Sparse Graph Regularized Mesh Color Edit Propagation

Ли, Бо; Lai, Yuekun; Rosin, Paul L.

doi:10.1109/tip.2020.2980962

Cited by 6 publications

(3 citation statements)

References 60 publications

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“…Lun et al [3] grouped discrete graphic patterns based on convolutional neural network architectures, trained on annotated datasets, resulting in robust grouping. A method based on sparse graph regularization was proposed in [4]. Initially, users interactively draw color strokes, and then colors diffuse across the entire grid by minimizing sparse graph and regularized nonlinear energy functions.…”

Section: Pattern Synthesis and Editingmentioning

confidence: 99%

Intelligent pattern reconstruction based on primitive spatial relationship adaptive transformation

Xia,

Li,

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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A novel method is proposed to construct relatively rich vector patterns from existing examples to address the problem of excessively simple and coarse details in automatically generated patterns. This method involves several key steps, including the extraction of vectorized primitives, the construction of primitive relationships, and the intelligent generation of patterns through optimization algorithms. Specifically, vectorized primitives are extracted from raster images, and directed graphs are used to establish relationships between primitives, taking into account the geometric relationships of the graph. Primitive relationships are calculated based on the extracted geometric relationships, and relevant constraints are used to transform the original pattern. The transformed pattern is then optimized to produce a more harmonious and aesthetically pleasing pattern variation. Experimental results show that the proposed algorithm can generate a diverse set of novel pattern variants, and the optimized variants demonstrate high levels of harmony and aesthetics. Users have the ability to influence the direction of pattern generation by adjusting the primitives, enabling them to compare and select the generated pattern variants that align with their implicit preferences. The proposed method provides an effective solution for pattern generation, catering to various requirements in practical applications and delivering a range of diverse pattern graphics for products.

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Section: Pattern Synthesis and Editingmentioning

confidence: 99%

Intelligent pattern reconstruction based on primitive spatial relationship adaptive transformation

Xia,

Li,

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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“…Lu et al [47] design a color retargeting approach, which composes a time-varying color image. Color editing has also been applied in multi-view [48][49][50][51] and 3D analysis [52,53]. Some researchers also attempt to preserve aesthetic or creative intentions during transfer [54][55][56].…”

Section: Color Editingmentioning

confidence: 99%

Towards natural object-based image recoloring

et al. 2021

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Existing color editing algorithms enable users to edit the colors in an image according to their own aesthetics. Unlike artists who have an accurate grasp of color, ordinary users are inexperienced in color selection and matching, and allowing non-professional users to edit colors arbitrarily may lead to unrealistic editing results. To address this issue, we introduce a palette-based approach for realistic object-level image recoloring. Our data-driven approach consists of an offline learning part that learns the color distributions for different objects in the real world, and an online recoloring part that first recognizes the object category, and then recommends appropriate realistic candidate colors learned in the offline step for that category. We also provide an intuitive user interface for efficient color manipulation. After color selection, image matting is performed to ensure smoothness of the object boundary. Comprehensive evaluation on various color editing examples demonstrates that our approach outperforms existing state-of-the-art color editing algorithms.

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“…Given the complexity of such networks, we limit our analysis to undirected graphs, which serve as a simplified model for microgrid connections. For the purposes of our discussion, we designate () VG as the point set, () EG as the edge set, denote the maximum degree as () G  , denote () G dx as the degree of x in graph G [1][2][3][4][5]. In graph G , the set of neighbors of point v is referred to as ()…”

Section: Introductionmentioning

confidence: 99%

Efficient 2-Distance Coloring Method for Maximum and Average Sparse Graphs in Resource Allocation Optimization of Microgrids

Chao Qin,

2024

jes

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In microgrids management, optimizing resource allocation and enhancing efficiency are critical challenges. This paper introduces the 2-distance coloring technique as a novel approach to address these issues by focusing on the coloring problem of sparse graphs. Through rigorous theoretical derivation, we establish the minimum number of colors required for 2-distance coloring in sparse graph scenarios. Specifically, we demonstrate that for a graph G with an average maximum degree less than 2+17/20 and a maximum degree of 6, it can be list 2-distance colored using no more than 11 colors. This means that, given any set of 11 colors for each vertex, a valid 2-distance coloring can be achieved, ensuring no two adjacent vertices share the same color. This finding is pivotal, as it sets a precedent for the number of resources needed to efficiently manage and allocate resources in microgrids systems through 2-distance coloring. The application of this technique in microgrids promises to revolutionize resource distribution, reducing overlap and maximizing efficiency across the network.

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Sparse Graph Regularized Mesh Color Edit Propagation

Cited by 6 publications

References 60 publications

Intelligent pattern reconstruction based on primitive spatial relationship adaptive transformation

Intelligent pattern reconstruction based on primitive spatial relationship adaptive transformation

Towards natural object-based image recoloring

Efficient 2-Distance Coloring Method for Maximum and Average Sparse Graphs in Resource Allocation Optimization of Microgrids

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