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Machine Vision Newsletter

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Sapera Processing
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SAPERA Software Architecture

Overview

Sapera Processing is a Windows®- based comprehensive programming library for image processing and analysis designed to simplify vision application development, Sapera Processing offers a comprehensive set of optimised tools, as a suite or standalone. While Sapera Processing is optimised for use with Dalsa Coreco's boards, it is hardware independent to facilitate portability across 3rd party platforms. Sapera uses high-performance C++ classes and MMX, SSE (streaming SIMD Extensions) and SSE2 to meet the challenging operational requirements of today's imaging systems. The Sapera Processing tool set includes:

Image Processing Libraries
A series of highly optimised basic image processing functions, including: filtering, geometry, measurement, morphology, point-to-point, segmentation, transform operations and subpixel edge detection.

Search (pattern matching)
Matches patterns to determine location and alignment, and includes correlation-based and geometric-based algorithms to achieve accuracy up to 1/50th of a pixel. Designed to work under poor and uneven lighting conditions, Search's fast, robust and accurate algorithms are well suited for demanding alignment applications.

Optical Character Recognition (OCR)
Supports both solid and dot matrix fonts, standard and user trainable fonts, scale/ aspect ratio invariant, works well on degraded and poorly illuminated images.

Barcode/Decoding
Locates and decodes "UNI" and 2-dimensional bar codes for a variety of standard codes. Designed to operate on degraded and poorly illuminated images, the Barcode tool includes rapid, robust algorithms and functions, and is fully rotational/scale invariant.

Blob Analysis
Separates foreground objects from the background and calculates more than 50 spatial and grayscale blob features, including: area, location, perimeter, elongation, roundness, convexity, bounding box and orientation.

Artificial Intelligence
Artificial intelligence (AI) inference based on models imported from AstrocyteTM training tool.

 

Features
- Hardware independent, image acquisition, image processing/analysis and artificial intelligence library
- Supports Area-scan and Line-scan, monochrome and color, 2D and 3D cameras
- Power tools for tasks like image recognition, object identification, 2D/3D measurement, machine guidance, surface inspection, object segmentation, object tracking and others
- Designed for machine vision OEMs, System Integrators and End users
- Modular components supporting C++ and .NET languages
- Multi-Core optimisation supported on single or multiple CPU configurations
- User-friendly non-programming graphical environment to quickly prototype and test drive application specific imaging tools

 

Sapera Processing includes a series of classes to perform AI inference on models generated by Astrocyte™ (available only with Sapera AI license).
 
Noise Reduction
Noise reduction intends to deliver a higher-quality image from its original state. It is an important component in applications such as digital photography, medical image analysis, remote sensing, surveillance and digital entertainment.
 
Segmentation
Image segmentation is important component in computer vision and is used for defect sorting/qualification, food sorting, shape analysis, etc. Image segmentation involves dividing input image into segments to simplify image analysis.
 
Object Detection
Object Detection involves identifying one or more objects of interest in an image. Object Detection is used to solve problems like presence detection, object tracking, defect localisation and sorting, etc.
 
Object Classification
Classification involves predicting which class an item belongs to. Classification is used to solve problems like defect identification, character recognition, presence detection, food sorting, etc.
Anomaly Detection
Anomaly Detection is the identification of rare occurrences, items or events of concern due to their differing characteristics from majority of the processed data. Anomaly Detection is a binary classifier dedicated to identifying good and bad samples. Unlike regular classification Anomaly Detection can train on unbalanced datasets (i.e. large number of good samples and small number of bad samples). Anomaly Detection is used on any application involving identification of defects on a surface or scene.
 

 

 

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