EasySegment

• A new code reader tool ECodeReader allowing to read multiple code types from a single integrated interface
• Deep Learning Studio is now available on Linux ARM-64 platforms
• Neural Network “engines” allow additional devices support (OpenVINO, Tensor RT) and increased speed.
• EasyBarCode2 now supports learning from images to improve its capabilities on similar images.

• A new assisted segmentation tool is available in Deep Learning Studio for the EasySegment Supervised annotations. It provides a quick and efficient way to get pixel- perfect annotations.
• EasyFind has been extended to support :
  • Vector learning patterns. This extension is well- suited to find objects with a known geometry.
  • Pattern matching using feature point technology
• EasyBarCode2 and EasyQRCode now support the GS1 symbology.
• EasyMatrixCode2 now supports the Data Matrix Rectangular Extension (ISO/IEC 21471, DMRE).

• EasyLocate Interest Point : New working mode (alternative to EasyLocate Axis Aligned Bounding Box) simplifying the annotation of the dataset and the configuration of the tools when all objects have the same size. A single click is enough to annotate an EasyLocate Interest Point object.
• EasyGauge : New Polygon Gauge in addition to Point, Line, Rectangle, Circle and Wedge gauges.
• Deep Learning tools now supporting GPU processing on NVIDIA Jetson.
• Faster compilation time with simplified C++ headers and reduced calling cost to the Open eVision API.

All Open eVision libraries are now also available for embedded ARM devices.

Our “Coffee” sample dataset shows how the supervised mode of EasySegment can be used to efficiently detect and segment foreign materials on a production line, even when the foreign materials’ color and texture are very close to the product of interest.

Neural Networks are computing systems inspired by the biological neural networks that constitute the human brain. Convolutional Neural Networks (CNN) are a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing images. Deep Learning uses large CNNs to solve complex problems difficult or impossible to solve with so-called conventional computer vision algorithms. Deep Learning algorithms may be easier to use as they typically learn by example. They do not require the user to figure out how to classify or inspect parts. Instead, in an initial training phase, they learn just by being shown many images of the parts to be inspected. After successful training, they can be used to classify parts, or detect and segment defects.

EasySegment is the segmentation tool of Deep Learning Bundle. EasySegment performs defect detection and segmentation. It identifies parts that contain defects, and precisely pinpoints where they are in the image. The supervised mode of EasySegment works by learning a model of what is a defect and what is a “good” part in an image. This is done by training with images annotated with the expected segmentation. Then, the tool can be used to detect and segment the defects in new images. The supervised mode of EasySegment achieves better precision and can segment more complex defects than the unsupervised mode thanks to the knowledge of the expected segmentation.

Deep Learning works by training a neural network, teaching it how to classify a set of reference images. The performance of the process highly depends on how representative and extensive the set of reference images is. Deep Learning Bundle implements “data augmentation”, which creates additional reference images by modifying (for example by shifting, rotating, scaling) existing reference images within programmable limits. This allows Deep Learning Bundle to work with as few as one hundred training images per class.

Open eVision includes the free Deep Learning Studio application. This application assists the user during the creation of the dataset as well as the training and testing of the deep learning tool. For EasySegment, Deep Learning Studio integrates an annotation tool and can transform prediction into ground truth annotation. It also allows to graphically configure the tool to fit performance requirements. For example, after training, one can choose a tradeoff between a better defect detection rate or a better good detection rate.

Deep Learning generally requires significant amounts of processing power, especially during the learning phase. Deep Learning Bundle supports standard CPUs and automatically detects Nvidia CUDA-compatible GPUs in the PC. Using a single GPU typically accelerates the learning and the processing phases by a factor of 100.

• Neo is the new Licensing System of Euresys. It is reliable, state-of-the-art, and is now available to store Open eVision and eGrabber licenses.
• Neo allows you to choose where to activate your licenses, either on a Neo Dongle or in a Neo Software Container. You buy a license, you decide later.
• Neo Dongles offer a sturdy hardware and provide the flexibility to be transferred from a computer to another.
• Neo Software Containers do not need any dedicated hardware, and instead are linked to the computer on which they have been activated.
• Neo ships with its own, dedicated, Neo License Manager, which comes in two flavours: an intuitive, easy to use, Graphical User Interface and a Command Line Interface that allows for easy automation of Neo licensing procedures.

EasySegment is the segmentation tool of Deep Learning Bundle. EasySegment performs defect detection and segmentation. It identifies parts that contain defects, and precisely pinpoints where they are in the image. The unsupervised mode of EasySegment works by learning a model of what is a “good” sample (i.e. a sample without any defect). This is done by training it only with images of “good” samples. Then, the tool can be used to classify new images as good or defective and segment the defects from these images. By training only with images of good samples, the unsupervised mode of EasySegment is able to perform inspection even when the type of defect is not known beforehand or when defective samples are not readily available.

Deep Learning is generally not suitable for applications requiring precise measurement or gauging. It is also not recommended when some types of errors (such as false negative) are completely unacceptable. The unsupervised mode of EasySegment is good for defect detection and segmentation tasks, especially when defectives samples are hard to come by. Deep Learning tools usually work very well with images of natural or manufactured objects that have complex surface patterns (e.g. wood, fabric, …) that make the detection of defects by conventional machine vision algorithm very hard. Besides, the "learn by example" paradigm of Deep Learning can also reduce the development time of a computer vision process.

• Deep Learning Bundle has been tailored, parametrized and optimized for analyzing images, particularly for machine vision applications.
• Deep Learning Bundle has a simple API and the user can benefit from the power of deep learning technologies with only a few lines of code.
• Try before you buy: Deep Learning Bundle comes with the free Deep Learning Studio training and evaluation application.

Our “Fabric” sample dataset shows how the unsupervised mode of EasySegment can be used to detect and segment defects in Fabric with only a few good samples for training and no knowledge about what kind of defects are expected. Moreover, the unsupervised mode of EasySegment can be used to ease the annotation of the expected segmentation required for the supervised mode by reviewing and importing the results of the unsupervised mode as ground truth.

• Windows 7 to Windows 10 x86 (32-bits) and x86-64 (64-bits)
• Windows 11 x86-64 (64-bits)
• Linux 64 bits (x86-64 and ARMv8-A) with a glibc version 2.18 or newer