Object Recognition Technique based on Level Set Method and Neural Network

Abstract

The Object recognition is the task of finding and labeling parts of a two-dimensional (2D) image of a scene that correspond to objects in the scene. In this paper, we have proposed an efficient approach using level set method for extracting object shape contour and convex hull as a shape invariant features to the Feed forward Neural Network classifier for object recognition. We extracted the shape contour by level set method. Then, we have obtained invariant shape feature, convex hull of the objects. This convex hull set serves as a pattern for the Neural Network. Initially Feed forward neural network trained on the odd data set and tested on even data set. Our approach is evaluated on the Amsterdam Library of Object Images collection, a large collection of object images containing 1000 objects recorded under various imaging circumstances. The experimental results clearly demonstrate that our approach significantly outperforms. The proposed method is shown to be effective under a wide variety of imaging conditions.

Deep Convolutional Level Set Method for Image Segmentation

“…The Variational Level Set Method is used in the feature extraction pipeline to remove highly uncertain regions. Pawar and Talbar [23] also used Variational Level Set Method as a feature extractor before feeding the image into a feed forward neural network for classification. These previous works used Level Set Method as a prior for the learning method.…”