EasyLocate

EasyFind : Significant speed increase, without any loss of accuracy.

EasyImage

• New Gabor filtering function to help with texture analysis and edge detection.
• New inverse circle warp function, providing conversion between polar and cartesian coordinates.

Import of standard datasets into Deep Learning Studio

• Import of COCO Json dataset for EasyLocate or EasySegment Supervised
• Import of YOLO TXT annotations for EasyLocate
• Import of Pascal VOC XML annotations for EasyLocate

• A single API and license for the alignment of region of interest, surface defect detection (particles, scratches, …) and classification with a custom trained Deep Learning classifier.
• Realtime processing for inline surface inspection

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.

• Microsoft Windows 11, 10, 8.1, 7 for x86-64 (64-bit) processor architecture
• Linux for x86-64 (64-bit) and ARMv8-A (64-bit) processor architectures with a glibc version greater or equal to 2.18

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.

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.

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.

Our "Electronic Component" dataset shows how EasyLocate Bounding Box is able to reliably detect and count different kinds of standard electronic components stored in bulk inside plastic bags, in spite of the poor lighting conditions.

EasyLocate is the localization and identification library of Deep Learning Bundle. It is used to locate and identify objects, products, or defects in the image. It has the capability of distinguishing overlapping objects and, as such, EasyLocate is suitable for counting the number of object instances. Two methods are available:

• “EasyLocate Axis Aligned Bounding Box” predicts the bounding box surrounding each object (or defect) it has found in the image and assigns a class label to each of them. It must be trained with images where the objects (or defects) that must be found have been annotated with a bounding box and a class label.
• “EasyLocate Interest Point” predicts the position (as one point, typically the center, but may be otherwise defined) for each object (or defect) it has found in the image and assigns a class label to each of them. All the objects (or defects) in the image must have the same approximate size. It must be trained with images where the objects (or defects) that must be found have simply been annotated with an interest point and a class label. The annotation process is faster with EasyLocate Interest Point as a single click is enough to annotate an object.

• 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 “Ceramic Capacitor” dataset shows how EasyLocate Interest Point is able to reliably detect and count a lot of ceramic capacitors that are overlapping or touching each other.

• 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.