Background:
A physical object, which is actually in 3D form, is captured by a sensor/
camera (in case of computer vision) and seen by a human eye (in case of a human vision). When
someone is observing something, many other things are also involved there which make it more challenging
to recognize. After capturing such a thing by a camera or sensor, a digital image is formed
which is nothing other than a bunch of pixels. It is becoming important to know that how a computer
understands images.
Objective:
This paper is for highlighting novel techniques on 3D object recognition system with local
shape descriptors and depth data analysis.
Methods:
The proposed work is applied to RGBD and COIL-100 datasets and this is of four-fold as
preprocessing, feature generation, dimensionality reduction, and classification. The first stage of preprocessing
is smoothing by 2D median filtering on the depth (Z-value) and registration by orientation
correction on 3D object data. The next stage is of feature generation and having two phases of shape
map generation with shape index map and SIFT/SURF descriptors. The dimensionality reduction is the
third stage of this proposed work where linear discriminant analysis and principal component analysis
are used. The final stage is fused on classification.
Results:
Here, calculation of the discriminative subspace for the training set, testing of object data and
classification is done by comparing target and query data with different aspects for finding proper
matching tasks.
Conclusion:
This concludes with new proposed approach of 3D Object Recognition. The local shape
descriptors are used for 3D object recognition system to implement and test. This system is achieves
89.2% accuracy for Columbia object image library-100 images by using local shape descriptors.
An evaluation of local shape descriptors for 3D shape retrieval