Detection of green apples in natural scenes based on saliency theory and Gaussian curve fitting

Green apple targets are difficult to identify for having similar color with backgrounds such as leaves. The primary goal of this study was to detect green apples in natural scenes by applying saliency detection and Gaussian curve fitting algorithm. Firstly, the image was represented as a close-loop graph with superpixels as nodes. These nodes were ranked based on the similarity to background and foreground queries to generate the final saliency map. Secondly, Gaussian curve fitting was carried out to fit the V-component in YUV color space in salient areas, and a threshold was selected to binarize the image. To verify the validity of the proposed algorithm, 55 images were selected and compared with the common used image segmentation algorithms such as ¿-means clustering algorithm and FCM (Fuzzy C-means clustering algorithm). Four parameters including recognition ratio, FPR (false positive rate), FNR (false negative rate) and FDR (false detection rate) were used to evaluate the results, which were 91.84%, 1.36%, 8.16% and 4.22%, respectively. The results indicated that it was effective and feasible to apply this method to the detection of green apples in nature scenes.