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Once the human vision system has seen a 3D object from a few different viewpoints, depending on the nature of the object, it can generally recognize that object from new arbitrary viewpoints. This useful interpolative skill relies on the highly complex pattern matching systems in the human brain, but the general idea can be applied to a computer vi...
Citations
... Several moment-based descriptors were proposed along the years, and some of them are present in this review: Ramalingam [238] presents a fuzzy surface classification paradigm, which is an extension of conventional techniques based on sign of mean and Gaussian curvatures. In his work, a fuzzy moment-based recognition technique described and tested in [275] was employed; Ong [219] presents a theoretical framework for deriving scale and translation invariants for 3-D Legendre moments through the use of direct and indirect methods, employing the obtained invariants on 3D object recognition; Xu [323] proposes a 3D object recognition method, which uses some features, color moments, texture features, Hu's moment invariants and the affine moment invariants, extracted from each 2D image of 3D objects; Mavrinac [191] presents an approach for recognition of 3D objects in arbitrary poses, providing only a limited set of training view samples. This approach involves computing a disparity map and extract, from the map, a set of disparity map features (compactness, first Hu moments and the image general distribution intensity histogram); the method presented by Wan [300] shows a classification method, based on fuzzy KNN and Bayesian Rules, to determine whether a 3D object belongs to the human class, Fig. 9 Representation of analyzed works that employ global features. ...
... Some works employed classifiers based on deep learning models [274,314,330], others used the k-nearest neighbor associated with Euclidean distance [122,158,309]. There are also a few works that utilized fuzzy [191,269], fuzzy associated with Bayesian networks, fuzzy associated with neural networks [221] and probabilistic models [237]. ...
In this paper, we present a systematic literature review concerning 3D object recognition and classification. We cover articles published between 2006 and 2016 available in three scientific databases (ScienceDirect, IEEE Xplore and ACM), using the methodology for systematic review proposed by Kitchenham. Based on this methodology, we used tags and exclusion criteria to select papers about the topic under study. After the works selection, we applied a categorization process aiming to group similar object representation types, analyzing the steps applied for object recognition, the tests and evaluation performed and the databases used. Lastly, we compressed all the obtained information in a general overview and presented future prospects for the area.
Link for the publication:
https://link.springer.com/epdf/10.1007/s10044-019-00804-4?author_access_token=paE7wTbqwKN7oCwVliHwLve4RwlQNchNByi7wbcMAY7uL2tJzq0UXA0O13kX7wvxz98EQWbRDi2uT7G5KxVe0WzCAoagCbJhmkFlrCPdZIPfyyYkaSt_0zAEiJJc2cojH9AajAmYQ5BT1LV4EonJMg%3D%3D
1 It is desirable for automated object recognition using computer vision systems to emulate the human capacity for recognition of shapes invariant to vari-ous transformations. We present an algorithm, based on a Fuzzy Associative Database approach, which uses appropriately invariant metrics and a neu-ro-fuzzy inference method to accurately classify both two-and three-dimensional objects (using different metrics for each). The system is trained using a small number of images of each object class under varying degrees of the transformations, and as we show expe-rimentally, is then able to identify objects under oth-er non-explicitly-trained degrees of the transforma-tions.
