Realistic texture synthesis for point-based fruitage phenotype

• Published in: Computers in biology and medicine Vol. 92; pp. 42 - 54
• Main Authors: Yang, HuiJun, Chang, Jian, He, Dongjian, Geng, Nan, Wang, Meili, Zhang, JianJun, Jing, Xu, Chaudhry, Ehtzaz
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2018; Elsevier Limited
• Subjects: 3-D technology; Algorithms; Apples; Color vision; Computer graphics; Data acquisition; Feature extraction; Food quality; Fruit - anatomy & histology; Fruitage morphology; Fruits; Genotype & phenotype; Image acquisition; Imaging, Three-Dimensional - methods; Internal Medicine; Malus - anatomy & histology; Methods; Morphology; Other; Oversampling; Pattern recognition; Perception; Sampling; Scanners; Seams; Splitting; Surface Properties; Synthesis; Texton division; Texture; Texture sampling; Texture synthesis; Texture synthesis; Fruitage morphology; Texton division; Texture sampling
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2016.08.017

Although current 3D scanner technology can acquire textural images from a point model, visible seams in the image, inconvenient data acquisition and occupancy of a large space during use are points of concern for outdoor fruit models. In this paper, an SPSDW (simplification and perception based subdivision followed by down-sampling weighted average) method is proposed to balance memory usage and texture synthesis quality using a crop fruit, such as apples, as a research subject for a point-based fruit model. First, the quadtree method is improved to make splitting more efficient, and a reasonable texton descriptor is defined to promote query efficiency. Then, the color perception feature is extracted from the image for all pixels. Next, an advanced sub-division scheme and down-sampling strategy are designed to optimize memory space. Finally, a weighted oversampling method is proposed for high-quality texture mixing. This experiment demonstrates that the SPSDW method preserves the mixed texture more realistically and smoothly and preserves color memory up to 94%, 84.7% and 85.7% better than the two-dimesional processing, truncating scalar quantitative and color vision model methods, respectively. •SPSDW algorithm for trade-off between visual quality and memory requirement.•Adaptive quadtree proposed to simplify texture and improve organization efficiency.•Perception-based subdivision and down sampling is developed to compress storage.•Proposed weighted oversampling scheme improved the fidelity of texture synthesis.