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黄频免费高清视频We are Industrial Machine Vision Solution Dealer India. We deal in all type of machine vision solutions like simple video inspection , machine vision or detailed image analysis.

Saturday, 16 May 2020

FACE MASK INSPECTION MADE BETTER AND FASTER WITH BASLER ACE CAMERAS



CUSTOMER

  •  O-Net Industry
  •  Location Shenzhen, China
  •  Industry: Medical Supply Inspection
  •  Implementaion: 2020

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An acute shortage of face masks caused by fear of the spreading coronavirus pandemic has been straining global medical supplies since the start of 2020. The smart face mask inspection system designed by O-Net Industry boosts productivity and increases product conformity rate for the manufacturers. By making the inspection process faster and more effective, this solution can both ease the pressing market need and help face mask manu-facturers drive production cost down.

Headquartered in Shenzhen China, O-Net Industry is a leading company dedicated to machine vision automa-tion. The vision systems designed by O-Net Industry are used in various applications including visual inspections, geometry measurement and OCR among others; they are also able to provide customized solutions tailored to the products to be inspected.


In a traditional production line, a high scrap rate is inevi-table due to interference by environmental factors and the inconsistent working conditions of face mask making machines, resulting in lower efficiency and conformity rate. The application of a vision inspection in the produc-tion process, however, can significantly improve the situation.

All parts of a face mask need to be inspected, including the covering, the edges, the ear loops and the metal strip that lets the wearer bend the mask around the bridge of the nose (Figure 1). Quality control needs to identify and remove masks that are overlapping, broken, contami-nated, askew or in the wrong size.
Face mask inspection is also made more complex by factors including:
  •  Uneven illumination occurs during inspection due to the grainy surface of the non-woven fabric of face masks
  •  Face masks to be inspected are mmoving and their positions are random on the conveyor
  •  The edge, ear loop and metal strip are difficult to distinguish in inspection images

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With the help of customized lighting and the Basler ace 5 MP camera, the smart face mask inspection system deve-loped by O-Net can obtain excellent images of each mask. The system can then use the alignment algorithm to check whether the face mask meets standards.

In the inspection process, the system finds the center and corners of the covering part of face masks via the image acquired (Figure 2), to identify products that are miss-hapen. Exact measurement of face masks can also be done. With the center confirmed, the software defines the region of interest (ROI) as well as the baseline, to measure the specific size of a face mask and determine whether it meets standards.

Inspection of ear loops focuses on whether the length of loops and the positions of the fixation points meet the set standards. In the image analysis process, ear loops can be defined as curved lines. The software will detect and extract these curved lines and determine whether they are broken (Figure 6), and if not, calculate their length (Figure 7). The system can detect the fixing points of the ear loops in the image (Figure 8) and measure the dis-tances between the fixation points and their respective neighboring edges, to determine whether they meet standards

Non-woven fabric allows some light to get through, but extra layers can significantly increase its opacity. Thus the folded section of a face mask will appear much darker than the rest in the image. In Figure 4, the upper and bottom part of the face mask appear pale; O-Net’s soft-ware is configured to accept an image where the paler area is 374550 pixels in size. By contrast, the paler area drops to only 28894 pixels, which is almost ten times less, when two face masks overlap (Figure 5). By using such features, O-Net’s system determines whether the face masks are overlapping.

Lastly, the edges of a face mask also need inspection. The system needs to check whether the pitting on the edges is well aligned. Two green baselines are defined based on the outer margins of a face mask. Then the vertical dis-tance from each pitting line to the baselines is measured, so that the system can tell if the pitting on the edges is well aligned

Inspection of the length and position of the metal strip in a face mask is also required. By using the cutomized ligh-ting, the inspection image can show both the metal strip inside and the non-woven fabric wrapping it. Once the ends of the metal strip are found in the image, the length can be calculated (Figure 10). Meanwhile, two baselines are drawn to check that the position of the metal strip is centered

The vision inspection systems developed by O-Net can effectively automate the tedious quality check process and significantly improve product conformity rate. On average, each system can replace up to four skilled human inspectors. In factory applications, the visual inspection system usually runs uninterrupted for long periods; there-fore system stability is essential. O-Net decided on the Basler ace acA2440-20gm camera, due to the well-known stability of this key vision component. Mr. Wang, sales manager of O-Net, explains that “the stability of Basler cameras has helped save considerable mainte-nance costs. Our system development is quite smooth thanks to the Basler pylon Camera Software Suite, as it’s genuinely a developer-friendly software suite, and a short time-to-mark gives us competitive advantages. The vision market is booming in China and speed is vital. Our custo-mers are demanding ever-faster delivery, so the fast and reliable lead time ensured by Basler China is another attractive reason for us to work together.”

The smart software system offers high compatibility and can be customized, as O-Net develops everything from operator interface to architecture. This type of vision inspection software solution can apply to many applications.

TECHNOLOGIES USED

  •  Camera: Basler ace acA2440-20gm
  •  Lightinh: Customized BT series lighting
  •  Software: SV Smart Vision System by O-Net

TO KNOW MORE ABOUT BASLER ACE CAMERAS FOR FACE MASK INSPECTION INDIA CONTACT MENZEL VISION AND ROBOTICS PVT LTD CONTACT US AT (+ 91) 22 67993158 OR EMAIL US AT INFO@MVRPL.COM



Thursday, 14 May 2020

THERMAL IMAGING FOR DETECTING ELEVATED BODY TEMPERATURE


Can thermal cameras be used to detect a virus or an infection? The quick answer to this question is no, but thermal imaging cameras can be used to detect Elevated Body Temperature. FLIR thermal cameras have a long history of being used in public spaces—such as airports, train terminals, businesses, factories, and concerts—as an effective tool to measure skin surface temperature and identify individuals with Elevated Body Temperature (EBT).

In light of the global outbreak of the coronavirus (COVID-19), which is now officially a pandemic, society is deeply concerned about the spread of infection and seeking tools to help slow and ultimately stop the spread of the virus. Although no thermal cameras can detect or diagnose the coronavirus, FLIR cameras can be used as an adjunct to other body temperature screening tools for detecting elevated skin temperature in high-traffic public places through quick individual screening.



If the temperature of the skin in key areas (especially the corner of the eye and forehead) is above average temperature, then the individual may be selected for additional screening. Identifying individuals with EBT, who should then be further screened with virus-specific diagnostic tests, can help reduce or dramatically slow the spread of viruses and infections.

Using thermal cameras, officials can be more discrete, efficient, and effective in identifying individuals that need further screening with virus-specific tests. A variety of institutions, including transportation agencies, businesses, factories, and first responders are using thermal screening as an EBT detection method and as part of employee health and screening (EH&S).

Airports in particular are actively employing FLIR thermal cameras as part of their screening measures for passengers and flight crews. The screening procedures implemented at airports and in other public places are just the first step when it comes to detecting a possible infection: it’s a quick way to screen for anyone who might be sick, and must always be followed up with further screening before authorities decide to quarantine a person.

WHAT FLIR CAMERAS ARE USED FOR THERMAL SCREENING?

While governments outside the United States may choose from many different cameras, FLIR has a 510(k) filing (K033967) with the US Food and Drug Administration (FDA) for select camera models for use as an adjunct to other body temperature screening tools to detect differences in skin surface temperatures. These cameras include the FLIR Exx-Series, FLIR T-Series, FLIR A320, and Extech IR200.


TO KNOW MORE ABOUT FLIR THERMAL BODY TEMPERATURE SCREENING CAMERAS DEALER MUMBAI CONTACT MENZEL VISION AND ROBOTICS PVT LTD CONTACT US AT (+ 91) 22 67993158 OR EMAIL US AT INFO@MVRPL.COM



Friday, 10 April 2020

WHATS THE DIFFERENCE BETWEEN VISION SENSORS AND VISION SYSTEMS?




The difference between vision sensors and vision systems is fairly basic:

A vision sensor does simple inspections like answering a simple yes-no question on a production line. A vision system does something complex like helping a robot arm weld parts together in an automated factory.

Machine vision sensors capture light waves from a camera’s lens and work together with digital signal processors (DSPs) to translate light data into pixels that generate digital images. Software analyzes pixel patterns to reveal critical facts about the object being photographed.



Automated production doesn’t have to mean robots building pickup trucks and smartphones. Many automated factory tasks require simple, straightforward kinds of vision sensor data:

  1. Presence or absence. Is there a part within the sensor’s field of view? If the sensor answers yes, then machine vision software gives the OK to move the part to its correct place in the production process.
  2. Inspection. Is the part damaged or flawed? If the sensor sees defects, then the part gets routed out of production.
  3. Optical character recognition (OCR). Does the part contain specific words or text? Answering this question can help automated systems sort products by brand name or product description.
Cognex machine vision systems use multiple sensors to perform all of these basic tasks plus many more complicated challenges:
  1. Guides/alignment: When parts require an exact position or alignment, vision systems use sensors to identify the correct parts and place them exactly where they need to go.
  2. Code reading: Codes on packages and individual components contain vital data that vision systems acquire in real time to sort finished goods and differentiate between parts within a production process.
  3. Gauges/measurement: Sensors can ensure that machined parts are cut to the proper dimensions.
  4. 3D imaging: Sensors create three-dimensional representations of parts and products. These images can help automate inspections and tell robotic arms where to pick up and place parts.
Every company has to decide whether they need simple vision sensors or more advanced vision systems. Vision sensors are designed to be easy to install and implement, so factory personnel typically can set them up and configure them without a lot of outside assistance. When the imaging job requires a simple go/no-go decision, vision sensors may be all the company needs.

Vision systems, by contrast, require more expertise and a significant investment of time and money for configuration, installment and training. Often, companies turn to third-party integrators who have deep expertise in vision system installations.

Every company in the machine vision sector has its own way of defining the difference between machine vision sensors and systems. Cognex, for instance, builds vision sensors that perform specific kinds of tasks, like quality control in food processing. Our vision systems combine advanced software with industrial-strength cameras to enable a broad spectrum of factory automation applications.

One way to distinguish between vision systems and sensors is to imagine hundreds of beer bottles on a conveyor belt in a bottling plant. A vision sensor can make sure every bottle has a cap. If the cap is there, then the bottle gets approved and sent to packaging, where another sensor makes sure every six-pack has six bottles.

But the bottling company may want to identify when a bottle cap is skewed past a certain angle. Or, perhaps they want to ensure that the six-pack doesn’t accidentally mix multiple beer varieties. That’s more likely to require a vision system.



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



Monday, 23 March 2020

HOW MACHINE VISION AND DEEP LEARNING ENABLE FACTORY AUTOMATION




The pace of technology’s change over the last decade has been nearly unprecedented in human history and it’s only poised to become even more breathtaking in the years ahead: blockchain, robotics, edge computing, artificial intelligence (AI), big data, 3D printing, sensors, machine vision, internet of things, are just some of the massive technological shifts on the cusp for industries


Credits : pexels.com

Strategically planning for the adoption and leveraging of some or all these technologies will be crucial in the manufacturing industry. In the United States, manufacturing accounts for $2.17 trillion in annual economic activity, but by 2025 – just half a decade away – McKinsey forecasts that “smart factories” could generate as much as $3.7 trillion in value. In other words, the companies that can quickly turn their factories into intelligent automation hubs will be the ones that win long term from those investments.

“If you’re stuck to the old way and don’t have the capacity to digitalize manufacturing processes, your costs are probably going to rise, your products are going to be late to market, and your ability to provide distinctive value-add to customers will decline,” Stephen Ezell, an expert in global innovation policy at the Information Technology and Innovation Foundation, says in a report from Intel on the future of AI in manufacturing.

These technologies as applied in a factory or manufacturing setting are no longer nice to have, they are business critical. According to a recent research report from Forbes Insights, 93% of respondents from the automotive and manufacturing sectors classified AI as ‘highly important’ or ‘absolutely critical to success’. And yet, only 56% of these respondents plan to increase spending on artificial intelligence by less than 10%.

The disconnect between recognizing the importance of new technologies that allow for more factory automation and the willingness to spend on them will be the difference between those companies that win and those that lose. Perhaps this reticence to invest in something like AI could be attributed to the lack of understanding of its ROI, capabilities, or real-world use cases. Industry analyst Gartner, Inc. still slots many of AI’s applications into the “peak of inflated expectations” after all.

But AI, specifically deep learning or examples-based machine vision, combined with traditional rules-based machine vision can give a manufacturing factory and its teams superpowers. Take a process such as the complex assembly of a modern smartphone or other consumer electronic devices. The combination of rules-based machine vision and deep learning can help robotic assemblers identify the correct parts, identify differences like missing screws or misaligned casings, help detect if a part was present or missing or assembled in a different place on the product, and more quickly determine if those were problems. And they can do this at an unfathomable scale.

The combination of machine vision and deep learning are the on-ramp for companies to adopt smarter technologies that will give them the scale, precision, efficiency, and financial growth for the next generation. But understanding the nuanced differences between traditional machine vision and deep learning and how they complement each other, rather than replace, are essential to maximizing those investments.

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



Thursday, 13 February 2020

THERMAL IMAGING FOR SAFER AUTONOMOUS VEHICLES

For the automotive industry, pedestrian safety has been a serious concern since the horseless carriage. Londoner Arthur Edsall was the first driver to strike and kill a pedestrian in 1896 at a speed of four miles per hour. It took the U.S. Congress almost seventy years to impose automotive safety standards and mandate the installation of safety equipment and another thirty years before airbags became a required safety feature. Automotive safety standards in the United States are promulgated by a process of reviewing accidents after they have occurred.

Credits : pexels.com

In 2019, the National Transportation Safety Board (“NTSB”) finally addressed this standards - promulgation process in their Most Wanted List of transportation safety improvements calling for an increase in the implementation of collision-avoidance systems in all new highway vehicles. The progression of this change in policy derived from the 2015 study (SIR-15/01) that described the benefits of forward-collision-avoidance systems and their ability to prevent thousands of accidents.

After that report was published, an agreement was reached with the National Highway Traffic Safety Administration (“NHTSA”) and the Insurance Institute for Highway Safety that would require compliance with the Automatic Emergency Braking standard (“AEB”) on all manufactured vehicles by 2022. However, the agreement did not identify the specific technology that would enable AEB, and the question remains whether such technology is readily available and economically viable for industry-wide adoption.

RAPIDLY IMPROVING SENSOR TECHNOLOGY


The pace of technology over the last thirty years has been astronomical, yet technology to make driving safer has not kept pace. A computer that not too long ago was the size of a garage now fits into the palm of your hand. Today driving should be safer than ever, but the reality is that without the implantation of available modern technologies, the uncertainties of the road will always be with us. According to the NHTSA, there were 37,461 traffic fatalities in 2016 in the United States. 

In 2015, there were a total of 6,243,000 passenger car accidents. 1 Globally, there is a fatality every twenty-five seconds and an injury every 1.25 seconds. In the United States there is a fatality every thirteen minutes and an injury every thirteen seconds. These statistics are mind blowing. Compared to recent events affecting the aviation industry, two Boeing 737 MAX 8 airplanes crashed killing 346 people, the same number of people that die as a result of automobile accidents every 144 minutes, and all Boeing 737 MAX 8 airplanes were grounded 

The cost for automotive accidents is high. According to the national safety counsel, in the United States, the annual cost of health care resulting from cigarette smoking is approximately $300 billion whereas the annual cost of health care for injuries arising from automobile accidents is roughly $415 billion.

Technology to protect automobile occupants has reduced the number of driver and passenger fatalities. However, the number of people who die as a result of an accident outside the automobile continue to climb at an alarming rate. Pedestrians are at the greatest risk, especially after dark. 

The NHTSA reports that in 2018, 6,227 pedestrians were killed in United States traffic accidents, with seventy-eight percent of pedestrian deaths occurring at dusk, dawn, or night.2 In the United States, pedestrian fatalities have increased forty-one percent since 2008. Solutions to address pedestrian fatalities are needed to meet the standards by 2022.

TECHNOLOGY IN THE DRIVER’S SEAT


Ultimately, it is safer cars and safer drivers that make driving safer, and automotive designers need to deploy every possible technological tool to improve driver awareness and make cars more automatically responsive to impending risks. Today’s safest cars can be equipped with a multitude of cameras and sensors to make them hyper-sensitive to the world around them and intelligent enough to take safe evasive action as needed. Microprocessors can process images and identify subject matter 1,000,000 times faster than a human being

Advanced Driver Assist Systems (“ADAS”) are becoming the norm, spotting potential problems ahead of the automobile making auto travel safer for drivers, passengers, and pedestrians, not to mention the more than one million ‘reported’ animals struck by automobiles in the United States annually resulting in $4.2 billion in insurance claims each year. The advances we have seen so far are the first steps to evolving towards a future of truly autonomous vehicles that will revolutionize both personal and commercial transportation. 

Drivers need no longer rely on eyes alone to maintain situational awareness. Early generations of vision-assisting cameras were innovative, but they were not particularly intelligent and could do little to perceive the environment around the car and communicate information that could be used for driver decision-making.

Today, with tools such as radar, light detection and ranging (“LIDAR”), cameras, and ultrasound installed, a car knows much more about the environment than the driver does and can control the vehicle faster and safer than the human driver. Risky driving conditions such as rain, fog, snow, and glare, are less hazardous when a driver is assisted by additional onboard sensors and data processors.

One of the most advanced automotive sensors is a thermal sensor that allows a driver and the automobile to perceive the heat signature of anything ahead of the driver. Previously used mainly for military and commercial applications, early forms of night vision first came to the mainstream automotive market in the 2000 Cadillac DeVille, albeit as a cost-prohibitive accessory priced at almost at a cost approaching $3,000.

Since then, thermal cameras and sensors have become smaller, lighter, faster and cheaper. After years of exclusive availability in luxury models, thermal sensors are now ready to take their place among other automotive sensors to provide a first line of driving defense that reaches far beyond the reach of headlights in all vehicles, regardless of the cost of the vehicle


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



Friday, 31 January 2020

THREE TRENDS DRIVING INDUSTRIAL AUTOMATION


Since its inception in the 1980s, machine vision has concerned itself with two things:improving the technology’s power and capability and making it easier to use. Today, machine vision is turning to higher-resolution cameras with greater intelligence to empower new automated solutions both on and off the plant floor — all with a simplicity of operation approaching that of the smartphone, which significantly reduces engineering requirements and associated costs.

And, just like in other industries which are benefiting from rapid advancements in technology like big data, the cloud, artificial intelligence (AI), and mobile, so too will manufacturers, logistics operations, and other enterprises benefit from three key advances in machine vision for automation.


RAPIDLY IMPROVING SENSOR TECHNOLOGY


While 1-, 2-, and 5-megapixel (MP) cameras continue to make up the bulk of machine vision camera shipments, we’re seeing considerable interest in even higher-resolution smart cameras, up to 12 MP. High-resolution sensors mean that a single smart camera inspecting an automobile engine can do the work of several lower resolution smart cameras while maintaining high-accuracy inspections.

Cognex’s patent-pending High Dynamic Range Plus (HDR+) image processing technology provides even better image fidelity than your typical HDR. It will help smart cameras inspect multiple areas across large objects where lighting uniformity is less than ideal. In the past, lighting variations could be mistaken for defects or the feature was not even visible. Today, HDR+ helps reduce the effects of lighting variations, enabling applications in challenging environments that were beyond the capability of machine vision technology just a few years ago.

While advanced smart cameras run HDR+ technology on field-programmable gate arrays (FPGAs) to improve the quality of the acquired image at frame rate speeds, complementary sensor technology, such as time-of-flight (ToF) sensors, are being incorporated to enable “distance-based dynamic focus”. The new high-powered integrated torch (HPIT ) image formation system, using ToF distance measurement and high-speed liquid lens technology, are also making an impact by enabling dynamic autofocus at frame rate.

The newest barcode readers incorporate HPIT capability for applications such as high-speed tunnel sortation and warehouse management in situations where packages and product size can vary significantly, requiring the camera to quickly adapt to different focal ranges.

INTEGRATION WITH DEEP LEARNING


Just like AI’s impact in other industries, deep learning vision software for factory automation is allowing enterprises to automate inspections that were previously only able to do manually or more efficiently solve complex inspection challenges that are cumbersome or time-consuming to do with traditional rule-based machine vision.

The biggest use driving the investment in deep learning is the potential of re-allocating, in many cases, hundreds of human inspectors with deep learning-based inspection systems. For the first time, manufacturers have a technology that offers an inspection solution that can achieve comparable performance to that of a human.

One example of how deep learning will benefit organizations is in defect detection inspection. Every manufacturer wants to eliminate industrial defects as much as possible and as early as possible in the manufacturing process to reduce downstream impacts that cost time and money.

Defect detection is challenging because it is nearly impossible to account for the sheer amount of variation in what constitutes a defect or what anomalies might fall within the range of acceptable variation. As a result, many manufacturers utilize human inspectors at the end of the process to perform a final check for unacceptable product defects. With deep learning, quality engineers can train a machine vision system to learn what is an acceptable or unacceptable defect from a data set of reference pictures rather than program the vision system to account for the thousands of defect possibilities.

THE INTERNET OF THINGS


An important development for smart camera vision systems enabling Industry 4.0 initiatives is Open Platform Communications Unified Architecture (OPC UA). With contributions from all major machine vision trade associations around the world, OPC UA is an industrial interoperability standard developed to help machine-to-machine communication.

Combined with advanced sensor technology and trends such as deep learning, OPC UA will help transition machine vision technology from a point solution to bridge the industrial world inside the plant and the physical world outside it. Today, vision systems and barcode readers are key sources of data for modern enterprises.

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




WHAT ARE THE BENEFITS OF CMOS BASED MACHINE VISION CAMERAS VS CCD?




Industrial machine vision cameras historically have used CCD image sensors, but there is a transition in the industrial imaging marketplace to move to CMOS imagers. Why is this?.. Sony who is the primary supplier of image sensors announced in 2015 it will stop making CCD image sensors and is already past its last time buy. The market was nervous at first until we experienced the new CMOS image sensor designs. The latest Sony Pregius Image sensors provide increased performance with lower cost making it compelling to make changes to systems using older CCD image sensors.


WHAT IS THE DIFFERENCE BETWEEN CCD AND CMOS IMAGE SENSORS IN MACHINE VISION CAMERAS?


Both produce an image by taking light energy (photons) and convert them into an electrical charge, but the process is done very differently.
In CCD image sensors, each pixel collects light, but then is moved across the circuit via current through vertical and horizontal shift registers. The light level is then sampled in the read out circuitry. Essentially its a bucket brigade to move the pixel information around which takes time and power. In CMOS sensors, each pixel has the read out circuitry located at the photosensitive site. The analog to digital circuit samples the information very quickly and eliminates artifacts such as smear and blooming. The pixel architecture has also radically changed moving the photosensitive electronics to be more efficient in collecting light.


Courtesy of Automated Imaging Association

6 ADVANTAGES OF CMOS IMAGE SENSORS VS CCD


    There are many advantages of CMOS versus CCDs and outlined below: 

  • 1 – Higher Sensitivity due to the latest pixel architecture which is beneficial in lower light applications.

  • 2 – Lower dark noise will contribute to a higher fidelity image.

  • 3 – Pixel well depth (saturation capacity) is improved providing higher dynamic range.

  • 4 – Lower Power consumption. This becomes important as lower heat dissipation equals a cooler camera and less noise.

  • 5 – Lower cost! – 5 Megapixel cameras used to cost ~ $2500 and only achieve 15 fps and now cost ~ $450 with increased frame rates.

  • 6 – Smaller pixels reduce the sensor format decreasing the lens cost.

WHAT CMOS IMAGE SENSORS CROSS OVER FROM EXISTING CCD IMAGE SENSORS?

MVRPL can help in the transition starting with crossing over CCDs to CMOS using the following cross reference chart. Once identified, use the camera selector and select the sensor from the pull down menu.
CCD to CMOS cross reference chart

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