Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images

TL;DR

This paper introduces a novel color-identification algorithm based on physics principles, enabling exhaustive detection of targeted color-dots and testing their spatial uniformness in images, with applications in precision agriculture.

Contribution

The paper presents a new physics-based color-identification method and a hierarchical clustering approach for testing spatial uniformness of color-dots in images.

Findings

The algorithm is robust across different lighting conditions.

It outperforms popular methods like Contour and OpenCV.

Applications demonstrated in drone-based precision agriculture.

Abstract

Targeted color-dots with varying shapes and sizes in images are first exhaustively identified, and then their multiscale 2D geometric patterns are extracted for testing spatial uniformness in a progressive fashion. Based on color theory in physics, we develop a new color-identification algorithm relying on highly associative relations among the three color-coordinates: RGB or HSV. Such high associations critically imply low color-complexity of a color image, and renders potentials of exhaustive identification of targeted color-dots of all shapes and sizes. Via heterogeneous shaded regions and lighting conditions, our algorithm is shown being robust, practical and efficient comparing with the popular Contour and OpenCV approaches. Upon all identified color-pixels, we form color-dots as individually connected networks with shapes and sizes. We construct minimum spanning trees (MST) as spatial geometries of dot-collectives of various size-scales. Given a size-scale, the distribution of distances between immediate neighbors in the observed MST is extracted, so do many simulated MSTs under the spatial uniformness assumption. We devise a new algorithm for testing 2D spatial uniformness based on a Hierarchical clustering tree upon all involving MSTs. Our developments are illustrated on images obtained by mimicking chemical spraying via drone in Precision Agriculture.