BOBCAT HD-SDI
Hardware User’s Manual
Imperx, Inc.
Rev. 1.4
6421 Congress Ave.
6/2/2014
Boca Raton, FL 33487
+1 (561) 989-0006
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3.1.
OVERVIEW
The camera has built-in several basic image processing functions. More functions will
be added later. Please contact Imperx for more information.
3.2.
IMAGE ENHANCEMENT
In many imaging applications the user will have a dark object on a bright background,
many dark and bright spots or shadows, or the light will not be sufficient, so the
resulting image will have a low contrast, and/or a very low dynamic range. To
improve the image quality in such conditions, BOBCAT offers a set of image
enhancing features – thresholding and multi-point image correction. The processing
function is applied to the entire image unless PAOI6 is enabled as “Processing ROI”.
In this case the processing function will apply only to the selected ROI.
3.2.1.
Threshold Operation
In many applications the binary images are much simpler to analyze that the
original gray scale one. The process, which converts the regular gray scale
image to binary, is called “Thresholding”. Thresholding is a special case of
intensity quantization (binarisation) where the image can be segmented into
foreground and background regions, having only two gray scale levels “white”
and “black”. Selecting the threshold value is very critical for the binary image
quality, and it is to a great extend scene dependent. If a threshold level is
chosen correctly, this will produce a well-defined boundary of the object,
which is essential. In some cases it is desirable if part of the image is binary
and some is grayscale image. The camera has built in four thresholding modes:
3.2.1.1.
Single Threshold Binary
If the image is a high contrast scene and has well defined bright and
dark regions a simple binarisation technique can be used for
thresholding – Formula 3.1. The binary image output is converted to
“white” for all gray level values higher or equal to the selected
threshold point X1, and to “black” for all gray levels lower than X1.
The user can set the optimal threshold value. Figure 3.1 shows the
original and the processed image with single threshold.
Output signal =>
“WHITE” if (input signal
≥
X1)
“BLACK” if (input signal < X1)
(3.1)
