Just-in-time outdoor color discrimination using adaptive similarity-based classifier

The color recognition and identification in operation time is a critical task in color-based computer vision applications. The main problem for recognizing the real color arises when the color characteristics are changed dynamically in the life time of a system. The outdoor color models which have been addressed by some researchers have serious practical limitations to employ in real applications. Moreover, due to high fluctuations in environment illumination, using conventional classifier for discriminating colors is a complicated task. In this paper, a just-in-time and model-free solution in order to discriminate outdoor colors on data driven modality is proposed. For this purpose, adaptive similarity-based classifier is utilized to track the color's data evolution during a day

Shadow-resistant segmentation based on illumination invariant image transformation

Robust plant image segmentation under natural illumination condition is still a challenging process for vision-based agricultural applications. One of the challenging aspects of natural condition is the large variation of illumination intensity. Illumination condition in the field continually changes, depending on the sunlight intensity, position, and moving clouds. This change affects RGB pixel values of acquired image and leads to inconsistent colour appearance of plant. Within this condition, plant segmentation based on RGB indices mostly produces poor threshold result. Besides, when shadows are presented in the scene, which is not uncommon in the field, plant segmentation becomes even more challenging. Excessive green (ExG) and other RGB indices have been widely used for plant image segmentation. Although ExG based segmentation is generally accepted as one of the most common and effective methods, it often provides poor segmentation results especially when the image scene contains an extreme illumination difference caused by dark shadows. To build an automated mobile weed control system, within the framework of the SmartBot project with the focus on the detection and control of volunteer potatoes in sugar beet, the vision-based system should first be able to detect plants out from the soil background even under dark shadow region. The objective of this research was to evaluate the segmentation robustness of illuminationinvariant transformation in comparison with ExG method under natural illumination conditions. Using illumination-invariant transformation, global and local thresholds (Otsu with reconstruction) were assessed to segment plant images. The ground shadow detection process was implemented to remove ground shadow region and background. Global threshold outperformed ExG, and local threshold could effectively remove the soil background region. Even under extreme illumination difference in a scene including sharp dark shadows due to bright sunshine, the illumination-invariant transformation produced robust segmentation results