ABSTRACT:

A new approach to extraction of affine invariant features of contour image and matching strategy is proposed for shape recognition. Firstly, the centroid distance and azimuth angle of each boundary point are computed. Then, with a prior-defined angle interval, all the points in the neighbor region of the sample point are considered to calculate the average distance for eliminating noise. After that, the centroid distance ratios (CDRs) of any two opposite contour points to the barycenter are achieved as the representation of the shape, which will be invariant to affine transformation. Since the angles of contour points will change non-linearly among affine related images, the CDRs should be re-sampled and combined sequentially to build one-by-one matching pairs of the corresponding points. The core issue is how to determine the angle positions for sampling, which can be regarded as an optimization problem of path planning. An ant colony optimization (ACO)-based path planning model with some constraints is presented to address this problem. Finally, the Euclidean distance is adopted to evaluate the similarity of shape features in different images. The experimental results demonstrate the efficiency of the proposed method in shape recognition with translation, scaling, rotation and distortion.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE :Extraction Algorithm of English Text Summarization for English Teaching - 2018ABSTRACT:In order to improve the power of sharing and scheduling capability of English teaching resources, an improved algorithm for
PROJECT TITLE :Theme-Related Keyword Extraction from Free Text Descriptions of Image Contents for Tagging - 2018ABSTRACT:This Project discusses a method for automatic theme-related keyword extraction from users' natural language
PROJECT TITLE :Large-Scale Kernel-Based Feature Extraction via Low-Rank Subspace Tracking on a Budget - 2018ABSTRACT:Kernel-primarily based ways get pleasure from powerful generalization capabilities in learning a selection of
PROJECT TITLE :BULDP Biomimetic Uncorrelated Locality Discriminant Projection for Feature Extraction in Face Recognition - 2018ABSTRACT:This Project develops a brand new dimensionality reduction technique, named biomimetic uncorrelated
PROJECT TITLE :Automatic Depth Extraction from 2D Images Using a Cluster-Based Learning Framework - 2018ABSTRACT:There has been a important increase in the provision of 3D players and displays in the last years. Nonetheless,

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry