However, using additional information (multiple data, motion vectors, prior), one can infer a more accurate object location with a sub-pixel resolution, i.e. With an accuracy finer than that of the pixel width, at a fraction of the pixel size, with non-integer coordinates. Chrome OS’s shelf is just like the macOS Dock. To add apps to the shelf, simply right-click on an icon and select Pin to shelf. You can do the same to remove an app (Unpin) or you can drag. PixelStyle Photo Editor for Mac is an all-in-one photo editing and graphic design software, providing professional high-quality photo processing tools to edit the photos, enhance and touch up photos on Mac OS X; PixelStyle Photo Editor comes with a huge range of high-end filters including lighting, blurs, distortions, tilt-shift, shadows, glows and many more. The plugin displays the pixel values of a square neighborhood around the current cursor position as a table. The position can be fixed by a keystroke; in this case the last cursor position is used further on and marked in the image. The arrow keys nudge this position if the Pixel Inspector window is in the foreground.
ActorPositionWS
ActorPositionWS outputs Vector3 (RGB) data representing the location of the object with this material on it in world-space.
In this example, you can see that ActorPositionWS is being fed directly into the Base Color of the material. As a result, each of the spheres with the material applied to them show a different color as they are moved to different locations in 3D space. Note that the result of the ActorPositionWS node is being divided by 1600 to create a nice blend-in color, rather than a pop.
CameraPositionWS
The CameraWorldPosition expression outputs a three-channel vector value representing the camera's position in world space.
The preview sphere changes color as the camera rotates.
LightmapUVs
The LightmapUVs expression outputs the lightmap UV texture coordinates in the form of a two-channel vector value. If lightmap UVs are unavailable, it will output a two-channel vector value of (0,0).
ObjectOrientation
The ObjectOrientation expression outputs the world-space up vector of the object. In other words, the object's local positive z-axis is pointing in this direction.
ObjectPositionWS
The ObjectPositionWS expression outputs the world-space center position of the object's bounds. For example, this is useful for creating spherical lighting for foliage.
ObjectRadius
The Object Radius outputs a value equal to the radius of a given object in Unreal units. Scaling is taken into account and the results can be unique for each individual object.
In this example, both meshes are receiving this material in which the ObjectRadius is fed into Diffuse. The ObjectRadius output is being divided by 512 to provide a more meaningful visual result.
Panner
The Panner expression outputs UV texture coordinates that can be used to create panning, or moving, textures.
Item | Description | |||||||
|---|---|---|---|---|---|---|---|---|
Item | Description | |
|---|---|---|
Item | Description | |
Preview Window Size: 740x700 | Preview Window Size: 740x280 |
In this example, ViewSize is being fed into Base Color. The result is divided by 2,400 to provide a more meaningful result.
WorldPosition
The WorldPosition expression outputs the position of the current pixel in world space. To visualize, simply plug the output into Emissive:
Common uses are to find the radial distance from the camera to a pixel (as opposed to the orthogonal distance from PixelDepth). WorldPosition is also useful to use as a texture coordinate and have unrelated meshes using the texture coord match up when they are near each other. Here is a basic example of using WorldPosition.xy to planar map a texture:
Dimensions/Edge Detection
Dimension measurement using edge detection is a recent trend of image sensor applications. In dimension inspection using image sensor, position, width, angle can be measured by capturing the object in two dimensions and detecting the edge. Here, the principle of edge detection is explained according to the processing process.
Understanding the principle makes it possible to set the detection to the optimum state. In addition, we introduce representative inspection examples using edges and explain how to select preprocessing filters for detection stabilization.
Principle of Edge Detection
An edge is a border that separates a bright area from a dark area within an image. To detect an edge this border of different shades must be processed. Edges can be obtained through the following four process steps.
(1)Perform projection processing
Projection processing scans the image vertically to obtain the average intensity of each projection line. The average intensity waveform of each line is called the projected waveform.
What is the projection processing?
Projection processing is used to obtain the average intensity and reduce false detection caused by noise within the measurement area.
(2)Perform Differential Processing
Larger deviation values are obtained when the difference in shades are more distinct.
What is the differential processing?
Differential processing eliminates the influence caused by changes in absolute intensity values within the measurement area.
(Example) The absolute intensity value is '0' if there are no changes in shade. If color changes from white (255) to black (0), the variation is -255.
(3)Maximum Deviation Value Always Needs to be 100%
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To stabilize the edge in actual production scenarios, internal compensation is performed so that the maximum deviation value is always maintained at 100%. Then, the edge position is determined from the peak point of the differential waveform where it exceeds the preset edge sensitivity (%). This method of edge normalization ensures that the edge's peak point is always detected, stabilizing image inspections that are prone to frequent changes in illumination.
(4)Perform Sub-Pixel Processing
Focus on the adjacent three pixels of the maximum differential waveform and perform interpolation calculations. Measure the edge position in units down to 1/100 of a pixel (sub-pixel processing).
Examples of inspection using edge detection
Edge detection includes many of the tools shown below. This section introduces some examples of frequently used tools.
Example 1. Inspections using the edge position
By setting an edge position window at several places, the X and Y coordinates of the target object are measured.
Example 2. Inspections using the edge width tool
By using the “outer diameter” feature of the edge width tool, the width of the metal plate and the diameter of the hole in the X and Y directions can be measured.
Example 3. Inspections using the circumference area of the profile position
By setting the measurement area as “circumference,” the angle (phase) of the notch is measured.
Example 4. Inspections using the profile width
Use the 'trend edge width' tool to scan the internal diameter and evaluate the degree of flatness.
PROFILE POSITION TOOL
The profile position tool combines a group of narrow edge windows to detect the edge position of each point. Since all of the data is collected within one inspection tool, it becomes easy to detect minute fluctuations by calculating minimum, maximum, and average values over the entire part.
Detection principle
By moving the narrow area segments in small pitches, the edge width and edge position of each point is detected.
- If highly accurate position detection is required,
- Reduce the segment size.
- If highly accurate position detection is required,
- Reduce the shift width of the segment.
- If highly accurate position detection is required,
- The direction towards which the segment is moved.
Pre-processing filter to further stabilize edge detection
In edge detection, it is very important to suppress the variations of edges. 'Median' and 'average' filters are effective at stabilizing edge detections. This section explains the characteristics of these pre-processing filters and effective selection method.
Original image
Averaging
Averaging filter with 3 × 3 pixels. This filter is effective in reducing the influence of noise components.
Median
Median filter with 3×3 pixels. This filter reduces the influence of noise components without blurring the image edge.
How to optimize the pre-processing filter
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Though “median” and “averaging” generally lead to the stabilization of edges, it is difficult to know which is effective for the target object. This section introduces a method of statistically evaluating the variations of measurements when these filters are used.
The CV-X series (CV2000 or later) is equipped with a statistical analysis function. This function records the measured data internally and performs statistical analysis simultaneously.
By repetitively measuring the static target with “no filter,” “median,” “averaging,” “median + averaging,” and “averaging + medial” the optimum filter can be selected.
Summary
Note the following four points to effectively utilize edge tools with an image sensor:
- By understanding the edge detection principle, proper adjustments can be made with ease.
- By understanding the capabilities of different edge tools the possibility of accurate inspection is significantly improved.
- By referencing typical detection examples, accurate detection can be implemented quickly.
- By selecting an optimum pre-processing filter, detection can be stabilized.
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