Drvision Technologies LLC

Michael William Jones

Shih-Jong James Lee

Luciano Andre Guerreiro Lucas

Casey James McBride

Bryce Graff

Hoyin Lai

A computerized automated parameterization image pattern detection and classification method performs (1) morphological metrics learning using labeled region data to generate morphological metrics; (2) intensity metrics learning using learning image and labeled region data to generate intensity metrics; and (3) population learning using the morphological metrics and the intensity metrics to generate learned pattern detection parameter. The method may further update the learned pattern detection parameter using additional labeled region data and learning image, and apply pattern detection with optional user parameter adjustment to image data to generate detected pattern. The method may alternatively perform pixel parameter learning and pixel classification to generate pixel class confidence, and uses the pixel class confidence and the labeled region data to perform pattern parameter learning to generate the learned pattern detection parameter. The method may further perform pattern classification learning to generate pattern classifier which is used to generate classified pattern.

Automated parameterization image pattern recognition method

A computerized efficient machine learning method for classification of data and new class discovery inputs labeled data and unlabeled data into a computer memory for a computerized machine tool to perform (a) initial supervised learning using the labeled data to generate a classifier, (b) semi-supervised learning using the labeled data, the classifier and the unlabeled data to generate an updated classifier and high confidence data, (c) active learning using the updated classifier and the unlabeled data to generate a data label request and receive new class labeled data to generate augmented labeled data, (d) new class discovery using the updated classifier and the data label request to generate data of potential new classes and receive labels for potential new class data to generate new class labeled data, and (e) supervised learning using the high confidence data, the labeled data and the augmented labeled data to generate an output classifier.

Efficient machine learning method

A computerized mask edit guided processing method for time-lapse image analysis performs by a computer program an assisted mask editing on an input image sequence to generate mask edit data, and performs a mask edit guided processing using the image sequence and the mask edit data. A computerized track edit guided processing method for time-lapse image analysis performs by a computer program an assisted track editing on an input image sequence to generate track edit data, and performs a track edit guided processing using the image sequence and the track edit data. A computerized edit guided processing method for time-lapse image analysis performs by a computer program a combination of assisted mask editing and assisted track editing on an input image sequence to generate edit data, and performs a combination of mask edit guided processing and track edit guided processing using the image sequence and the edit data.

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Kenmore, WA, United States of America

Michael William Jones