Procedural modeling and visualization of multiple leaves

Kim, Daeyeoul; Kim, JinMo

doi:10.1007/s00530-016-0503-z

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…By taking into account the biology and physics of plant growth or dehydration, the researchers created a more realistic twodimensional simulation of leaf veins. Kim and Kim, (2017) designed a contour-based method to model leaf veins. This method can use the growth information detected in the binary image of leaves to model the growth of leaf veins.…”

Section: Biological Modelingmentioning

confidence: 99%

Shrinkage properties of porous materials during drying: a review

Li,

Huang,

Gao

et al. 2024

Front. Mater.

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The shrinkage characteristic of porous materials is an important consideration in the drying process, as it can significantly impact the texture of the dried product and energy utilization. This phenomenon is influenced by numerous factors, including the structure of the cells, drying conditions, and the glass transition temperature. To gain a deeper understanding of the drying process, it is necessary to develop theoretical models that account for the simultaneous heat and mass transfer processes at the cellular level, as well as simulation tools to analyze the associated changes in drying morphology. In this paper, we highlight several key factors affecting shrinkage during the drying of porous materials, and also outline drying modeling, morphological simulation, and drying technology design considerations to provide guidance for improving the drying quality of porous materials as well as energy conversion efficiency.

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Section: Biological Modelingmentioning

confidence: 99%

Shrinkage properties of porous materials during drying: a review

Li,

Huang,

Gao

et al. 2024

Front. Mater.

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“…Leaves are important elements that constitute plants [12]. In [13] we see the leaves model of convolution sum of divisor functions. First, we fix the random color variation step and then we compute convolution sums by use of (6) as shown in Figure 2.…”

Section: Color Of Leavesmentioning

confidence: 99%

“…Here, size is the number of values that are not duplicated among the elements of convolution sums calculated in the selected table; max , min are the maximum and minimum noise values; and step indicates the section of noise values influenced by a single element of convolution sums. Once these parameters are fixed, the leaf color is determined by finding the index " " and the corresponding color " " in the color table comprised of divisor functions that correspond to the noise color in th row and th column [13]. Figure 2 represents the image of a color table set created by calculating the color table of each section.…”

Section: Color Of Leavesmentioning

confidence: 99%

Euler Polynomials and Combinatoric Convolution Sums of Divisor Functions with Even Indices

Kim

Bayad

Park

2014

Abstract and Applied Analysis

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We study combinatoric convolution sums of certain divisor functions involving even indices. We express them as a linear combination of divisor functions and Euler polynomials and obtain identitiesD2k(n)=(1/4)σ2k+1,0(n;2)-2·42kσ2k+1(n/4) -(1/2)[∑d|n,d≡1 (4){E2k(d)+E2k(d-1)}+22k∑d|n,d≡1 (2)E2k((d+(-1)(d-1)/2)/2)],U2k(p,q)=22k-2[-((p+q)/2)E2k((p+q)/2+1)+((q-p)/2)E2k((q-p)/2)-E2k((p+1)/2)-E2k((q+1)/2)+E2k+1((p+q)/2+1)-E2k+1((q-p)/2)], andF2k(n)=(1/2){σ2k+1†(n)-σ2k†(n)}. As applications of these identities, we give several concrete interpretations in terms of the procedural modelling method.

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“…Colorization can thus be considered as a separate problem addressed once the leaf geometry is available, and is typically implemented via an ad hoc third-party rendering software. Among the various approaches to obtain the color map to be used, we cite here the color model based on convolution sums of divisor functions proposed in [11], the shading model based on the PROSPECT model for light transmission in leaves [12] of [13], the "virtual rice" leaves generated in [14] from a RGB-SPAD color model and the Markov chain model of [10] in which environmental factors as air temperature and soil moisture were used as conditions to drive the probability to transfer from one state (texture and color) to another of the Markov chain.…”

Section: Introductionmentioning

confidence: 99%

A deep learning generative model approach for image synthesis of plant leaves

et al. 2022

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Objectives A well-known drawback to the implementation of Convolutional Neural Networks (CNNs) for image-recognition is the intensive annotation effort for large enough training dataset, that can become prohibitive in several applications. In this study we focus on applications in the agricultural domain and we implement Deep Learning (DL) techniques for the automatic generation of meaningful synthetic images of plant leaves, which can be used as a virtually unlimited dataset to train or validate specialized CNN models or other image-recognition algorithms. Methods Following an approach based on DL generative models, we introduce a Leaf-to-Leaf Translation (L2L) algorithm, able to produce collections of novel synthetic images in two steps: first, a residual variational autoencoder architecture is used to generate novel synthetic leaf skeletons geometry, starting from binarized skeletons obtained from real leaf images. Second, a translation via Pix2pix framework based on conditional generator adversarial networks (cGANs) reproduces the color distribution of the leaf surface, by preserving the underneath venation pattern and leaf shape. Results The L2L algorithm generates synthetic images of leaves with meaningful and realistic appearance, indicating that it can significantly contribute to expand a small dataset of real images. The performance was assessed qualitatively and quantitatively, by employing a DL anomaly detection strategy which quantifies the anomaly degree of synthetic leaves with respect to real samples. Finally, as an illustrative example, the proposed L2L algorithm was used for generating a set of synthetic images of healthy end diseased cucumber leaves aimed at training a CNN model for automatic detection of disease symptoms. Conclusions Generative DL approaches have the potential to be a new paradigm to provide low-cost meaningful synthetic samples. Our focus was to dispose of synthetic leaves images for smart agriculture applications but, more in general, they can serve for all computer-aided applications which require the representation of vegetation. The present L2L approach represents a step towards this goal, being able to generate synthetic samples with a relevant qualitative and quantitative resemblance to real leaves.

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Procedural modeling and visualization of multiple leaves

Cited by 10 publications

References 25 publications

Shrinkage properties of porous materials during drying: a review

Shrinkage properties of porous materials during drying: a review

Euler Polynomials and Combinatoric Convolution Sums of Divisor Functions with Even Indices

A deep learning generative model approach for image synthesis of plant leaves

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