Biologically Inspired Hexagonal Image Structure for Computer Vision

Varghese, Prathibha; Saroja, G. Arockia Selva

doi:10.1007/978-981-19-0619-0_43

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of the overall rectification performance with deformable meshes, it can be observed that the hexagon performs the best, followed by the rectangular mesh, while the triangular mesh exhibits a poorer rectification effect. This is because the hexagonal mesh has better adaptability compared to other meshes; it can effectively cover and adapt to various irregular contours, thereby enhancing the performance of the regression model [68,69]. Simultaneously, the hexagonal mesh can more compactly cover the image area, reducing redundancy.…”

Section: Impact Of Deformable Mesh and Loss Functions On Image Rectif...mentioning

confidence: 99%

Rectification for Image Stitching with Deformable Meshes and Residual Networks

Fan,

Mao,

Mei

et al. 2024

Preprint

View full text Add to dashboard Cite

Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

show abstract

Section: Impact Of Deformable Mesh and Loss Functions On Image Rectif...mentioning

confidence: 99%