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We are provided a project with java code. Measurement is perhaps one of the most fundamental concepts in science. Without the ability to measure, it would be difficult for scientists to conduct experiments or form theories. Not only is measurement important in science and the chemical industry, but it is also essential in farming, engineering, construction, manufacturing, commerce, and numerous other occupations and activities. The technology for automatic measurement of overall dimensions of a generic object using a commercially available mobile phone. The user only must go around the measured object and scan it by the mobile phone’s camera. The measurement uses computer vision algorithms for scene reconstruction to obtain the object’s point cloud. “Pixel per Metric” algorithm for processing the point cloud and for estimating the dimensions of the object. It focuses on collecting and filtering points where the biggest challenge is to recognize and separate points belonging to the measured object from the rest.

Keywords: Canny edge algorithm, pixel per metric, Object measurement, Pre-processing OpenCV, Computer Vision.


Real time object measurement are very vital tasks in our day-to-day life style. Measuring is about finding the dimensions of a specific object. Measuring is done with help of basic tools like tapes, ruler or a protractor. when these tools are not in handy we get real issue. Our proposed system can deal with the current problem by providing a handy app in our mobile phones. There is a high chance that everyone has a mobile phone in their hands rather than having a tapes/ruler. To calculate the size of each object, firstly we need to determine the reference object. After that, the dimensions of the reference objects will be used to calculate the size of other objects. Our phone captures the image using the phone camera which is inbuilt in our mobile phone. The image is stored in a local storage and we consider the reference object and deduce the measurements of the object which is taken from the mobile. These measurements are displayed to the user.


Canny Edge Detection Algorithm:

The algorithm runs in 5 separate steps:

4. Noise Reduction: Blurring of the image to remove noise.

5. Gradient Calculation: The edges should be marked where the gradients of the image has large


6. Non-maximum suppression : Only local maxima should be marked as edges.

7. Double thresholding: Potential edges are determined by thresholding.

8. Edge tracking by hysteresis: Final edges are determined by suppressing all edges that are not

connected to a very certain (strong) edge.

b) Pixel per metric:

To determine the size of an object in an image, we first need to perform a “calibration” using a

reference object. Our reference object should have two important properties:

Property #1: We should know the dimensions of this object (in terms of width or height) in a

measurable unit (such as millimeters, inches, etc.).

Property #2: We should be able to easily find this reference object in an image, either based on the

placement of the object (such as the reference object always being placed in the top-left corner of

an image) or via appearances (like being a distinctive color or shape, unique and different from all

other objects in the image). In either case, our reference should be uniquely identifiable in some

manner. This algorithm is used to determine the size of the object with respect to the size of the

reference object.

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