Recognition method for apple fruit based on SUSAN and PCNN

Unsupervised Greenhouse Tomato Plant Segmentation Based on Self-Adaptive Iterative Latent Dirichlet Allocation from Surveillance Camera

“…Here we provide some experiments to verify the superiority of our algorithm, comparing with several state-of-the-art approaches utilizing quantum-behaved particle swarm optimization (QPSO) for muti-threshold searching [20], fuzzy c-means clustering (FCM) [16], pulse coupled neural network-based segmentation (PCNN) [14], Co-segmentation [53] and the traditional LDA, seen from Figures 17-20. We use red, green, and blue color to represent the labeled area of fruits, leaves, and the background, respectively.…”

“…We selected three unsupervised segmentation algorithms [20], [16], and [14] in the field of plant segmentation for comparative experiments. Through the results, we can see that when the same segmentation algorithm is applied to diverse backgrounds, their weaknesses will emerge at different level.…”

“…Artificial pulse coupled neural network PCNN is used in [14] for the recognition and segmentation of fruits. It matches the pixels with neurons and connects each other to form a single-layer, two-dimensional local feedback network.…”

“…It retains more information from the original image with the lowest similarity between classes and the greatest one within classes by the objective function. Although the concept of neighborhood density is introduced to initialize the clustering center in the literature and it does show higher segmentation accuracy than the above two, however, FCM is effective only for the recognition of mature tomatoes in close shots similar to [14]. As shown in image 4,7,8,10,17,19, and 20, all tomatoes have very high visual saliency in the images.…”

“…Until now, a diverse range of image segmentation algorithms have been presented in the agriculture field, among which there are color-based threshold methods [10,11], active contour methods combined with prior knowledge [12,13], neural network-based methods [14], and graph-clustering methods [15][16][17]. In order to obtain more accurate results, supervised algorithms [18,19] are also involved.…”