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distortions as determined by the shape and orientation of object surfaces on which the patterns are projected This is illustrated in Figure 117 (also see Figure 114) Note that the light pattern as seen by the camera contains discontinuities and changes in orientation and curvature The 3-D object coordinate corresponding to any point in the image plane may be calculated by computing the intersection of the camera's line of sight with the light plane To obtain the complete object description, either the light source is panned as shown in the figure or the object is moved on a conveyor belt to acquire multiple images Different surfaces in the object are detected by clustering stripes of light having similar spatial attributes In dynamically changing situations it may not be practical to project light stripes in sequence to acquire the complete image set covering the entire scene Note that if multiple stripes of light are simultaneously projected to acquire the entire depth map, there would be potential ambiguities in matching stripe segments resulting from object surfaces at different depths In such a case, patterns of multiple stripes of light in which each stripe is uniquely encoded are projected For example, using a binary encoding scheme, it is possible to acquire a complete set of data by projecting only log2N patterns where (N - 1) is the total number of stripes This method is illustrated in Figure 118 for N = 8 Each of the seven stripes has a unique binary code from (001) to (111)

[131]

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Since log28 is 3, only three images are acquired Each image is identified by the bit position 1, 2, or 3 of the 3-bit binary code A particular stripe of light is turned ON in an image only if its corresponding bit position is 1 For example, stripe 2 (010) is ON only in the second image, whereas stripe 7 (111) is ON in all three images The stripes in the set of three images are now uniquely identified and hence there would be no ambiguities in matching stripe segments In rapidly changing scenes, a single color-coded image is used instead of several binary-encoded images Structured lighting techniques have been used extensively in industrial vision applications in which it is possible to easily control the illumination of the scene In a typical application, objects on a conveyor belt pass through a plane of light creating a distortion in the image of the light stripe The profile of the object at the plane of the light beam is then calculated This process is repeated at regular intervals to recover the shape of the object The primary drawback of structured lighting systems is that it is not possible to obtain data for object points which are not visible to either the light source or the imaging camera

10

Joint reinforcement is typically fabricated in 10-foot lengths To accommodate wall intersections, joint reinforcement is commonly prefabricated in T-shapes and L-shapes, as shown in Figure 39 The wires in joint reinforcement can either be plain wire conforming to ASTM A 82 or stainless steel wire conforming to ASTM A 580, Type 304 ( Standard Specification for Stainless and HeatResisting Steel Wire ) The longitudinal wires in joint reinforcement must be deformed Deformations are made by knurling as defined in ASTM A 951 Therefore, the deformed wires in joint reinforcement do not conform to ASTM A 496 Deformed wires must have

4 (a)

3 (b)

3 (c)

(Panarese, Kosmatka, & Randall, Concrete Masonry Handbook, Portland Cement Association, 1991)

Figure 118: Illustration of binary-coded structured lighting where the sequence of projections determines the binary code for each stripe

A second method for range imaging is imaging radar In a time-of-flight pulsed radar, the distance to the object is computed by observing the time difference between the transmitted and received electromagnetic pulse The depth information can also be obtained by detecting the phase difference between the transmitted and received waves of an amplitude-modulated beam or the beat frequency in a coherently mixed transmitted and received signal in a frequency-modulated beam Several commercial laser beam imaging systems are built using these principles Range images are useful due to their explicit specification of depth values At one time it was believed that if depth information is explicitly available, later processing would be easy It became clear that though the depth information helps, the basic task of image interpretation retains all its difficulties

(Beall & Jaffe, Concrete and Masonry Databook, McGraw-Hill, 2003)

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