1.
Image recognition
To teach a computer to learn an object, we could let it taking images of sample objects and stored in database.
For example, taking photo of a hot dog with maximum and minimum sizes recognized for a hot dog. The max and min size would properly be scaled in sample images stored in database. Of course, operators must enter the real max/min size of that hot dog in database.
Assuming we take photo of an object (hot dog) at the same distance as when taking sample, the photo of an object (hot dog) with appropriate photo scale could be recognized as a hot dog or not. With max or min sizes’ photo in database, we could estimate the size of the captured hot hog. If an object is larger than a max hot dog, system would rule it out.
Speaking of pattern recognition, we could think the sample object as a transparent. When an object is captured in photo, we could apply the transparent of sample objects on top of the captured image. Operators must train the system to determine some acceptable variation of the captured image. If variation conditions of the sample image and captured image are met, that object is identified.
We could think of an object is composed of curves, straight lines, etc. We could zoom in or out of sample image on top of a captured object to compare its shapes in terms of curves, straight lines, etc. Some curves of the captured image could be varied a little bit as compared to the sample object. (Operators must train the system to determine some acceptable variation of curves and lines.)
If we take photo of an object at the same distance, we could estimate size of another object placed at the same distance.
If we know the size of an object, we could estimate the distance between the camera to the same object placed at different distance.
If an object image is captured at a known zoom, the real size of that object could be estimated as well as the distance between the object and the cameras, i.e. the size of a photo image must be a constant in width and length. For example, at a certain zoom, system would be able to tell the real size of the scene in a photo; so an object’s real size and its distance could be estimated.
2. Voice pattern recognition
When we talked, our voice was captured as waves in an image. If we used the same strategy as in image recognition, we could determine correct words, letters, or phrases.
To teach a computer to learn an object, we could let it taking images of sample objects and stored in database.
For example, taking photo of a hot dog with maximum and minimum sizes recognized for a hot dog. The max and min size would properly be scaled in sample images stored in database. Of course, operators must enter the real max/min size of that hot dog in database.
Assuming we take photo of an object (hot dog) at the same distance as when taking sample, the photo of an object (hot dog) with appropriate photo scale could be recognized as a hot dog or not. With max or min sizes’ photo in database, we could estimate the size of the captured hot hog. If an object is larger than a max hot dog, system would rule it out.
Speaking of pattern recognition, we could think the sample object as a transparent. When an object is captured in photo, we could apply the transparent of sample objects on top of the captured image. Operators must train the system to determine some acceptable variation of the captured image. If variation conditions of the sample image and captured image are met, that object is identified.
We could think of an object is composed of curves, straight lines, etc. We could zoom in or out of sample image on top of a captured object to compare its shapes in terms of curves, straight lines, etc. Some curves of the captured image could be varied a little bit as compared to the sample object. (Operators must train the system to determine some acceptable variation of curves and lines.)
If we take photo of an object at the same distance, we could estimate size of another object placed at the same distance.
If we know the size of an object, we could estimate the distance between the camera to the same object placed at different distance.
If an object image is captured at a known zoom, the real size of that object could be estimated as well as the distance between the object and the cameras, i.e. the size of a photo image must be a constant in width and length. For example, at a certain zoom, system would be able to tell the real size of the scene in a photo; so an object’s real size and its distance could be estimated.
2. Voice pattern recognition
When we talked, our voice was captured as waves in an image. If we used the same strategy as in image recognition, we could determine correct words, letters, or phrases.
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