositional Structures in Art Historical Images using Pose and Gaze Priors [20.98603643788824]
  Image compositions are useful in analyzing the interactions in an image to study artists and their artworks. In this work, we attempt to automate this process using the existing state of the art machine learning techniques. Our approach focuses on two central themes of image composition: (a) detection of action regions and action lines of the artwork; and (b) pose-based segmentation of foreground and background.
  arXiv  Detail & Related papers  (2020-09-08T15:01:56Z)
• Artistic Style in Robotic Painting; a Machine Learning Approach to Learning Brushstroke from Human Artists [7.906207218788341]
  We propose a method to integrate an artistic style to the brushstrokes and the painting process through collaboration with a human artist. In a preliminary study, 71% of human evaluators find our reconstructed brushstrokes are pertaining to the characteristics of the artist's style.
  arXiv  Detail & Related papers  (2020-07-07T17:35:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.