# Display Filter and Sorting Examples

## Blue Locate Tool Filter Examples

Display Filter | Description
--- | ---
count(match)=0 | This filter corresponds to the no matches option in the Display drop-down menu. Only views that do not contain matched models are displayed.
count(feature A feature is a visually distinguishable area in an image. Features typically represent something of interest for the application (a defect, an object, a particular component of an object).[t]/x in [500,600]) > 0 and count(feature[t]/y in [300,400]) > 0 | Returns Views where at least one feature (ID = t) is found in the specified ranges of horizontal and vertical positions.
count(match[Model1]/scale in [0.9,1])>0 and count(match[Model1]/angle in [0,90]) > 0 | Returns Views where at least one model A specific spatial arrangement of a set of features (Blue Locate and Blue Read tools only.) During a post-processing step, the Blue Locate and Blue Read tools can fit all of the features detected in an image to the models defined for the tool. The overall pose and identity of the model is then returned. (Model name = Model1) is found, among marked features, and has its scale and orientation angle in the specified range of values.
match[Model1]/features<5 | Returns Views where the model (Model name = Model1) is found and matched with less than five marked.
features.min(match/features)=2 | Returns Views where the minimal number of features used to match any of the models is exactly two.
min(feature[h]/x) > 500 | Returns Views where the leftmost feature (h is located at least 500 pixels away from the left image border).

## Green Classify Tool Filter Examples

Display Filter | Description
--- | ---
tag!/score < max(tag/score) or score < threshold tag!/score < score or score < threshold | These filters actually correspond to the display filter mismatch from the drop-down menu, whenever threshold is the processing threshold. It filters views for which the actual class (the given tag) probability is lower than the maximal probability among all classes (score is equivalent to max(tag/score)); in other words, the wrong tag/class is predicted, as well as views for which the maximal probability score is lower than the processing threshold, in other words, no tag/class is predicted.
not trained and tag![cat] and tag[dog]/score = max(tag/score) not trained and tag![cat] and tag[dog]/score = score | Filters views that were not used for training Training is the process that your tool, which is a neural network, is learning about the features (pixels) based on the labels you made. For example, a tool will learn the defect/normal pixels in each image based on the defect/normal labels you drew. The goal of the tool Training is learning enough to give the correct inspection results of whether an unseen image is defective or not. The key to training is to ensure that you include all possible variations within your training set, and that your images are accurately labeled. Training times vary by the application, tool setup and the GPU in the PC being used to train the network., that are labeled with the tag cat, but for which the tag dog is predicted.

## Red Analyze Tool Filter Examples

Display Filter | Description
--- | ---
bad and count(region!/score > T2) <= 2 | Returns views labeled as bad, which have two or less labeled defective regions with a score higher than the upper threshold (T2).
count(region) > 1 and max(region/area) < 200 | Returns views with more than one marked (found by the system) defective region and the largest defect area smaller than 200 pixels.
count(region/x in [100,200]) >= 2 | Returns views with at least two defective regions, whose center is located in the specified range of horizontal positions.