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Open eVision
General Features
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EasyImage
EasyColor
EasyObject
EasyMatch
EasyFind
EasyGauge
EasyOCV
EasyOCR
EasyBarCode
EasyMatrixCode
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New Acquisition Front Ends
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ActiveGigE for Open eVision
ActiveDcam for Open eVision
Evaluation Programs
Data Sheet
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Blob analysis Library
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Main Features
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Typical Applications
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Image segmentation
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Surface inspection
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Object labeling
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Packaging inspection
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Geometric feature extraction
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Object location
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Flexible Masks
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High performance, especially for large images and images with numerous objects
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The EasyObject library handles image segmentation, i.e. the decomposition of images into separate objects, also called blobs. Once
the objects have been constructed, they can be handled as independent entities. Various geometric parameters or features, such as
area, width, or ellipse of inertia, can be computed on them. Then the objects of interest can be selected by means of their position or
of their computed features. EasyObject also supports the inspection of holes in defined objects. Holes are managed as the objects
themselves, benefiting from the same geometrical features. The EasyObject library manages the relationship between objects and
holes, defining parent objects for holes.
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NEW
Flexible Masks
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EasyObject supports the restriction of the blob analysis to complex- or disconnected-shape
regions of the image thanks to the Flexible Masks that are available for the encoding functions.
EasyImage
can also generate Flexible Masks from an encoded image.
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NEW
Refactoring
globally improving the execution time, especially for large images and images with numerous objects.
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NEW
Object-Oriented API
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EasyObject is accessible through a brand-new object oriented API
which brings several majors improvements to the library
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- Separate classes according to the different concepts such as
encoding, object feature extraction, object selection
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- Improved access to the features of the objects. The objects and the holes can be efficiently accessed randomly
 (i.e. in an indexbased fashion)
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Functions
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Image Encoding:
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- Construction of the runs
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Segmentation
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- Grayscale single threshold
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- Grayscale double threshold
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- Color single threshold
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- Color range threshold
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- Reference image
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- Image range
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- Labeled Image
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- Binary Image
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Pixel aggregation
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- Object construction: aggregation of the runs into objects
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- Hole construction: aggregation of the runs into holes
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- Continuous mode for web inspection applications using line-scan cameras
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Object feature extraction: geometric parameters computation
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Object selection and sorting according to any feature value
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Blob Features
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Computable features
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Position
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- Limit (top, bottom, left, right)
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- Gravity center (X and Y)
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- Weight gravity center (X and Y)
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Extent
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- Area (pixel count)
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- Feret box (center X and Y, height, width with distinct orientation angles at 22, 45, 68 degrees) and
Bounding box (center X and Y, height, width)
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- Minimum enclosing rectangle (angle, center X and Y, heath, width)
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Miscellaneous
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- Starting point of the object contour (X and Y)
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- Largest run
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- Run count
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- Object number (index)
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Statistics
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- Pixel gray-level value (average, deviation, variance)
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- Pixel gray-level value (min and max)
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Ellipse of Inertia
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- Eccentricity of the ellipse of inertia
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- Ellipse
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- Second order geometric moments
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Convex hull
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The convex hull of a shape is the convex polygon of minimum area that completely surrounds an object.
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The convex hull can be used to characterize the object footprint, as well as to observe concavities.
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Graphic representation
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The objects can be drawn onto the source image. The following blob features have a pre-set graphical representation:
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- Objects
- Diagonals
- Bounding box
- Convex hull
- Ellipse
- Feret Box
- Feret box with an angle of 22°
- Feret box with an angle of 45°
- Feret box with an angle of 68°
- Gravity center
- Minimum enclosing rectangle
- Weighted gravity center
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