Image Sampling for Machine Vision

Cui, Jiashuai; Fan, Li; Wang, Liejun

doi:10.1007/978-3-031-20497-5_19

Cited by 1 publication

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we present an image sampling method based on a dominant color component (ISDCC) for computer vision. Building upon our previous work [5] published at a conference, ISDCC aims to greatly reduce the data volume while retaining the effective information used in downstream vision tasks. The main contributions of our method are summarized as follows:…”

Section: Machine Vision Algorithm Data Volume Machine Vision Algorith...mentioning

confidence: 99%

Image Sampling Based on Dominant Color Component for Computer Vision

Wang

Cui

et al. 2023

Electronics

Self Cite

View full text Add to dashboard Cite

Image sampling is a fundamental technique for image compression, which greatly improves the efficiency of image storage, transmission, and applications. However, existing sampling algorithms primarily consider human visual perception and discard irrelevant information based on subjective preferences. Unfortunately, these methods may not adequately meet the demands of computer vision tasks and can even lead to redundancy because of the different preferences between human and computer. To tackle this issue, this paper investigates the key features of computer vision. Based on our findings, we propose an image sampling method based on the dominant color component (ISDCC). In this method, we utilize a grayscale image to preserve the essential structural information for computer vision. Then, we construct a concise color feature map based on the dominant channel of pixels. This approach provides relevant color information for computer vision tasks. We conducted experimental evaluations using well-known benchmark datasets. The results demonstrate that ISDCC adapts effectively to computer vision requirements, significantly reducing the amount of data needed. Furthermore, our method has a minimal impact on the performance of mainstream computer vision algorithms across various tasks. Compared to other sampling approaches, our proposed method exhibits clear advantages by achieving superior results with less data usage.

show abstract

Section: Machine Vision Algorithm Data Volume Machine Vision Algorith...mentioning

confidence: 99%