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Open eVision
General Features
Evaluation
Learning
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EasyImage
EasyColor
EasyObject
EasyMatch
EasyFind
EasyGauge
EasyOCV
EasyOCR
EasyBarCode
EasyMatrixCode
Licensing System
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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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Image Processing Library
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Main Features
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Typical Applications
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Convolution and morphology
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Image enhancement
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Geometric transformations
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Image restoration
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Image statistics
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Presence / Absence check
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16-bit accuracy processing
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EasyImage includes operations usually performed as pre-processing steps to improve the image quality and obtain a good contrast
between the background and the objects to be inspected. EasyImage supports gray-level and color images. Selected morphology
functions are also optimized for binary (1-bit per pixel) and bi-level images. EasyImage includes numerous image processing functions,
such as enhancement and restoration by linear or non-linear filtering, arithmetic and logic operations, geometric transformations
for image registration, histogram analysis for thresholding, projection, …
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NEW
Refactoring
improving the execution time due to SSE2 technology.
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NEW
Flexible Masks
for selected image analysis functions. They provide a powerful way of restricting the processing to freely shaped parts of the image.
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NEW
Interest Point Detectors
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Canny edge detector
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The Canny detector is known as the optimal edge detector. It operates on a gray-scale BW8 image
and delivers a black-and-white BW8 image where pixels have only 2 possible values, 0 and 255.
Pixels corresponding to edges in the source image are set to value 255 while other pixels are set
to value 0. The Canny edge detector offers three optimal characteristics for the image processing
applications:
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A good detection: find as many edges in the image as possible
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A good localization: the found edges are as close as possible to the "real" edges in the image
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A minimal response: a single edge response is accepted for each position, i.e. avoiding multiple close or intersecting edge responses
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Harris corner detector
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The Harris corner detector is popular due to its strong invariance to rotation, illumination variation
and image noise. It operates only on a grayscale BW8 image. The Harris Corner Detector delivers
a vector of points of interest. The following characteristics are available for every point of interest:
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The corner position (pixel coordinates with sub-pixel accuracy if enabled)
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The cornerness measure
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The magnitude of the gradient w.r.t. the differentiation scale σd
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The value of the gradient along the X-axis w.r.t. the differentiation scale σd
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The value of the gradient along the Y-axis w.r.t. the differentiation scale σd
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Functions
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Intensity scale transformation functions
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- Gain / Offset change
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- Normalization
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- Uniformization
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- Lookup mapping
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Thresholding
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- Automatic thresholding
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 Min residue, max entropy, isodata
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- Manual thresholding
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Single threshold (absolute and relative)
Double threshold
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- Histogram-based threshold
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Arithmetic and logic operations
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- Arithmetic operations:
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Add, Subtract
Multiply, divide
Copy
Invert, module, shift
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- Logical and bitwise operations:
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AND, OR, XOR, NOT
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- Minimum, maximum
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- Pixel compare
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- Histogram equalization
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Convolution
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- Pre-defined filters for
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Edge detection:
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Laplacian, Gradient, Prewitt, Sobel, Roberts
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Sharpening:
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Several high-pass filters
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Smoothing:
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Several low-pass including Gaussian filter and uniform filters
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Gaussian filter
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- Custom kernel filtering
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Kernel creation and management functions
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Non-linear filtering
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- Morphological operators
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Erosion, dilation
Opening, closing
Thinning, thickening
Top-hat filters
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New
Hit-and-miss transform:
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It detects a particular pattern of foreground and background pixels in an image
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Morphological distance
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- Median filter
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Geometric transformations
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- Image registration (alignment)
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- Horizontal and vertical mirroring
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- Translation, scaling and rotation with optional interpolation
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- LUT-based (un)warping
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Vector operations
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- Projection
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- Profile: sampling (line segment, path, contour) and analysis
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Statistics
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- Measurement of
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- Area, binary moments
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- Weighted moments
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- Gravity center
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- Pixel count and pixel statistics
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- Minimum and maximum gray-level value
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- Average, variance and standard deviation
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- Histogram computation and analysis
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- Image focus
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Noise reduction and estimation
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- Spatial noise reduction
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Convolution
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Median filters
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- Temporal noise reduction
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Recursive average
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Moving average
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Average
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- Noise estimation
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Root-mean-square noise
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Signal-to-noise ratio
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Operation on interfaced video frames
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- Elimination of the interlaced images artifacts by
rebuilding or re-aligning fields
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Feature point detectors
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- Harris corner detector
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- Canny edge detector
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Other operations
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- Overlay
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- Scalar gradient
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