A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for clustering data
Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal template matching by nonorthogonal image expansion using restoration
Machine Vision and Applications
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Building detection and description from a single intensity image
Computer Vision and Image Understanding
A Neural-Network-Based Approach to Detecting Hyperellipsoidal Shells
Neural Processing Letters
Detection and Modeling of Buildings from Multiple Aerial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Detection and Separation of Ring-Shaped Clusters Using Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building Reconstruction from Optical and Range Images
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Recognition and reconstruction of buildings from multiple aerial images
Computer Vision and Image Understanding
Rectangle Detection based on a Windowed Hough Transform
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
Fuzzy shell clustering algorithms in image processing: fuzzy C-rectangular and 2-rectangular shells
IEEE Transactions on Fuzzy Systems
Detection and segmentation of generic shapes based on affine modeling of energy in eigenspace
IEEE Transactions on Image Processing
Adaptive fuzzy c-shells clustering and detection of ellipses
IEEE Transactions on Neural Networks
Real-Time Car License Plate Recognition Improvement Based on Spatiognitron Neural Network
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
A Dipolar Competitive Neural Network for Video Segmentation
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Hi-index | 0.01 |
Many man-made objects are composed of a number of some simple geometric shapes such as lines, circles, rectangles, etc. Therefore, the detection of rectangular objects is an important issue to some practical applications such as the detection of buildings and vehicles in aerial imagery, the detection of license plates in car images, etc. Several methods have been proposed for solving the problem of the detection of rectangular objects. While some approaches are based on the detecting lines, some approaches are based on the Hough transform. Each approach has its own advantages and disadvantages (e.g., computational load). In this paper, we propose a class of neural networks with a special type of neural junctions for the detection of rectangular objects. The proposed neural networks can be trained in either an unsupervised mode or a batch mode. In contrast to some popular clustering algorithms such as the fuzzy c-means algorithm and the fuzzy c-rectangular shells algorithm, our approach is not based on minimizing an objective function but based on the idea of competitive learning. Based on the idea of competitive learning, the computational load can be decreased. Several data sets were tested to illustrate the effectiveness of our proposed approach.