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New version 23.05 extends HALCON's comprehensive software libraryNew Deep Counting feature for counting large quantitiesRelease on May 23, 2023

Munich, April 13, 2023 - MVTec Software GmbH (www.mvtec.com), a leading international software manufacturer for machine vision worldwide, will launch version 23.05 of the standard machine vision software HALCON on May 23, 2023. The focus of the new release is deep learning methods. The main feature here is Deep Counting, a deep-learning-based method that can robustly count large quantities of objects. In addition, improvements for the training of the deep learning technologies 3D Gripping Point Detection as well as Deep OCR have been integrated into the new HALCON version. With HALCON 23.05, it is now possible to further optimize the underlying deep learning networks, which are already pre-trained on industry-related images, for the user's own application. This allows even more robust recognition rates for Deep OCR applications as well as an even more reliable detection of suitable gripping surfaces for applications using 3D Gripping Point Detection technology. In addition, there are many other helpful improvements, such as the fact that external code can now be integrated into HALCON more easily.

Training for Deep OCRDeep OCR reads texts in a very robust way, even regardless of their orientation and font. For this purpose, the technology first detects the relevant text within the image and then reads it. With HALCON 23.05, it's now also possible to fine-tune the text detection by retraining the pretrained network with application-specific images. This provides even more robust results and opens new application possibilities. For example: the detection of text with arbitrary printing type or unseen character types as well as an improved readability in noisy, low contrast environments.

Training for 3D Gripping Point Detection3D Gripping Point Detection can be used to robustly detect surfaces on any object that is suitable for gripping with suction. In HALCON 23.05 there is now the possibility to retrain the pretrained model with own application-specific image data. The grippable surfaces are thus recognized even more robustly. The necessary labeling is done easily and efficiently via the MVTec Deep Learning Tool.

Easy Extensions InterfaceWith the help of HALCON extension packages the integration of external programming languages is possible. The advantage for customers: Functionalities that go beyond pure image processing can thus be covered by HALCON. In HALCON 23.05, the integration of external code has become much easier with the Easy Extensions Interface. This allows users to make their own functions written in .NET code usable in HDevelop and HDevEngine in just a few steps, while benefiting from the wide range of functionalities offered by the .NET framework. Even the data types and HALCON operators known from the HALCON/.NET language interface can be used. This increases both the flexibility and the application possibilities of HALCON.

About MVTec Software GmbHMVTec is a leading manufacturer of standard software for machine vision. MVTec products are used in all demanding areas of imaging: semiconductor industry, surface inspection, automatic optical inspection systems, quality control, metrology, as well as medicine and surveillance. By providing modern technologies such as 3D vision, deep learning, and embedded vision, software by MVTec also enables new automation solutions for the Industrial Internet of Things aka Industry 4.0. With locations in Germany, the USA, and China, as well as an established network of international distributors, MVTec is represented in more than 35 countries worldwide. http://www.mvtec.com

About MVTec HALCONMVTec HALCON is the comprehensive standard software for machine vision with an integrated development environment (HDevelop) that is used worldwide. It enables cost savings and improved time to market. HALCON's flexible architecture facilitates rapid development of any kind of machine vision application. MVTec HALCON provides outstanding performance and a comprehensive support of multi-core platforms, special instruction sets like AVX2 and NEON, as well as GPU acceleration. It serves all industries, with a library used in hundreds of thousands of installations in all areas of imaging like blob analysis, morphology, matching, measuring, and identification. The software provides the latest state-of-the-art machine vision technologies, such as comprehensive 3D vision and deep learning algorithms. The software secures your investment by supporting a wide range of operating systems and providing interfaces to hundreds of industrial cameras and frame grabbers, in particular by supporting standards like GenICam, GigE Vision, and USB3 Vision. By default, MVTec HALCON runs on Arm-based embedded vision platforms. It can also be ported to various target platforms. Thus, the software is ideally suited for the use within embedded and customized systems. http://www.halcon.com, http://www.embedded-vision-software.com

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MVTec further expands HALCON functionality with new deep ... - Robotics Tomorrow

Multimodal Deep Learning and its Applications

As humans, our perception of the world is through our senses. We identify objects or anything through vision, sound, touch, and odor. Our way of processing this sensory information is multimodal. Modality refers to the way something is recognized, experienced, and recorded. Multimodal deep learning is an extensive research branch in Deep learning that works on the fusion of multimodal data.

The human brain consists of millions of neural networks that process multiple modalities from the external world. It could be recognizing a persons body movements, tone of voice, or even mimicking sounds. For AI to interpret Human Intelligence, we need a reasonable fusion of multimodal data and this is done through Multimodal Deep Learning.

Multimodal Machine Learning is developing computer algorithms that learn and predict using Multimodal datasets.

Multimodal Deep learning is a subset of the machine learning branch. With this technology, AI models are trained to identify relationships between multiple modalities such as images, videos, and texts and provide accurate predictions. From identifying the relevant link between datasets, Deep Learning models will be able to capture any place's environment and a person's emotional state.

If we say, Unimodal models that interpret only a single dataset have proven efficient in computer vision and Natural Language Processing. Unimodal models have limited capabilities; in certain tasks, these models failed to recognize humor, sarcasm, and hate speech. Whereas, Multimodal learning models can be referred to as a combination of unimodal models.

Multimodal deep learning includes modalities like visual, audio, and textual datasets. 3D visual and LiDAR data are slightly used multimodal data.

Multimodal Learning models work on the fusion of multiple unimodal neural networks.

First unimodal neural networks process the data separately and encode them, later, the encoded data is extracted and fused. Multimodal data fusion is an important process carried out using multiple fusion techniques. Finally, with the fusion of multimodal data, neural networks recognize and predict the outcome of the input key.

For example, in any video, there might be two unimodal models visual data and audio data. The perfect synchronization of both unimodal datasets provides simultaneous working of both models.

Fusing multimodal datasets improves the accuracy and robustness of Deep learning models, enhancing their performance in real-time scenarios.

Multimodal Deep learning has potential applications in computer vision algorithms. Here are some of its applications;

The research to reduce human efforts and develop machines matching with human intelligence is enormous. This requires multimodal datasets that can be combined using Machine Learning and Deep Learning models, paving the way for more advanced AI tools.

The recent surge in the popularity of AI tools has brought more additional investments in Artificial Intelligence and Machine Learning technology. This is a great time to grab job opportunities by learning and upskilling yourself in Artificial Intelligence and Machine Learning.

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Multimodal Deep Learning - A Fusion of Multiple Modalities - NASSCOM Community

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Transport for NSW (TfNSW) is installing a number of machine learning traffic counting and classifying cameras across the state.

Images of heavy vehicles are taken by the cameras, which then classify the type of vehicle in transit and the type of cargo being transported.

In a bulletin to industry TfNSW said the information collected helps to shape the future of freight, to better understand freight movements, improve road safety, and enable more efficient deliveries.

The cameras are not used for enforcement or monitoring people or private vehicles, said TfNSW.

Truckies can expect to see cameras installed at the following locations over the coming weeks:

According to thefact sheet on the cameras webpage, there is a radar sensor and camera on the unit that takes a picture of the heavy vehicles when certain criteria are met.

After the picture is taken, artificial intelligence within the unit can tell the difference between different types of heavy vehicles, for example, a container carrying heavy vehicle, B-double or semi-trailer.

The units are also able to track changes in load. If a shipping container truck entered a location carrying one container and left with two containers the platform contains a record of this change.

Aside from the above locations, TfNSw says similar cameras can also be found at:

For more information, visit the Machine Learning cameras webpagewhich includes a factsheet with details about what these units are and what they do.

If you have any questions or would like more information, you can contact the TfNSW project team at freight@transport.nsw.gov.au.

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More 'machine learning' cameras to track truckies through NSW - Big Rigs

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More

Less than a decade ago, the prevailing wisdom was that every business should undergo digital transformations to boost internal operations and improve client relationships. Next, they were being told that cloud workloads are the future and that elastic computer solutions enabled them to operate in an agile and more cost-effective manner, scaling up and down as needed.

While digital transformations and cloud migrations are undoubtedly smart decisions that all organizations should make (and those that havent yet, what are you doing!), security systems meant to protect such IT infrastructures havent been able to keep pace with threats capable of undermining them.

As internal business operations become increasingly digitized, boatloads more data are being produced. With data piling up, IT and cloud security systems come under increased pressure because more data leads to greater threats of security breaches.

In early 2022, a cyber extortion gang known as Lapsus$ went on a hacking spree, stealing source code and other valuable data from prominent companies, including Nvidia, Samsung, Microsoft and Ubisoft. The attackers had originally exploited the companies networks using phishing attacks, which led to a contractor being compromised, giving the hackers all the access the contractor had via Okta (an ID and authentication service). Source code and other files were then leaked online.

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This attack and numerous other data breaches target organizations of all types, ranging from large multinational corporations to small startups and growing firms. Unfortunately, in most organizations, there are simply too many data points for security engineers to locate, meaning current systems and methods to safeguard a network are fundamentally flawed.

Additionally, organizations are often overwhelmed by the various available tools to tackle these security challenges. Too many tools means organizations invest an exorbitant amount of time and energy not to mention resources in researching, purchasing and then integrating and running these tools. This puts added stress on executives and IT teams.

With so many moving parts, even the best security engineers are left helpless in trying to mitigate potential vulnerabilities in a network. Most organizations simply dont have the resources to make cybersecurity investments.

As a result, they are subject to a double-edged sword: Their business operations rely on the highest levels of security, but achieving that comes at a cost that most organizations simply cant afford.

A new approach to computer security is desperately needed to safeguard businesses and organizations sensitive data. The current standard approach comprises rules-based systems, usually with multiple tools to cover all bases. This practice leaves security analysts wasting time enabling and disabling rules and logging in and out of different systems in an attempt to establish what is and what isnt considered a threat.

The best option for organizations dealing with these ever-present pain points is to leverage machine learning (ML) algorithms. This way, algorithms can train a model based on behaviors, providing any business or organization a secure IT infrastructure. A tailored ML-based SaaS platform that operates efficiently and in a timely manner must be the priority of any organization or business seeking to revamp its security infrastructure.

Cloud-native application protection platforms (CNAPP), a security and compliance solution, can empower IT security teams to deploy and run secure cloud native applications in automated public cloud environments. CNAPPs can apply ML algorithms on cloud-based data to discover accounts with unusual permissions (one of the most common and undetected attack paths) and uncover potential threats including host and open source vulnerabilities.

ML can also knit together many anomalous data points to create rich stories of whats happening in a given network something that would take a human analyst days or weeks to uncover.

These platforms leverage ML through two primary practices. Cloud security posture management (CSPM) handles platform security by monitoring and delivering a full inventory to identify any deviations from customized security objectives and standard frameworks.

Cloud infrastructure entitlements management (CIEM) focuses on identity security by understanding all possible access to sensitive data through every identitys permission. On top of this, host and container vulnerabilities are also taken into account, meaning correct urgency can be applied to ongoing attacks. For example, anomalous behavior seen on a host with known vulnerabilities is far more pressing than on a host without known vulnerabilities.

Another ML-based SaaS option is to outsource the security operations center (SOC) and security incident and event management (SIEM) function to a third party and benefit from their ML algorithm. With dedicated security analysts investigating any and all threats, SaaS can use ML to handle critical security functions such as network monitoring, log management, single-sign on (SSO) and endpoint alerts, as well as access gateways.

SaaS ML platforms offer the most effective way to cover all the security bases. By applying ML to all behaviors, organizations can focus on their business objectives while algorithms pull all the necessary context and insights into a single security platform.

Running the complex ML algorithms to learn a baseline of what is normal in a given network and assessing risk is challenging even if an organization has the personnel to make it a reality. For the majority of organizations, using third-party platforms that have already built algorithms to be trained on data produces a more scalable and secure network infrastructure, doing so far more conveniently and effectively than home grown options.

Relying on a trusted third party to host a SaaS ML platform enables organizations to dedicate more time to internal needs, while the algorithms study the networks behavior to provide the highest levels of security.

When it comes to network security, relying on a trusted third party is no different than hiring a locksmith to repair the locks on your home. Most of us dont know how the locks on our homes work but we trust an outside expert to get the job done. Turning to third-party experts to run ML-algorithms enables businesses and organizations the flexibility and agility they need to operate in todays digital environment.

Maximizing this new approach to security allows all types of organizations to overcome their complex data problems without having to worry about the resources and tools needed to protect their network, providing unparalleled peace of mind.

Ganesh the Awesome (Steven Puddephatt) is a technical sales architect at GlobalDots.

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