We propose to teach deformable models to find object boundaries in low-quality images. We will do so in two different domains, and we will show how to choose what to teach the model---that is, what details should guide it. The training consists of looking at a body of ground-truth data and finding the correlation between an object's shape and various aspects of the image it lies in. Segmentation is then done by finding the shape that maximizes this correlation in an input image. We will use statistics on practical measures of segmentation success to choose the best shape and image features to correlate. We will then evaluate this best algorithm on data we did not train on. We will test the training's effectiveness in the domains ...
This study introduces an interactive image segmentation algorithm for extraction of ill-defined edge...
Automated medical image segmentation is a challenging task that benefits from the use of effective i...
image segmentation using deformable surfaces H Delingette, M Hebert and K lkeuchi W e present a tech...
We describe how to teach deformable models (snakes) to find object boundaries based on user-specifie...
In deformable model segmentation, the geometric training process plays a crucial role in providing s...
Medical images are challenging for segmentation. Deformable models proved to be one of the most effe...
A method for deformable shape detection and recognition is described. Deformable shape templates are...
International audienceIn this work we propose a machine learning approach to improve shape detection...
International audienceIn this work we propose a machine learning approach to improve shape detection...
We present the Deformable Probability Maps (DPMs) for object segmentation, which are graphical learn...
International audienceIn this work we propose a machine learning approach to improve shape detection...
Deformable shape models (DSMs) comprise a general approach that shows great promise for automatic im...
Deformable shape models (DSMs) comprise a general approach that shows great promise for automatic im...
Automated medical image segmentation is a challenging task that benefits from the use of effective i...
In this work we propose a machine learning approach to improve shape detection accuracy in medical i...
This study introduces an interactive image segmentation algorithm for extraction of ill-defined edge...
Automated medical image segmentation is a challenging task that benefits from the use of effective i...
image segmentation using deformable surfaces H Delingette, M Hebert and K lkeuchi W e present a tech...
We describe how to teach deformable models (snakes) to find object boundaries based on user-specifie...
In deformable model segmentation, the geometric training process plays a crucial role in providing s...
Medical images are challenging for segmentation. Deformable models proved to be one of the most effe...
A method for deformable shape detection and recognition is described. Deformable shape templates are...
International audienceIn this work we propose a machine learning approach to improve shape detection...
International audienceIn this work we propose a machine learning approach to improve shape detection...
We present the Deformable Probability Maps (DPMs) for object segmentation, which are graphical learn...
International audienceIn this work we propose a machine learning approach to improve shape detection...
Deformable shape models (DSMs) comprise a general approach that shows great promise for automatic im...
Deformable shape models (DSMs) comprise a general approach that shows great promise for automatic im...
Automated medical image segmentation is a challenging task that benefits from the use of effective i...
In this work we propose a machine learning approach to improve shape detection accuracy in medical i...
This study introduces an interactive image segmentation algorithm for extraction of ill-defined edge...
Automated medical image segmentation is a challenging task that benefits from the use of effective i...
image segmentation using deformable surfaces H Delingette, M Hebert and K lkeuchi W e present a tech...