To solve the problem of orchard environmental perception, a 2D LiDAR sensor was used to scan fruit trees on both sides test platform obtain their position. Firstly, two-dimensional iterative closest point (2D-ICP) algorithm complete cloud data sides. Then, combining lightning connection (LAPO) and density-based clustering (DBSCAN), tree detection method based (LAPO-DBSCAN) proposed. After obtai...

Density-based clustering algorithms are widely used for discovering clusters in pattern recognition and machine learning. They can deal with non-hyperspherical robust to outliers. However, the runtime of density-based is heavily dominated by neighborhood finding density estimation which time-consuming. Meanwhile, traditional acceleration methods using indexing techniques such as KD-tree may not...

The success of the Case Based Reasoning (CBR) system depends on the quality of the case data. This quality is dedicated to the study of the case base competence which is measured by the range of problems that can be satisfactorily solved. In fact, modeling case-base competence is a clamorous issue in the discipline of CBR. However, the existence of erroneous cases as noises and the non uniform ...

In this paper, we present VSCAN, a novel approach for generating static video summaries. This approach is based on a modified DBSCAN clustering algorithm to summarize the video content utilizing both color and texture features of the video frames. The paper also introduces an enhanced evaluation method that depends on color and texture features. Video Summaries generated by VSCAN are compared w...

The hybrid approach is proposed to show that the clusters also show the swarm behavior. Krill herd algorithm is used to show the simulation of the herding behavior of the krill individuals. Density based approach is used for discovering the clusters and to show the region with sufficiently high density into clusters of krill individuals that of the arbitrary shape in environment. The minimum di...