Weighted Unsupervised Learning for 3D Object Detection

Abstract

This paper introduces a novel weighted unsupervised learning for object detection using an RGB-D camera. This technique is feasible for detecting the moving objects in the noisy environments that are captured by an RGB-D camera. The main contribution of this paper is a real-time algorithm for detecting each object using weighted clustering as a separate cluster. In a preprocessing step, the algorithm calculates the pose 3D position X, Y, Z and RGB color of each data point and then it calculates each data point's normal vector using the point's neighbor. After preprocessing, our algorithm calculates k-weights for each data point; each weight indicates membership. Resulting in clustered objects of the scene.

IJACSA

References in corpus (4)

• Geometric Adaptive Tracking Control of a Quadrotor UAV on SE(3) for Agile Maneuvers
• Construction of FuzzyFind Dictionary using Golay Coding Transformation for Searching Applications
• Geometric Nonlinear Controls for Multiple Cooperative Quadrotor UAVs Transporting a Rigid Body
• Novel Metaknowledge-based Processing Technique for Multimedia Big Data clustering challenges

Cited by in corpus (4)

• HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach
• FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification
• Weakly Supervised 3D Object Detection from Point Clouds
• Diagnosis and Analysis of Celiac Disease and Environmental Enteropathy on Biopsy Images using Deep Learning Approaches