An Inverse Node Graph-Based Method for the Urban Scene Segmentation of 3D Point Clouds

Urban object segmentation and classification tasks are critical data processing steps in scene understanding, intelligent vehicles and 3D high-precision maps. Semantic segmentation of 3D point clouds is the foundational step in object recognition. To identify the intersecting objects and improve the accuracy of classification, this paper proposes a segment-based classification method for 3D point clouds. This method firstly divides points into multi-scale supervoxels and groups them by proposed inverse node graph (IN-Graph) construction, which does not need to define prior information about the node, it divides supervoxels by judging the connection state of edges between them. This method reaches minimum global energy by graph cutting, obtains the structural segments as completely as possible, and retains boundaries at the same time. Then, the random forest classifier is utilized for supervised classification. To deal with the mislabeling of scattered fragments, higher-order CRF with small-label cluster optimization is proposed to refine the classification results. Experiments were carried out on mobile laser scan (MLS) point dataset and terrestrial laser scan (TLS) points dataset, and the results show that overall accuracies of 97.57% and 96.39% were obtained in the two datasets. The boundaries of objects were retained well, and the method achieved a good result in the classification of cars and motorcycles. More experimental analyses have verified the advantages of the proposed method and proved the practicability and versatility of the method.

Variation among pineapple half-sibs and selecting genitors based on potential genetic divergence

Knowledge about variation and relative importance of agronomic traits for genetic divergence studies can reveal useful information to guide the breeding programs. The objective of this study was to evaluate the phenotypic variation, and select of pineapple half-sib seedlings based on genetic divergence of fruit and plant traits. 'Pérola' cultivar (female genitor) received a pollen mix as from cultivars 'MD-2', 'BRS Imperial' and 'BRS Vitória' (male genitors). Four hundred twenty-nine F1 individuals propagated from seeds were evaluated under field conditions. Descriptive statistics, Singh's (1981) relative contribution, and Tocher's cluster optimization methods based on the distances matrix were estimated. Coefficients of variation ranged from 9.89 % to 63.79 %. Regarding total variance, fruit traits grouped 52.69%, while plant traits accumulated 47.30%. These results demonstrated that evaluated traits contribute for half-sib progeny relative discrimination and that none of them should be discarded for studies of diversity. Among the 12 heterotic clusters formed, group VII, IX and VIII are recommended to compose hybridization blocks and evaluation cycles of phenotypic stability for use per se. These groups have a broad heterotic potential, and desirable agronomic traits, mainly regarding to high means for fruit mass without crown (FMWC) upper than 4,000g and soluble solids content (SSC) around 20-21°Brix, which can be used by the IPA’s pineapple breeding program.

An intelligent cluster optimization algorithm based on Whale Optimization Algorithm for VANETs (WOACNET)

Vehicular Ad hoc Networks (VANETs) an important category in networking focuses on many applications, such as safety and intelligent traffic management systems. The high node mobility and sparse vehicle distribution (on the road) compromise VANETs network scalability and rapid topology, hence creating major challenges, such as network physical layout formation, unstable links to enable robust, reliable, and scalable vehicle communication, especially in a dense traffic network. This study discusses a novel optimization approach considering transmission range, node density, speed, direction, and grid size during clustering. Whale Optimization Algorithm for Clustering in Vehicular Ad hoc Networks (WOACNET) was introduced to select an optimum cluster head (CH) and was calculated and evaluated based on intelligence and capability. Initially, simulations were performed, Subsequently, rigorous experimentations were conducted on WOACNET. The model was compared and evaluated with state-of-the-art well-established other methods, such as Gray Wolf Optimization (GWO) and Ant Lion Optimization (ALO) employing various performance metrics. The results demonstrate that the developed method performance is well ahead compared to other methods in VANET in terms of cluster head, varying transmission ranges, grid size, and nodes. The developed method results in achieving an overall 46% enhancement in cluster optimization and an F-value of 31.64 compared to other established methods (11.95 and 22.50) consequently, increase in cluster lifetime.

Cluster Optimization for Boundary Points using Distributive Progressive Feature Selection Algorithm

A group of different data objects is classified as similar objects is known as clusters. It is the process of finding homogeneous data items like patterns, documents etc. and then group the homogenous data items togetherothers groupsmay have dissimilar data items. Most of the clustering methods are either crisp or fuzzy and moreover member allocation to the respective clusters is strictly based on similarity measures and membership functions.Both of the methods have limitations in terms of membership. One strictly decides a sample must belong to single cluster and other anyway fuzzy i.e probability. Finally, Quality and Purity like measure are applied to understand how well clusters are created. But there is a grey area in between i.e. ‘Boundary Points’ and ‘Moderately Far’ points from the cluster centre. We considered the cluster quality [18], processing time and relevant features identification as basis for our problem statement and implemented Zone based clustering by using map reducer concept. I have implemented the process to find far points from different clusters and generate a new cluster, repeat the above process until cluster quantity is stabilized. By using this processwe can improve the cluster quality and processing time also.