A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-dimensional signal and image processing
Two-dimensional signal and image processing
An introduction to neural computing
An introduction to neural computing
An introduction to genetic algorithms
An introduction to genetic algorithms
Unsupervised learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computational Learning and Probabilistic Reasoning
Computational Learning and Probabilistic Reasoning
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
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A semi-automated object segmentation approach has been introduced in this paper. Object segmentation is a crucial task in image processing. The proposed approach learns segmentation from a small number of gold samples. The segmentation is performed in two main sequential steps, namely, target object localization, by applying optimal mathematical morphology procedure, and segmentation, by conducting some basic image processing operations. The outstanding feature of this approach is, unlike other existent approaches, that it does not need a prior knowledge or a large number of samples to learn from. The performance of the approach has been examined by a comprehensive well-designed validation set. For all test images, the target object was segmented accurately and the conducted experiments clearly showed that the proposed segmentation approach is highly invariant to noise, rotation, translation, overlapping, and scaling. The architecture of the approach and employed methodologies are explained in detail. Results are provided.