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Technical aspects
Computer Vision
Computer vision is a field of study that focuses on enabling computers to interpret and understand visual information from the world around them, including images, videos, and other forms of visual data. The goal of computer vision is to replicate the complex functions of human vision using algorithms and computer systems.

Computer vision algorithms typically involve techniques from artificial intelligence, machine learning, and signal processing, among other fields. These algorithms can be trained to recognize patterns, objects, and other visual features in images and videos, allowing computers to "see" and interpret visual data in ways that were previously impossible.

Some common applications of computer vision include facial recognition, object detection and tracking, image and video classification, and augmented reality. Computer vision technology is used in a wide range of industries, including healthcare, automotive, retail, and entertainment, among others.

As computer vision technology continues to improve, it is expected to play an increasingly important role in areas such as robotics, autonomous vehicles, and smart cities, among other fields.
Versatility
At StereoStitch, we can adapt to & work with all image/video capture incl. High Dynamic Range for great indoor capture. Visual input can be video frames, camera views, or multidimensional data. Therefore, a challenge is to manipulate digital input coming in diverse formats and shapes.
Quality
There are 2 main reasons why quality image processing is required. So that:
1) a human gets immersed in a digital twin as accurate in its visualisation/virtualization of reality as possible (e.g. complete with depth of field when 2D is not good enough and stereoscopy is a must). We seek to replicate or at times enhance human eyesight.
2) analytics (AI, Machine Learning, pattern recognition) running on visual processing output yield accurate results. Therefore, digital image/video processing is a critical step in the pre-analysis stages of computer vision.
As part of image processing, high-quality digital image/video stitching enables such accuracy thanks to seamless visual content.

Devices & Displays
Possible image/video capture devices
  • Drones
  • Robots
  • Manned/unmanned vehicles
  • Portable cameras in mobile devices
  • CCTV cameras
Possible displays
  • CAVE (Computer Assisted Virtual Environment) virtual display device
  • HMD (Head Mounted Display)
  • Spatial Computers (Apple Vision Pro, Microsoft Hololens, Meta Quest Pro, HTC Vive, etc...)

Frequently Asked Questions
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Contact us via the form at the bottom of the page
Q – How can my firm partner with StereoStitch?
A – If your firm is interested in partnering with StereoStitch, contact us through the submission form below.
Q – What is stitching?
A – Stitching is the mathematical process of joining
together multiple visual inputs.
Q – Where is StereoStitch 360 useful?
A – StereoStitch 360 improves task efficiency by providing users with smooth, interactive 360° content.
Q – Do you offer custom projects?
A – Yes, all StereoStitch products can be tailored to your specific
needs.
Q – How can StereoStitch Linear improve shelf management?
A – StereoStitch Linear enables the collection of live data, giving
AI the opportunity to analyse shelving stock.
Q – How can StereoStitch Stereo improve our robotics products?
A – StereoStitch Stereo increases the accuracy of visually–reliant robotics by replicating human eyesight through
stitching
Glossary
What is Stitching?
Digital image stitching (2D, 3D, linear, panoramic, spherical) is the process of combining multiple digital images into a single, seamless panoramic or wide–angle image. The process involves aligning and blending multiple images that have overlapping areas, to create a single, high–resolution image that provides a wider and more comprehensive view of a scene or location.
The process of image stitching typically involves several steps. First, the images are pre–processed to correct for any distortions or variations in lighting or color. Then, the images are aligned and matched, based on features such as edges or corners, to ensure that they are properly aligned. Next, the overlapping areas of the images are blended together, using techniques such as feathering or gradient blending, to create a seamless transition between the images. Finally, the stitched image is post–processed to correct any remaining distortions or color variations, and to enhance the overall image quality.
Digital image stitching is used in a wide range of applications, including photography, cinematography, remote sensing, and medical imaging. It allows for the creation of high–resolution images that provide a more comprehensive and detailed view of a scene or location, which can be useful for applications such as surveillance, site planning, and virtual reality.
What are the success factors?
The success factors for high–quality digital image stitching include:
High–quality source images: To achieve a high–quality stitched image, the source images must be of high resolution, sharpness, and clarity. This ensures that the final image is detailed and free from artifacts or blurring.
Accurate alignment: The source images must be accurately aligned to ensure that they match up seamlessly, without visible seams or gaps. This requires precise matching of features such as edges or corners, and careful consideration of any distortions or variations in the source images.
Proper blending: The overlapping areas of the source images must be properly blended to create a seamless transition between images. This requires careful consideration of lighting and color variations, and the use of techniques such as feathering or gradient blending to create a smooth transition.
Proper image processing: The stitched image must be properly processed to correct for any remaining distortions or color variations. This includes correcting for lens distortion and chromatic aberration, as well as adjusting color and contrast to create a consistent and appealing final image.
High–quality software: The software used for image stitching must be of high quality, with advanced algorithms for accurate alignment and blending, as well as options for manual control and adjustment. This allows for greater control over the final output and can lead to higher quality results.
Overall, the success of high–quality digital image stitching depends on a combination of factors, including the quality of the source images, the accuracy of alignment and blending, and the quality of the software used for the process. With careful attention to these factors, it is possible to achieve high–quality stitched images that provide a more comprehensive and detailed view of a scene or location.
What are the limitations of off–the–shelf digital image stitching software?
Off–the–shelf digital image stitching software can be a useful tool for creating panoramic images, but there are several limitations to consider:
Limited control: Off–the–shelf software may not provide the level of control necessary for some specialized applications, such as creating high–resolution or HDR panoramas.
Limited customization: The available stitching algorithms and tools may not be sufficient for some specialized applications, and customization options may be limited.
Limited compatibility: Off–the–shelf software may not be compatible with all types of images or camera systems, limiting its usefulness for some applications.
Limited accuracy: Some off–the–shelf software may not be able to achieve the same level of accuracy as specialized software, resulting in visible seams or other artifacts in the final image.
Limited speed: Image stitching can be a computationally intensive process, and some off–the–shelf software may not be optimized for speed, resulting in slow processing times.
Limited customer support: Support and troubleshooting for off–the–shelf software may be limited or nonexistent, making it difficult to resolve issues or receive assistance.
Overall, off–the–shelf digital image stitching software can be a useful tool for basic stitching needs, but for more specialized applications, such as creating high–resolution or HDR panoramas, specialized software may be necessary. It's important to carefully evaluate the limitations of off–the–shelf software before making a purchase, and to consider specialized software or custom solutions for more complex applications.
What role StereoStitch can play for you?
At StereoStitch, we live and breath digital stitching, combining the use of both applied mathematics and software programming skills. Image stitching is the process of combining multiple images with overlapping fields of view into a single, seamless image. This involves identifying the common features in the images, such as edges or corners, and aligning them to create a panoramic or wide-angle view.
Thus, we can overcome the limitations mentioned above.
The mathematical concepts involved in image stitching include linear algebra, calculus, and geometry. For example, homography, a transformation that maps points in one image to their corresponding points in another image, is a key concept in image stitching. Other concepts, such as feature extraction, feature matching, and image warping, also require a good understanding of mathematical concepts.
In addition to mathematics, software programming skills are also required for image stitching. Image stitching algorithms must be implemented using programming languages such as Python, C++, or MATLAB. The algorithms must be optimized for performance, particularly when processing large images or multiple images.
Furthermore, image stitching also involves image processing techniques, such as image filtering, image blending, and color correction. These techniques require knowledge of image processing algorithms and programming skills to implement them in software.
Overall, image stitching is a complex and challenging task that requires a combination of applied mathematics and software programming skills. This is MetaPeak’s #1 area of subject-matter expertise.
What is Inter Pupillary Distance (IPD)
Interpupillary distance (IPD) is the distance between the centers of the pupils of the two eyes. IPD is an essential parameter in head–mounted displays (HMDs) since it affects the visual comfort, stereoacuity, and sense of immersion for the wearer. The following are some of the issues related to interpupillary distance for head–mounted displays:
Lack of Adjustment: HMDs with fixed interpupillary distance settings may not fit all users, resulting in visual discomfort and reduced stereoacuity. This can cause eyestrain, headaches, and fatigue, which can limit the amount of time a user can wear the device.
Incorrect Settings: Incorrect IPD settings can also cause visual discomfort and reduced stereoacuity. Users may not be aware of the importance of IPD and may not set it correctly, resulting in a suboptimal viewing experience.
Inaccurate Measurements: Inaccurate IPD measurements can cause issues with HMDs. If the measured IPD is incorrect, the HMD may not fit the user correctly, leading to visual discomfort and reduced stereoacuity.
Eye Strain: If the IPD is not set correctly, the user's eyes may not be able to converge correctly, causing eye strain and headaches.
Reduced Sense of Immersion: If the IPD is not set correctly, the sense of immersion may be reduced. This can limit the overall effectiveness of the HMD, particularly in applications such as virtual reality, where immersion is critical.
To address these issues, HMD manufacturers can offer adjustable IPD settings, which allow users to adjust the IPD to their individual needs. Additionally, HMDs can use eye–tracking technology to adjust the display in real–time, based on the user's eye movements, ensuring that the IPD is always set correctly. Overall, addressing issues related to interpupillary distance is critical for ensuring the comfort and effectiveness of HMDs.
What role StereoStitch can play for you?
We can provide you automatic IPD adjustment for your HMD to avoid operator nausea.

What are immersive technologies?
Immersive technologies refer to digital technologies that aim to fully immerse the user in a virtual or simulated environment. These technologies include virtual reality (VR), augmented reality (AR), and mixed reality (MR).
Virtual reality involves using a headset or other devices to create a fully immersive, computer-generated environment that completely replaces the user's physical surroundings. Augmented reality, on the other hand, overlays digital information onto the user's real-world environment, typically using a smartphone or other mobile device.
Mixed reality combines elements of both virtual and augmented reality to create an interactive, immersive environment that responds to the user's movements and actions in real-time.
Immersive technologies are used in a wide range of fields, from entertainment and gaming to education, healthcare, and training. They offer unique opportunities for creating engaging and interactive experiences, as well as for enhancing learning, visualization, and problem-solving.
What role StereoStitch can play for you?
We can provide you with the highest quality visualization of real-world environment for your augmented reality applications.
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