Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images
This work addresses the need for precise spatial pattern analysis in domain-specific applications like Precision Agriculture, though it appears incremental in its algorithmic improvements.
The paper tackles the problem of exhaustively identifying targeted color-dots of varying shapes and sizes in images and testing their spatial patterns for uniformity, achieving robust and efficient performance compared to existing methods like Contour and OpenCV. It demonstrates this on images from drone-based chemical spraying in Precision Agriculture.
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.