Object identification and Segmentation

Apply advanced image segmentation and object detection using a big selection of different methods that consider both the spectral and spatial information in your images.

Breeze includes tools based on machine learning and the latest in neural networks and deep learning, with support for applying customer-provided models such as YOLO and Fast SAM in ONNX format, and custom processing via a Python interface. In addition to this you can do manual selection of regions of interests, and apply pixel binning, pixel coordinates and many others.

Spectral based segmentation

Apply methods that use the spectral differences in your data to do image segmentation and detect objects, such as machine learning and chemometrics models as well as band method functions.

Shape based analysis

Using the spatial information related to the shape of objects, you can apply methods based on deep learning and neural networks. The Breeze software includes the pre-trained FastSAM neural network for shape-based analysis, which can be used directly without any need for additional training. Other models, such as YOLO and Faster R-CNN, must be created and trained externally by the user, then converted to ONNX format for use within the software.

Other segmentation

Other segmentations based on pixel coordinates or manual selection of ROI are also available options. To make use of the data each pixel can provide, Breeze also includes segmentation options for dividing an object into subsections or selecting a desired amount of pixels from each object.