An introduction to digital image processing
An introduction to digital image processing
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
An AI tool for the petroleum industry based on image analysis and hierarchical clustering
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Unsupervised and supervised learning in cascade for petroleum geology
Expert Systems with Applications: An International Journal
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One of the most recurrent problem in digital image processing applications is segmentation. Segmentation is the separation of components in the image: the ability to identify and to separate objects from the background. Depending on the application, this activity can be very difficult and segmentation accuracy is crucial in order to obtain reliable results. In this paper we propose an approach for spot detection in images with noisy background. The overall approach can be divided in three main steps: image segmentation, region labeling and selection. Three segmentation algorithms, based on global or local thresholding technique, are developed and tested in a real-world petroleum geology industrial application. To assess algorithm accuracy we use a simple voting technique: by a visual comparison of the results, three domain experts vote for the best algorithms. Results are encouraging, in terms of accuracy and time reduction, especially for the algorithm based on local thresholding technique.