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      "body": "Embedded vision is quickly gaining more prominence in Artificial Intelligence (AI), the Internet of Things (IoT), and other emerging technology based applications. Hence, more companies are looking to cost-efficiently integrate imaging capabilities into their products. For many such products and applications, Mobile Industry Processor Interface (MIPI) is one of the most popular and convenient ways of interfacing cameras with the host processor.\n\nIn this blog, we attempt to learn more about the MIPI interface and how MIPI cameras work.\n\nBefore we even get into the advantages of the MIPI interface and how MIPI cameras work, let us talk a bit of history on how the interface evolved over time.\n\nEvolution of the MIPI interface\nCSI-1\nCSI-1 was the original standard MIPI interface architecture that defined the interface between a camera and a host processor.\n\nCSI-2\nReleased in 2005, the first version of MIPI CSI-2 came with a protocol divided into layers, such as:\n\nPhysical Layer\nLane Merger Layer\nLow-Level Protocol Layer\nPixel to Byte Conversion Layer\nApplication Layer\nLater in 2017, the second version of MIPI CSI-2 was released along with support for RAW-16 and RAW-20 color depths. In addition, it could increase virtual channels from 4 to 32, and reduce Latency Reduction and Transport Efficiency (LRTE).\n\nThe third version of MIPI CSI-2 was released in 2019 and came with support for RAW-24 color depth.\n\nCSI-3\nMIPI CSI-3 was first released in 2012 followed by the next version in 2014. It provided a high-speed and bidirectional protocol for image and video transmission between cameras and hosts.\nAmong the three types, MIPI CSI-2 is the most commonly used interface in mobile and remote applications like autonomous driving, drones, smart city, medical imaging, computer vision, etc.\n\nMore about MIPI CSI-2\nIn the previous section, we looked at how the MIPI interface evolved through the years. Now, let us try to understand the MIPI CSI-2 interface a bit more in detail.\n\nCommonly used in embedded vision systems, MIPI CSI-2 is a camera interface that connects an image sensor with an embedded board to control and process the image data. This helps the sensor and embedded board to act together as a camera system to capture images. The below image represents an embedded camera board connected to an image sensor using a MIPI CSI-2 interface.\n\nRead more: https://www.e-consystems.com/blog/camera/technology-thursday/what-is-a-mipi-camera-how-does-mipi-camera-work/",
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      "body": "Hello welcome to Steemit world! \n I'm @steem.history, who is steem witness. \n This is a recommended post for you.[Newcomers Guide](https://steemitdev.com/guide/@steemitblog/steemit-a-guide-for-newcomers) and [The Complete Steemit Etiquette Guide (Revision 2.0)](https://steemit.com/steem/@steem.history/the-complete-steemit-etiquette-guide-revision-20-homage-1598425779) and, recommended community [Newcomers Community](https://steemit.com/trending/hive-172186) \n I wish you luck to your steemit activities.<center> \n \n \n https://cdn.steemitimages.com/DQmXHwdcNs5VPcBft1iSosPdHLpBNBfjuG84g3ffWhMw5JQ/image.png \n <sub>(The bots avatar has been created using https://robohash.org/)</sub> \n @steem.history \n \n ### My witness activity \n - [My aspiration for STEEM witness](https://steemit.com/hive-185836/@steem.history/my-aspiration-for-steem-witness-1601280729) \n - Provides information on Steem.  \n [Reference](https://steemit.com/trending/hive-130095) \n - Supporting the Steem project. \n [SPUD4STEEM project](https://steemit.com/trending/spud4steem) \n - Supporting the community. \n [Newcomers Community](https://steemit.com/trending/hive-172186),[Steem Sri Lanka](https://steemit.com/trending/hive-133716) ,[WORLD OF XPILAR](https://steemit.com/trending/hive-185836), [GLOBAL STEEM](https://steemit.com/trending/hive-145160), [Scouts](https://steemit.com/trending/hive-181136), [Latino Community](https://steemit.com/trending/hive-188619) \n \n ### My featured posts \n - [The Complete Steemit Etiquette Guide (Revision 2.0) -Homage](https://steemit.com/steem/@steem.history/the-complete-steemit-etiquette-guide-revision-20-homage-1598425779) \n \n [![image.png](https://cdn.steemitimages.com/DQmd7of2TpLGqvckkrReWahnkxMWH6eMg5upXesfsujDCnW/image.png)](https://steemlogin.com/sign/account-witness-vote?witness=steem.history&amp;approve=1) \n <sub>please click it!</sub> \n \n ![image.png](https://cdn.steemitimages.com/DQmWDnFh7Kcgj2gdPc5RgG9Cezc4Bapq8sQQJvrkxR8rx5z/image.png) \n <sub>(Go to https://steemit.com/~witnesses and type fbslo at the bottom of the page)</sub> \n \n </center>",
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      "permlink": "how-time-of-flight-tof-compares-with-other-3d-depth-mapping-technologies",
      "title": "How Time-of-Flight (ToF) compares with other 3D depth mapping technologies",
      "body": "At e-con Systems, we have been working on 3D camera technologies for more than a decade – starting with developing our first passive stereo camera. Since then, we have been exploring different ways of improving the generation of 3D data. After all, 3D visualization has been invaluable in helping several industries break new frontiers in innovation. Being merely equipped with 2D camera data is restrictive for applications that require robust and reliable data for making real-time decisions.\n\nWhile we received great feedback for our STEEREOCAM camera solution, we also started to dig deep and explore a new 3D depth mapping technology called Time-of-Flight (ToF). Even though ToF has been around for a while (ever since the introduction of the lock-in CCD technique in the 1990s), it has only recently started to mature. As camera features start to evolve, ToF technology is becoming more viable for non-mobile markets like industrial, retail, etc.\n\nWhat is a time of flight camera?\nTime-of-Flight (ToF) cameras use infrared light to extract depth information– making it easy to evaluate distances in a full scene with just one laser pulse. Hence, they have seen wide adoption in industries where applications rely on real-time depth and visual information.\n\nTo learn more about time of flight cameras and their key components, visit the article What is a time of flight sensor? What are the key components of a time of flight camera?\n\nPopular use cases of time of flight cameras\nTime-of-Flight (ToF) cameras play a vital role in the industrial sector. Modern industrial AGVs (Automated Guided Vehicles) and AMRs (Autonomous Mobile Robots) depend on their ability to draw insights about their surroundings to avoid collisions. This is possible by capturing accurate depth data. They also amplify volume dimensioning capabilities in warehouses – especially to meet the demands of the e-commerce sector, where speed and accuracy are competitive differentiators. ToF cameras provide 3D data to help pinpoint the dimensions of products – saving a lot of time and effort.\n\nFurthermore, ToF cameras have also witnessed tremendous growth in the biometrics sector, considering the push for face recognition-based authentication protocols to address spoofing and other security concerns.\n\nStereo Vision and 3D Mapping: How it works\nBefore we compare Time-of-Flight with other 3D mapping technologies, let’s take a deeper look at one of its challengers – Stereo Vision.\n\nAs you may already know, human binocular vision is based on the depth being perceived by using stereo disparity (difference in image location of an object seen by the left and right eye). Then, the brain uses this binocular disparity to extract depth information from the 2D retinal images (known as stereopsis).\n\nSimilarly, stereo vision cameras like Tara and TaraXL try to mimic this technique of human vision to perceive depth by using a geometric approach called triangulation. Some of the properties considered are:\n\nBaseline: It is the distance between the two cameras (about 50–75 mm – interpupillary distance).\n\nResolution: It is directly proportional to the depth. The higher the number of pixels to search, the higher the number of disparity levels (but with a higher computational load).\n\nFocal length: It is directly proportional to the depth. The lower the focal length, the farther we see – but with a reduced Field of View. The higher the focal length, the more near depth we see with a high Field of View.\n\nAfter capturing two 2D images from different positions, stereo vision cameras enable correlation to create a depth image. So, stereo vision cameras are suitable for outdoor applications with a large field of view. However, it is necessary for both images to have sufficient details and texture or non-uniformity. You can also add those details by illuminating the scene with structured lighting to achieve better quality.\n\nWhat about Structured Light imaging?\nStructured light involves using a light source (laser/LED) to provide a narrow light pattern onto the surface and detect distortions of illuminated patterns as a 3D image is geometrically reconstructed by the camera. Then, using triangulation, it scans several images and assesses the object’s dimensions, even if they are complex. Basically, this approach ensures that cameras can capture moving scenes from various perspectives before quickly building a 3D reconstruction.\n\nEffective vision solutions can also capture multiple images of structured light simultaneously. Some sectors that harness this approach include biometrics, entertainment, manufacturing, healthcare, security, etc.\n\nHow ToF stands when compared with other 3D mapping technologies?\nEvery embedded vision technology available for 3D image mapping has its own pros and cons. Let’s see how Time-of-Flight (ToF) cameras fare in comparison to the other stereo and 3D technologies – stereo vision and structured light.\n\nFollowing is a pictorial representation of how the 3 stereo vision technologies work:\n\n\nFigure 1: A quick glance at the big three 3D mapping technologies\nStereo Vision vs. Structured Light vs. Time-of-Flight (ToF)\nThe following table gives a detailed comparison of the three 3D mapping technologies by parameters such as cost, accuracy, depth range, low light performance etc.\n\n \tSTEREO VISION\tSTRUCTURED LIGHT\tTIME-OF-FLIGHT\nPrinciple\tCompares disparities of stereo images from two 2D sensors\tDetects distortions of illuminated patterns by 3D surface\tMeasures the transit time of reflected light from the target object\nSoftware Complexity\tHigh\tMedium\tLow\nMaterial Cost\tLow\tHigh\tMedium\nDepth(“z”) Accuracy\tcm\tum~cm\tmm~cm\nDepth Range\tLimited\tScalable\tScalable\nLow light\tWeak\tGood\tGood\nOutdoor\tGood\tWeak\tFair\nResponse Time\tMedium\tSlow\tFast\nCompactness\tLow\tHigh\tLow\nPower Consumption\tLow\tMedium\tScalable\nWhy Time-of-Flight (ToF) camera is a better choice for 3D mapping\nAs evidenced in the above comparison table, ToF cameras are ahead in the race to achieve excellence in 3D image performance. Some of their key differentiators are:\n\nReduced software complexity\n\nToF cameras provide the depth data directly from the module – thereby avoiding complications like running depth matching algorithms in the host platform\n\nHigher imaging accuracy\n\nToF cameras provide better output in terms of image quality since they rely on accurate laser lighting.\n\nMore depth scalability\n\nToF cameras have a scalable depth range based on the number of VCSELs used for illumination.\n\nBetter low light performance\n\nToF cameras perform better in low-light conditions due to their active and reliable light source.\n\nCompact-sized\n\nToF cameras boast of an impressive form factor with their compactness – attributed to the fact that the sensor and illumination can be placed together.\n\ne-con Systems & ToF cameras: What’s happening now?\nAs earlier mentioned, e-con Systems is making giant strides in maximizing the effectiveness of Time-of-Flight (ToF) camera solutions. We are well aware that designing a ToF-based depth-sensing camera is certainly no jog in the park. It can be a complex journey, given that it involves factors like optical calibration, temperature drifts, VCSEL pulse timing patterns, etc. Each one of these has the potential to affect depth accuracy.\n\nWe also know that it’s a time-consuming process – as anyone who wants to design a ToF system should be prepared for a very long design cycle. Having said that, it definitely helps that e-con Systems has over a decade’s worth of specialized experience in working with stereo vision-based 3D camera technologies! Over the years, we have helped customers across the world to successfully to integrate into live products.\n\nAnd today, we are proud to say that we have been working on a state-of-the-art ToF camera product for the past year.\n\nWe are excited to soon share more details with you in this Technology Thursday blog series as part of this journey.\n\nSee you next Thursday!\n\nThis blog post was originally published at e-con Systems’ website. It is reprinted here with the permission of e-con Systems.",
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