6.5 Methods for Restoring Electrical Circuit …
585
Dimension recognition is a simpler procedure compared to recognition in
window. The criterion for coincidence of topology elements with a library
element is the overall size of the element.
2. By group handling method. The user shall set points (vector elements of the
editor) in the upper left corner of the element contour for the group of recognized seats in advance. When executing the command, the user sets a rectangular
contour limiting the field of recognition. The program detects all points within
the set limits and performs a single command of recognition based on the left
corner for each one of them (Fig. 6.58).
Arrangement of anchor points at each topology element across the entire working
field of the project and setting the corresponding parameters allow the program
to independently analyze the type of the topology element and its orientation and
set them across the entire working field of the project. During element recognition, the program searches all elements of the topology; during comparison, each
element is rotated (four positions total) and reflected (two positions total), until the
best comparison result is identified. The recognition process completes when all the
entered anchor points have been processed, or the given number of steps have been
passed.
In order to eliminate the effect of incorrect selection of the anchor point position,
the program includes the cycle of library element shift relative to the entered anchor
point by the set value along two axes (recognition area). Scanning of points in the
recognition area is performed helically (Fig. 6.59).
Successful recognition is often implemented in the subset of the recognition
points. If the process is stopped upon detection of the very first successful point
in the recognition area, a certain deviation of the found element position from the
optimal one is observed. To solve this problem, it is necessary to identify the entire
subset of successful points and select the optimal point according to the accepted
criterion (Fig. 6.60).
For example, it is possible to assume that the optimal position is characterized
by minimum deviation of the average value of actual brightness in all topological
rectangles of the same vector color from the value set for this vector color. Two
versions of optimum selection are implemented in the program; these versions differ
in the method of calculating the minimum deviation.
In order to save time, processing of the point from the recognition area stops if
the percentage of unrecognized rectangles exceeds the set value. If the percentage
of unrecognized rectangles is smaller, the data on such points are saved. If there
are no successful points, the program selects the optimal point from the ones saved
according to the accepted criterion similar to the successful recognition scenario.
An algorithm that artificially shifts the unrecognized rectangles along two axes
by the set number of pixels is implemented in the program and used in case of a large
quantity of recognized rectangles. If all the rectangles are recognized successfully,
this type is also considered recognized successfully in the optimal point. This algorithm can be useful in case of significant geometric distortions of the bitmap image
585
Dimension recognition is a simpler procedure compared to recognition in
window. The criterion for coincidence of topology elements with a library
element is the overall size of the element.
2. By group handling method. The user shall set points (vector elements of the
editor) in the upper left corner of the element contour for the group of recognized seats in advance. When executing the command, the user sets a rectangular
contour limiting the field of recognition. The program detects all points within
the set limits and performs a single command of recognition based on the left
corner for each one of them (Fig. 6.58).
Arrangement of anchor points at each topology element across the entire working
field of the project and setting the corresponding parameters allow the program
to independently analyze the type of the topology element and its orientation and
set them across the entire working field of the project. During element recognition, the program searches all elements of the topology; during comparison, each
element is rotated (four positions total) and reflected (two positions total), until the
best comparison result is identified. The recognition process completes when all the
entered anchor points have been processed, or the given number of steps have been
passed.
In order to eliminate the effect of incorrect selection of the anchor point position,
the program includes the cycle of library element shift relative to the entered anchor
point by the set value along two axes (recognition area). Scanning of points in the
recognition area is performed helically (Fig. 6.59).
Successful recognition is often implemented in the subset of the recognition
points. If the process is stopped upon detection of the very first successful point
in the recognition area, a certain deviation of the found element position from the
optimal one is observed. To solve this problem, it is necessary to identify the entire
subset of successful points and select the optimal point according to the accepted
criterion (Fig. 6.60).
For example, it is possible to assume that the optimal position is characterized
by minimum deviation of the average value of actual brightness in all topological
rectangles of the same vector color from the value set for this vector color. Two
versions of optimum selection are implemented in the program; these versions differ
in the method of calculating the minimum deviation.
In order to save time, processing of the point from the recognition area stops if
the percentage of unrecognized rectangles exceeds the set value. If the percentage
of unrecognized rectangles is smaller, the data on such points are saved. If there
are no successful points, the program selects the optimal point from the ones saved
according to the accepted criterion similar to the successful recognition scenario.
An algorithm that artificially shifts the unrecognized rectangles along two axes
by the set number of pixels is implemented in the program and used in case of a large
quantity of recognized rectangles. If all the rectangles are recognized successfully,
this type is also considered recognized successfully in the optimal point. This algorithm can be useful in case of significant geometric distortions of the bitmap image