IoT Imaging Systems
As more and more “things” connect to the internet, the amount of connected devices and their capabilities seem endless. While connected devices like smart phones and laptops are a big part of our everyday lives, there are many other IoT devices being developed that can significantly improve the way that business functions are performed.
IoT devices can reduce costs, improve efficiency and increase the quality of customer service. The benefits of IoT are already being realized by several businesses across a wide range of industries.
Mint Controls’ IoT imaging system is just one example of how the IoT can help businesses become more efficient. This unique technology allows businesses to complete tasks and improve efficiency.
Detecting Defects in Equipment
From casinos to factories, equipment is an important part of running a successful business. Equipment that does not sit flush with the floor or that has other defects can reduce efficiency, increase maintenance and repair costs, and lose a lot of money.
Our unique system allows an image to be taken of a piece of equipment. The image is fully analyzed and any defects are reported back to the user. The equipment might sit a fraction of an inch off balance. While we can’t see this tiny imperfection, it can negatively affect the performance of the equipment. Our system makes it possible to locate these imperfections quickly and easily by simply taking a picture. This cutting edge technology is like nothing else available on the market today.
Being able to detect imperfections with equipment puts organizations in a much better position to conduct business. It provides them with real-time information to show suppliers. Our solution allows businesses to reduce costs on maintenance and repairs, ensure equipment is properly built and alert suppliers of a potential problem. This is only a small percentage of the capabilities of this type of system.
Understanding Digital Image Analysis
Humans are able to recognize objects in images with little effort. We can accomplish this task regardless of the object’s viewpoint, size and scale. Objects can even be recognized when they are partially obstructed from view. Although many attempts have been made over the years, computers still have difficulty completing this task with the same accuracy.
Digital Image Analysis takes place when a computer or electrical device automatically studies an image to obtain information from it. Image Analysis techniques are used for computer vision as well as medical imaging. Image Analysis uses patterns of recognition, digital geometry and signal processing to analyze images. 2D images are typically analyzed in computer vision while 3D images are analyzed for medical imaging purposes.
There are a number of different techniques used in automatic analysis of images. While each technique may be useful for a small range of tasks, none are able to compete with a human’s ability to analyze images.
Commonly used image analysis techniques include:
- 2D and 3D object recognition
- Image segmentation
- Motion detection
- Video tracking
- Optical flow
- Medical scan analysis
- 3D pose estimation
- Automatic number plate recognition
Example images of an object, known as templates or exemplars, are used for recognition. These objects vary in appearance based on lighting, direction of the object and changes to its size and shape. While a single exemplar is unlikely to succeed, it’s impossible to represent all possible appearances of an object.
Computer Image Analysis Techniques
There are several techniques that can help overcome the limitations of computer image analysis.
- Edge matching
Changes in lighting and color don’t typically have much effect on image edges. Edge matching uses edge detection techniques such as Canny Edge Detection to locate the edges of objects. - Divide and conquer search
This technique is guaranteed to find all matches that meet the criterion as long as the lower bound is accurate. - Greyscale matching
While edges are generally robust to illumination changes, they throw away a lot of information. The greyscale matching technique computes pixel distance as a function of pixel position and intensity. - Gradient matching
Comparing gradients is another way to be robust to illumination changes without throwing away too much information. - Histograms of receptive field responses
This technique finds relations between different image points implicitly coded in the receptive field responses. - Large model bases
Model bases are a collection of geometric models of objects that need to be recognized. This approach efficiently searches the database for a specific image and uses eigenvectors of the templates.
Object recognition methods are used in the following applications:
- Android Eyes
- Image Panoramas
- Image Watermarking
- Global Robot Localization
- Face Detection
- Optical Character Recognition
- Manufacturing Quality Control
- Content Based Image Indexing
- Object Counting and Monitoring
- Automated Vehicle Parking Systems
- Visual Positioning and Tracking
- Video Stabilization
- Pedestrian Detection
- Surveys
Face Detection and Recognition
Face detection is accomplished through iFace, a computer technology used to identify human faces in digital images. 3D face recognition uses three-dimensional geometry of the human face. This method has proven to be far more effective than 2D methods, rivaled only by fingerprint recognition technology.
IoT Solutions
Mint Controls is a build to suit company offering IoT solutions to solve many of the issues faced by businesses and organizations today. We provide full support of our solutions. Please contact us for more information about our IoT imaging systems.