Selecting the Optimal Focus Measure for Autofocusing and Depth-From-Focus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identification of tuberculosis bacteria based on shape and color
Real-Time Imaging - Special issue on imaging in bioinformatics: Part III
Automatic Acquisition of Immunofluorescence Images: Algorithms and Evaluation
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
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Auto-focusing is an important problem for various imaging systems. A good focus measure, or evaluation function, is critical for the accomplishment of this task. Some evaluation functions are introduced. Analysis is done on evaluation functions using global data such as the squared image gradient magnitude of the whole image, asserting that these approaches may suffer from the indiscriminate use of the data. A new evaluation function is proposed. By partitioning the image into small blocks and using the maximal squared gradient magnitude in each block to construct the evaluation value, impacts of strong off-boundary gradients deteriorating the performance of the "global" approaches can be suppressed. Experiments on a real-world microscopy video data set show that our method exhibits higher focusing accuracy.