Build a Reproducible Image-Processing Pipeline
Let us turn these operations into a small inspection project.
Build a Reproducible Image-Processing Pipeline explained visually
Let us turn these operations into a small inspection project. English captions are optional and start off. The complete transcript follows the written lesson.
What you will learn
Let us turn these operations into a small inspection project.
Start with a measurable goal
Let's build a small project around counting dark washers on a bright table. First define the outcome: how many separate washers are visible, and how accurate must their boundaries be? Set aside some photographs with shadows, reflections, and touching washers for final evaluation.
Build the smallest useful pipeline
Check each input's dimensions and color interpretation. On a copy, try grayscale conversion, mild denoising, and a threshold. If tiny artifacts remain, test limited morphology; then label connected regions. At each stage, ask what observed problem that operation solves. Ordinary component counting may merge touching washers, so flag that limitation rather than silently reporting a wrong total.
Validate the whole process
Record the original image, calibration if we need physical sizes, parameters, code version, and intermediate masks. A reviewer should be able to trace one reported count back through the pipeline. Compare with a simple unprocessed baseline and inspect both easy and difficult parts.
A worked example
Suppose a photograph truly contains five washers but the mask shows six groups because of one shadow fragment. A size filter might make this example right; check that it doesn't erase a genuinely small washer elsewhere. Next, we'll explore the camera measurements behind every one of these images.
Worked example
Suppose manual review finds five washers. Your mask produces six components because one small shadow fragment remains. A size filter removes the fragment and gives five, but test that rule on a genuinely small washer before accepting it. Matching one image is not sufficient validation.
Quick check: test your understanding
Try it yourself: Create a short experiment record containing the goal, input IDs, operations, parameter values, observed errors, and one condition where the pipeline fails.
Check the reasoning
A useful record lets another person reproduce the output and understand the failure. Include unchanged originals and reviewed reference counts, rather than only successful screenshots.
When should another processing step be added?
When a specific observed problem justifies it and evaluation shows that it helps without unacceptable side effects.
How would you solve the practice case?
A useful record lets another person reproduce the output and understand the failure. Include unchanged originals and reviewed reference counts, rather than only successful screenshots.
What does the worked example show?
Suppose manual review finds five washers. Your mask produces six components because one small shadow fragment remains. A size filter removes the fragment and gives five, but test that rule on a genuinely small washer before accepting it. Matching one image is not sufficient validation.
Further reading
Digital Image Processing, 4th edition — Rafael C. Gonzalez and Richard E. Woods.
Original ToolsInVision teaching examples. Editorial approach · Suggest a correction
Accessible referenceLesson 16 transcript191 words · Video transcript · Matches the published narration and captionsOpen transcript
Let's build a small project around counting dark washers on a bright table. First define the outcome: how many separate washers are visible, and how accurate must their boundaries be? Set aside some photographs with shadows, reflections, and touching washers for final evaluation.
Check each input's dimensions and color interpretation. On a copy, try grayscale conversion, mild denoising, and a threshold. If tiny artifacts remain, test limited morphology; then label connected regions. At each stage, ask what observed problem that operation solves. Ordinary component counting may merge touching washers, so flag that limitation rather than silently reporting a wrong total.
Record the original image, calibration if we need physical sizes, parameters, code version, and intermediate masks. A reviewer should be able to trace one reported count back through the pipeline. Compare with a simple unprocessed baseline and inspect both easy and difficult parts.
Suppose a photograph truly contains five washers but the mask shows six groups because of one shadow fragment. A size filter might make this example right; check that it doesn't erase a genuinely small washer elsewhere. Next, we'll explore the camera measurements behind every one of these images.