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processed image size reduced. In this example a windowed area approximately 
one-quarter of the entire 380x480 pixel image is processed with the convolu- 
tion kernel indicated. This technique is especially germane if commercially 
available processors are unable to match tomorrow's 1000x1000, 50MHz solid 
state camera outputs. If the unprocessed surrounding border is 
objectionable in the stereo view, it may be reduced in intensity or blanked. 
The alternative, frame rate reduction, may be an acceptable alternative in 
some stereo measurement situations. The following crude experiment was 
conducted to determine a relationship between updated video frame and object 
(aerial photograph) movement. 
Table 2. Object vs Frame Grab Rate 
  
Object Frame Grab 
Rate mm/s Rate (frames/sec) 
2 30(V), 25 (X) 
1 25(W), 20(X) 
0.5 16(7), 12(X) 
0.2 12(V), 8(2), 4(X) 
Y = objectively acceptable X = objectively unacceptable 
Experiments with a stereo field must be conducted before more decisive 
conclusions can be reached. Fig. 3b shows two simultaneous image operations 
displayed together, (the processing associated with each field may also be 
interchanged). Window size and position is adjustable. 
Parallel processing is illustrated in Figs. 4 and 5. Histograms are 
shown for selected portions of an image, Fig. 4a. (In these results the 
histogram result is biased to show only the significant peaks.) The histo- 
gram in Fig. 4b is for the entire image, rather than the windowed portions 
as in Fig. 4a. The processing kernel shown has concentrated much of the 
photometric data into a narrow peak. Yet a different processing of Fig. ^a 
appears in Fig. 4c with an even more pronounced concentration of illumina- 
tion. It is important to note that the histogram function can be used to 
measure, then modify the image based upon that measurement. Since computa- 
tion of image equalization or specification, Hall, 1979, is usually 
performed in a host computer, the author proposes using the measured distri- 
bution peaks to determine which one of n previously stored look-up tables to 
address to approximate the desired transformation in a real-time environment 
in order to circumvent delay caused by including the host in the loop. Two 
dimensional autocorrelation and histogramming are illustrated in Fig. 5. 
The double cusps in the left hand windows represent autocorrelation of the 
windowed regions along the major x, y axes of the window, Real, 1984b. The 
histograms in the right hand windows are for the entire image and are biased 
to show only significant peaks. Fig. 5a is the "original" with a rather 
broad distribution of illumination. Fig. 5b is processed with the kernel 
indicated for shading compression and edge enhancement. The autocorrela- 
tions and illumination distribution are thereby compressed. Fig. 5c is the 
result of image subtraction with a diagonal displacement of two pixels. 
Fig. 5d is processed with the kernel shown for edge detection, which proves 
to be more effective than edge detection by image displacement, Fig. 5c. 
Note the narrowed autocorrelations and histogram. In these experiments, the 
image processing, correlation and histogramming are parallel operations. 
The windowed results are normally viewed integral with the displayed image 
and are clearly visible in contrasting color in a way they cannot be seen in 
a black and white reproduction. 
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