Tuesday, 7 November 2017

WHAT ARE VISION INSPECTION SYSTEMS?

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VISION INSPECTION SYSTEMS (sometimes referred to as machine vision systems) provide image-based inspection automated for your convenience for a variety of industrial and manufacturing applications. Though not a new technology, 2D and 3D machine vision systems are now commonly used for automated inspection, robot guidance, quality control and sorting, and much more.

WHAT VISION INSPECTION SYSTEMS CAN DO

These intelligent inspection systems come equipped with a camera or multiple cameras, and even video and lighting. Vision systems are capable of measuring parts, verifying parts are in the correct position, and recognizing the shape of parts. Also, vision systems can measure and sort parts at high speeds. Computer software processes images captured during the process you are trying to assess to capture data. The vision system can be intelligent enough to make decisions that impact the function you are trying to assess, often in a pass/fail capacity to trigger an operator to act. These systems can be embedded into your lines to provide a constant stream of information.

APPLICATIONS FOR VISION INSPECTION SYSTEMS

VISION INSPECTION SYSTEMS can be used in any number of industries in which quality control is necessary. For example, vision systems can assist robotic systems to obtain the positioning of parts to further automate and streamline the manufacturing process. Data collected by a vision system can help improve efficiency in manufacturing lines, sorting, packing and other applications. In addition, the information captured by the vision system can identify problems with the manufacturing line or other function you are examining in an effort to improve efficiency, stop inefficient or ineffective processes, and identify unacceptable products.

INDUSTRIES USING VISION SYSTEMS FOR INSPECTION

Because vision inspection systems combine various technologies, the design of these systems can be customized to meet the needs of many industries. Thus, many companies enjoy the use of this technology for quality control purposes, and even security purposes. Industries using vision inspection systems include automation, robotics, pharmaceuticals, packaging, automotive, food and beverage, semiconductors, life sciences, medical imaging, electronics, consumer goods among other kinds of manufacturing and non-manufacturing companies.

BENEFITS OF VISION INSPECTION SYSTEMS

Overall, the benefits of VISION INSPECTION SYSTEMS , include, but are not limited to, production improvements, increased uptime, and reduction in expenses. Vision systems allow companies to conduct 100% inspection of parts for quality control purposes. This ensures that all products will meet the customers’ specifications. If you want to improve the quality and efficiency of your industry, a vision inspection system could be the answer for you.






TO KNOW MORE ABOUT VISUAL INSPECTION SYSTEM IN INDIA, CONTACT MENZEL VISION AND ROBOTICS PVT LTD AT (+ 91) 22 67993158 OR EMAIL US AT INFO@MVRPL.COM


Tuesday, 24 October 2017

VISION INSPECTION SYSTEMS: WHAT TO KNOW BEFORE IMPLEMENTATION




VISION INSPECTION SYSTEMS are popular mechanisms in the industrial sector because of their accuracy, repeatability and efficiency. They provide numerous advantages over human inspection of parts during production.
However, Vision inspection systems are complicated systems with a lot of variables that affect equipment that needs to be implemented correctly in order to realize the long-term benefits.
So what do you need to know before implementation to realize the full benefits of VISION INSPECTION SYSTEMS?

KNOW YOUR EQUIPMENT AND ENVIRONMENT

Implementation of vision inspection systems will often involve integration with existing production equipment and processes, so it’s important to understand how your cameras will fit in with this equipment and the production environment.
Will the integration involve conveyors, product rejection mechanisms, pick and place robotics or rugged environmental factors like extreme heat or low light?
It may take mechanical engineering, robotics and programming experts to figure out exactly how your vision inspection system will fit into existing production environments.

START TO NARROW DOWN CAMERAS FOR VISION INSPECTION SYSTEMS

There are a lot of vision systems on the market that are suitable for a variety of inspection applications. Trying to narrow them down can seem like a daunting task, but to start, you can ask yourself a simple question: do we need a single sensor camera system (smart camera) or multiple sensor camera system (multi-camera vision system)?
For production lines with fewer inspection points, where inspection data does not need heavy processing, smart cameras may be a wise choice, as they’re self-contained and easily programmed to perform a specific task.
On the other hand, production lines with dozens of inspection points, especially when centralized data on inspections could be useful, a multi-camera vision system would be most beneficial.
There are many other considerations to take into account, but starting with the question of single or multi sensor camera systems is a good start for narrowing down which type of vision system would be best for you.
There's a lot to understand about your application and the pros and cons of various vision inspection systems before implementation. Taking into account the tips above, you can increase your chances of successful implementation and see the full benefits of vision inspection systems for years to come.



TO KNOW MORE ABOUT MACHINE VISION SYSTEM IN MUMBAI INDIA CONTACT MENZEL VISION AND ROBOTICS PVT LTD AT (+ 91) 22 67993158 OR EMAIL US AT INFO@MVRPL.COM


Thursday, 28 September 2017

MACHINE VISION KEEPS AN EYE ON FACIAL RECOGNITION

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While privacy concerns have been a factor for years, it turns out that if you put a useful application in front of the machine vision algorithm —i.e., you make it fun — everyone’s happy. For example, a Russian music festival used a facial recognition algorithm to supply attendees with photos of themselves from the event, while a firm in Singapore is developing a transport ticketing system that uses voluntary facial recognition to charge commuters as they pass through fare gates.
It helps that consumers have face detection technology in the palm of their hands. Mobile applications such as FaceLock scan a user’s face in order to unlock apps on their smartphone or tablet. Furthermore, a recent patent filed by Apple suggests that the next generation iPhone will have “enhanced face detection using depth information.” Users also are relying on facial recognition for critical tasks such as mobile banking and commerce.
The projected growth of facial recognition and other biometrics usage reflects these trends. Facial recognition market size is estimated to rise from $3.3 billion in 2016 to $6.84 billion in 2021. Analysts attribute the growth to an expanding surveillance market, increasing government deployment, and other applications in identity management.
The machine vision industry is starting to find ways to capitalize on the growth opportunities in facial recognition, whether it’s a camera calibrated to work in low light or a mobile app that helps police officers catch suspects. But the technology needs to overcome a few hiccups first.
To Redact and Serve
Suspect Technologies, a startup in Cambridge, Massachusetts, has developed advanced facial recognition algorithms, but for two very different purposes within law enforcement. One use addresses the privacy considerations around body cameras worn by police officers. The most frequently cited goal of body worn video (BWV) is to improve law enforcement accountability and transparency. When someone files a Freedom of Information Act request to acquire one of these videos, law enforcement agencies must promptly comply.
But they can’t do that without first blurring the identities of victims, minors, and innocent bystanders, which typically has been a slow, tedious process restricted to video specialists. Suspect Technologies’ automated video redaction (AVR) software, available on cameras manufactured by VIEVU, is optimized for the real-world conditions of BWV — most notably high movement and low lighting. The technology, which can track multiple objects simultaneously, features a simple interface that allows users to add or adjust redacted objects. AVR reduces the time it takes to redact video footage by tenfold over existing methods.
Unlike AVR which covers up identities, Suspect Technologies is rolling out a mobile facial recognition app to identify suspects. “As it stands now, there’s no simple way for law enforcement to tell if someone is a wanted criminal,” says Jacob Sniff, CEO and CTO of Suspect Technologies.
Compatible with iPhone and Android devices, the company’s cloud-based watchlist recognition software has been tested on 10 million faces. The algorithm takes advantage of better facial recognition accuracy, which increases tenfold every four years. “Our goal is to be 100% accurate on the order of 10,000 identities,” Sniff says.
Suspect Technologies will start by customizing the product for regional law enforcement agencies in midsized towns, which typically have about 100 wanted felons. The company also plans to introduce its software to schools and businesses for attendance-oriented applications. 
Machine Vision System | MVRPL
Cameras That Recognize
On the hardware side, the specifications of a facial recognition application are driving machine vision camera selection. “Monochrome cameras offer better sensitivity to light, so they are ideal in low-light conditions indoors and outdoors,” says Mike Fussell, product marketing manager of the integrated imaging division at FLIR SYSTEMS , Inc.(Wilsonville, Oregon). “If someone is strongly backlit or shadowed, cameras with the latest generation of high-performance CMOS sensors really shine in those difficult situations.”
For customers seeking better performance in low light, FLIR offers higher-end sensors that have high frame rates and global shutter. The entire pixel count reads out at the same time instantly, eliminating the distortion caused by the rolling shutter readout found on less expensive sensors, Fussell says. Rolling shutter cameras show distortion caused by the movement of the subject relative to the shutter movement, but they present a lower-cost alternative in low-light conditions.
Most cameras used in facial recognition are in the 3–5 MP range, according to Fussell. But in an application like a passport kiosk, where all of the variables are controlled, a lower-resolution camera is suitable. FLIR also offers stereo vision products that customers calibrate for optical tracking, which measures eye movement relative to the head. Some companies are taking the concept of facial recognition to the next level with gait analysis, the study of human motion. “In a building automation application, where you want to learn people’s habits, you could track their gait to turn lights on and off or have elevators waiting in advance for them,” Fussell says.
Facing Obstacles Head-on
For all its potential, facial recognition technology must address fundamental challenges before an algorithm reaches a camera or mobile device. According to one study, face recognition systems are 5–10 percent less accurate when trying to identify African Americans compared to white subjects. What’s more, female subjects were more difficult to recognize than males, and younger subjects were more difficult to identify than adults. 
As such, algorithm developers must focus more on the content and quality of the training data so that data sets are evenly distributed across demographics. Testing the face recognition system, a service currently offered by the National Institute of Standards and Technology (NIST), can improve accuracy. 
Once the algorithm reaches the camera, facial recognition’s accuracy is dependent upon the number and quality of photos in the comparison database. And even though most facial recognition technology Is automated, most systems require human examination to make the final match. Without specialized training, human reviewers make the wrong decision about a match half the time.
The machine vision industry, however, is no stranger to waiting for a technology to mature. Once facial recognition does that, camera makers and software vendors will be ready to supply the equipment and services for secure, accurate identity verification.
Suspect Technologies will start by customizing the product for regional law enforcement agencies in midsized towns, which typically have about 100 wanted felons. The company also plans to introduce its software to schools and businesses for attendance-oriented applications. 




TO KNOW MORE ABOUT MACHINE VISION SYSTEM, CONTACT MENZEL VISION AND ROBOTICS PVT LTD AT (+ 91) 22 67993158 OR EMAIL US AT INFO@MVRPL.COM




Wednesday, 12 July 2017

WHAT IS EMBEDDED VISION

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In recent years, a miniaturization trend has been established in many areas of electronics. For example, ICs have become more and more integrated and circuit boards in the electrical industry have become smaller and more powerful. This has also made PCs, mobile phones and cameras more and more compact and powerful. This trend can also be observed in the world of vision technology.
A classic machine vision system consists of an industrial camera and a PC: Both were significantly larger a few years ago. But within a short time, smaller and smaller-sized PCs became possible, and in the meantime, the industry saw the introduction of single-board computers, i.e. computers that can be found on a single board. At the same time, the camera electronics became more compact and the cameras successively smaller. On the way to even higher integration, small cameras without housings are now offered, which can be easily integrated into compact systems.

Due to these two developments, the reduction in size of the PC and the camera, it is now possible to design highly compact camera vision systems for new applications. Such systems are called embedded (vision) systems.

Design and use of an embedded vision system

An embedded vision system consists, for example, of a camera, a so-called board level camera, which is connected to a processing board. Processing boards take over the tasks of the PC from the classic machine vision setup. As processing boards are much cheaper than classic industrial PCs, vision systems can become smaller and also more cost-effective. The interfaces for embedded vision systems are primarily USB or Basler BCON for LVDS.

Basler Camera Distributor in India

Embedded vision systems are used in a wide range of applications and devices, such as in medical technology, in vehicles, in industry and in consumer electronics. Embedded systems enable new products to be created and thereby create innovative possibilities in several areas.

Which embedded systems are available?

As embedded systems, there are popular single-board computers (SBC), such as the Raspberry Pi®. The Raspberry Pi ® is a mini-computer with established interfaces and offers a similar range of features as a classic PC or laptop.

Embedded vision solutions can also be implemented with so-called system on modules (SoM) or computer on modules (CoM). These modules represent a computing unit. For the adaptation of the desired interfaces to the respective application, a so-called individual carrier board is needed. This is connected to the SoM via specific connectors and can be designed and manufactured relatively simply. The SoMs or CoMs (or the entire system) are cost-effective on the one hand since they are available off-the-shelf, while on the other hand they can also be individually customized through the carrier board.

For large manufactured quantities, individual processing boards are a good idea.

All modules, single-board computers, and SoMs, are based on a system on chip (SoC). This is a component on which the processor(s), controllers, memory modules, power management and other components are integrated on a single chip.

Due to these efficient components, the SoCs, embedded vision systems have only become available in such a small size and at a low cost as today.

Characteristics of embedded vision systems versus standard vision systems

Most of the above-mentioned single-board computers and SoMs do not include the x86 family processors common in standard PCs. Rather, the CPUs are often based on the ARM architecture. 

The open-source Linux operating system is widely used as an operating system in the world of ARM processors. For Linux, there is a large number of open-source application programs, as well as numerous freely-available program libraries.

Increasingly, however, x86-based single-board computers are also spreading.

A consistently important criterion for the computer is the space available for the embedded system.
For the SW developer, the program development for an embedded system is different than for a standard PC. As a rule, the target system does not provide a suitable user interface which can also be used for programming. The SW developer must connect to the embedded system via an appropriate interface if available (e.g. network interface) or develop the SW on the standard PC and then transfer it to the target system. 

When developing the SW, it should be noted that the HW concept of the embedded system is oriented to a specific application and thus differs significantly from the universally usable PC.

However, the boundary between embedded and desktop computer systems is sometimes difficult to define. Just think of the mobile phone, which on the one hand has many features of an embedded system (ARM-based, single-board construction), but on the other hand can cope with very different tasks and is therefore a universal computer.

What are the benefits of embedded vision systems?

In some cases, much depends on how the embedded vision system is designed. A single-board computer is often a good choice as this is a standard product. It is a small compact computer that is easy to use. This solution is also useful for developers who have had little to do with embedded vision. 

On the other hand, however, the single-board computer is a system which contains unused components and thus generally does not allow the leanest system configuration. This solution is suitable for small to medium quantities. The leanest setup is obtained through a customized system. Here, however, higher integration effort is a factor. This solution is therefore suitable for large unit numbers.

The benefits of embedded vision systems at a glance:
  • Lean system design
  • Light weight
  • Cost-effective, because there is no unnecessary hardware
  • Lower manufacturing costs
  • Lower energy consumption
  • Small footprint

Which interfaces are suitable for an embedded vision application?

Embedded vision is the technology of choice for many applications. Accordingly, the design requirements are widely diversified. Depending on the specification, Basler offers a variety of cameras with different sensors, resolutions and interfaces.

The two interface technologies that Basler offers for embedded vision systems in the portfolio are:
  • USB3 Vision for easy integration and
  • Basler BCON for LVDS for a lean system design
Both technologies work with the same Basler pylon SDK, making it easier to switch from one interface technology to the other.

USB3 Vision

USB 3.0 is the right interface for a simple plug and play camera connection and ideal for camera connections to single-board computers. The Basler pylon SDK gives you easy access to the camera within seconds (for example, images and settings), since USB 3.0 cameras are standard-compliant and GenICam compatible.

Benefits
  • Easy connection to single-board computers with USB 2.0 or USB 3.0 connection
  • Field-tested solutions with Raspberry Pi®, NVIDIA Jetson TK1 and many other systems
  • Profitable solutions for SoMs with associated base boards
  • Stable data transfer with a bandwidth of up to 350 MB/s

BCON for LVDS

BCON - Basler's proprietary LVDS-based interface allows a direct camera connection with processing boards and thus also to on-board logic modules such as FPGAs (field programmable gate arrays) or comparable components. This allows a lean system design to be achieved and you can benefit from a direct board-to-board connection and data transfer. 

The interface is therefore ideal for connecting to a SoM on a carrier / adapter board or with an individually-developed processor unit.

If your system is FPGA-based, you can fully use its advantages with the BCON interface.
BCON is designed with a 28-pin ZIF connector for flat flex cables. It contains the 5V power supply together with the LVDS lanes for image data transfer and image triggering. You can configure the camera vialanes that work with the I²C standard.

Basler's pylon SDK is tailored to work with the BCON for LVDS interface. Therefore, it is easy to change settings such as exposure control, gain, and image properties using your software code and pylons API. The image acquisition of the application must be implemented individually as it depends on the hardware used.

Benefits
  • Image processing directly on the camera. This results in the highest image quality, without compromising the very limited resources of the downstream processing board.
  • Direct connection via LVDS-based image data exchange to FPGA
  • With the pylon SDK the camera configuration is possible via standard I²C bus without further programming. The compatibility with the GenICam standard is given.
  • The image data software protocol is openly and comprehensively documented
  • Development kit with reference implementation available
  • Flexible flat flex cable and small connector for applications with maximum space limitations
  • Stable, reliable data transfer with a bandwidth of up to 252 MB/s

How can an embedded vision system be developed and how can the camera be integrated?

Although it is unusual for developers who have not had much to do with embedded vision to develop an embedded vision system, there are many possibilities for this. In particular, the switch from standard machine vision system to embedded vision system can be made easy. In addition to its embedded product portfolio, Basler offers many tools that simplify integration.

Find out how you can develop an embedded vision system and how easy it is to integrate a camera in our simpleshow video.

Machine learning in embedded vision applications

Embedded vision systems often have the task of classifying images captured by the camera: On a conveyor belt, for example, in round and square biscuits. In the past, software developers have spent a lot of time and energy developing intelligent algorithms that are designed to classify a biscuit based on its characteristics (features) in type A (round) or B (square). In this example, this may sound relatively simple, but the more complex the features of an object, the more difficult it becomes.

Algorithms of machine learning (e.g., Convolutional Neural Networks, CNNs), however, do not require any features as input. If the algorithm is presented with large numbers of images of round and square biscuits, together with the information which image represents which variety, the algorithm automatically learns how to distinguish the two types of biscuits. If the algorithm is shown a new, unknown image, it decides for one of the two varieties because of its "experience" of the images already seen. The algorithms are particularly fast on graphics processor units (GPUs) and FPGAs.




To Know More About Basler Camera Distributor in India, Contact Menzel Vision and Robotics Pvt Ltd at (+ 91) 22 67993158 or Email us at info@mvrpl.com


 

Contact Details



Address: 4, A-Wing, Bezzola Complex,
Sion Trombay Road, Chembur

400071 Mumbai, India
Tel:(+91) 22 67993158
Fax: (+91) 22 67993159
Mobile:+91 9323786005 / 9820143131
E-mail: info@mvrpl.com


 

Tuesday, 20 June 2017

New maintenance release 3.0.1 for MERLIC 3

http://mvrpl.com


The latest MERLIC maintenance release is now available for download on our website and provides minor bug fixes.

In particular, a bug has been fixed within the ADLINK NEON Digital I/O interface, that occurred in some cases when the tool 'Acquire Image from Camera' was used in the same MVApp as the interface.
MVTec Merlic


Furthermore, reading data codes with MERLIC has become more robust with this release.

We recommend all MERLIC 3 users to update their MERLIC installation with this release.

Please keep up-to-date with more features at WWW.MERLIC.COM.





TO KNOW MORE ABOUT MVTEC MACHINE VISION SOFTWARE DEALER MUMBAI, INDIA, CONTACT MENZEL VISION AND ROBOTICS PVT LTD AT (+ 91) 22 67993158 OR EMAIL US AT INFO@MVRPL.COM



Contact Details



Address: 4, A-Wing, Bezzola Complex,
Sion Trombay Road, Chembur

400071 Mumbai, India
Tel:(+91) 22 67993158
Fax: (+91) 22 67993159
Mobile:+91 9323786005 / 9820143131
E-mail: info@mvrpl.com

Thursday, 23 March 2017

IMPERX Welcomes Keith Wetzel as Director of New Product Development

IMPERX, Inc., a designer and manufacturer of industrial cameras and frame grabbers, has named industry veteran Keith Wetzel as Director of New Product Development. Wetzel will focus on strategic marketing initiatives and new product development aimed at driving continued customer satisfaction with winning imaging solutions.

"Keith brings over 28 years of results-oriented expertise in image sensor technology from TRUESENSE Imaging, Inc./Eastman Kodak Company," says Petko Dinev, President and CEO of IMPERX. "He will be a great addition to our senior management team and tremendous asset to our company's growth plan."

Wetzel joins IMPERX from TRUESENSE Imaging, Inc., formerly Eastman Kodak Company Image Sensor Solutions group, where he held various product and sales management roles during his 19-year tenure within the image sensor business and 28 year employment at Eastman Kodak Company. In his most recent assignment, he held the position of business development within the Americas region.

Wetzel received his bachelor's degree in electrical engineering and master's degree in business administration from Rochester Institute of Technology. He also holds several patents and contributed several image sensor articles to various photonics publications over his career.

About IMPERX  IMPERX designs and manufactures high performance cameras, frame grabbers and industrial imaging systems used in the machine vision, medical, military, surveillance and automation markets. Our multi-service brand is recognized for superior performance, reliability, accuracy, and cutting-edge design. Headquartered in Boca Raton, FL, with offices and distributors worldwide, IMPERX is registered to the ISO 9001:2008 Quality Management System Standard and the Environmental Management System Standard ISO 14001:2004. IMPERX is registered with DDTC.

Tuesday, 7 March 2017

Tailor-Made for You and Your Swing

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How CCR determines the best golf club using slow motion analysis

At first glimpse, swinging a golf club looks really easy. However, it requires a high level of precision, physical control and coordination and is among the most technically challenging motion sequences in sport. The ball lies just above the ground and has to be hit accurately with a club head measuring just ten by five centimetres. On impact, the golfer must hit the face of the club at a precise right angle and while doing so, take the ground conditions, distances, wind strength and gradients into account.

Reasons for Custom-Made Golf Clubs

Selecting the right club is crucial to the perfect golf swing. The club must be tailored to the individual measurements of the player as well as his or her unique requirements. If, for example, the club is too long, it becomes difficult to control. On the other hand, if it is too short, the player has to stoop, and achieving the correct shoulder turn from such a stance is challenging.

In golf businesses or specialist departments, clubs tend to come in standard versions. These naturally suit players who match such standard measurements. However, the majority of players don't fit the norm and require clubs that are custom-made. 

This is where Mr. Erhardt brings his fitting service into play. The first step is to measure his customers. The ideal club length can be determined by using body height, distance from hand to floor and other factors. Then an analysis is performed of body movements. To do so, Mr. Erhardt uses the MotionBLITZ EoSens® mini1 high-speed camera.

Slow-Motion Analysis

On average, a golf stroke is executed in just 0.8 - 1.5 seconds. The club head can reach a speed of up to 200 km/h when wielded by an experienced player. But such details of a swing can't be picked up with the naked eye. The MotionBLITZ EoSens® mini1 provides detailed slow-motion recordings, which Dietmar Erhardt analyses.

The recordings provide important findings relating to the best material to choose for the shaft. For example, the greater the club head speed in relation to timing, the more the shaft is inclined to bend out of shape. For players with a fast, powerful swing, a less flexible shaft is therefore recommended. This allows the player to have increased control over the stroke. For older players or beginners with a slower, weaker swing, a shaft made from a more supple material is advised. With a shaft made from graphite, the weight shifts to the club head and the total weight is reduced, enabling improved acceleration.

The cameras feature compact, robust housing (29 X 29×43 MM) which allows for easy installation - even in tight spaces. The compact form factor satisfies the requirements of transportation departments for discrete installation while providing efficient monitoring. The cameras are also shock-resistant so that camera-shake and blurred images can be avoided; GigE interfaces enable cable lengths of up to 100 meters.




To Know More About Mikrotron High Speed Camera Distributor in India, Contact Menzel Vision and Robotics Pvt Ltd at (+ 91) 22 67993158 or Email us at info@mvrpl.com


Contact Details



Address: 4, A-Wing, Bezzola Complex,
Sion Trombay Road, Chembur

400071 Mumbai, India
Tel:(+91) 22 67993158
Fax: (+91) 22 67993159
Mobile:+91 9323786005 / 9820143131
E-mail: info@mvrpl.com

Source - mikrotron.de