A Novel Approach to Smoothing on 3D Structured Adaptive Mesh of the Kinect-Based Models

Özbay, Erdal; Çınar, Ahmet

doi:10.5121/csit.2013.3802

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Low-cost sensor can obtain with the resulting point cloud scanning; basic measurement data, 2D or 3D graphical drawings, solid surface models, photometric images, 3D animation or 3D models dressed tissue. These laser scanners have practical and effective data collection techniques, both for researchers who will perform the measurement process and provide great convenience for using these measures [8].…”

Section: Introductionmentioning

confidence: 99%

“…The approach of a mesh on the object's surfaces (meshing) are usually done by obtaining reference data from objects that has a complicated exterior surfaces such as animal models, sculptures, buildings and art models with the aid of a laser scanner [9]. The basic method to distinguish geometrical based objects is done by extracting and using the data from a low cost laser scanner.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Metric Based Novel Classification Approach to Kinect Point Cloud Data

Özbay¹,

Çınar²

2016

NWSA

View full text Add to dashboard Cite

Object classification in 3D point cloud data is an emerging topic attracting increasing research interest. Object detection is one of the most important challenges in computer vision. This paper proposes a novel method for the efficient detection of the real objects with respect to 3D point cloud data. The real object detection is performed using a technique based on mean shift clustering algorithm. The efficiency of the method is verified comparative official 3D data and real 3D point cloud data. We embed presented approach in a framework that combination of extracts shape and point cloud data metric information to improve the outcome of the classification stage. For this aim, classification of 3D point cloud data allows robust segmentation and feature descriptions into different objects by significantly reducing the error. Performed mean shift classification algorithm on the raw data and metric data classification with mean shift algorithm implementation are automatically compared to for evaluation the accuracy of the classification of metric classification algorithm. The results obtained metric classification algorithm and mean shift algorithm on automatic classification of simple planimetric object shapes with the surface of the point cloud show that proposed method is an efficient process.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%