Detection of visual defects in citrus fruits: multivariate image analysis vs graph image segmentation

¿The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40261-6_28This paper presents an application of visual quality control in orange post-harvesting comparing two different approaches. These approaches correspond to two very different methodologies released in the area of Computer Vision. The first approach is based on Multivariate Image Analysis (MIA) and was originally developed for the detection of defects in random color textures. It uses Principal Component Analysis and the T2 statistic to map the defective areas. The second approach is based on Graph Image Segmentation (GIS). It is an efficient segmentation algorithm that uses a graph-based representation of the image and a predicate to measure the evidence of boundaries between adjacent regions. While the MIA approach performs novelty detection on defects using a trained model of sound color textures, the GIS approach is strictly an unsupervised method with no training required on sound or defective areas. Both methods are compared through experimental work performed on a ground truth of 120 samples of citrus coming from four different cultivars. Although the GIS approach is faster and achieves better results in defect detection, the MIA method provides less false detections and does not need to use the hypothesis that the bigger area in samples always correspond to the non-damaged areaLópez García, F.; Andreu García, G.; Valiente González, JM.; Atienza Vanacloig, VL. (2013). Detection of visual defects in citrus fruits: multivariate image analysis vs graph image segmentation. En Computer Analysis of Images and Patterns. Springer Verlag (Germany). 8047:237-244. doi:10.1007/978-3-642-40261-6S237244804

Robust Rigid Shape Registration Method Using a Level Set Formulation

This paper presents a fast algorithm for robust registration of shapes implicitly represented by signed distance functions(SDF). The proposed algorithm aims to recover the transformation parameters( scaling, rotation, and translation) by minimizing the dissimilarity between two shapes. To achieve a robust and fast algorithm, linear orthogonal transformations are employed to minimize the dissimilarity measures. The algorithm is applied to various shape registration problems, to address issues such as topological invariance, shape complexity, and convergence speed and stability. The outcomes are compared with other state-of-the-art shape registration algorithms to show the advantages of the new technique