White Cabbage stump detection. (Dutch: Witte Kool stam detectie)

For automatically processing of white cabbage it is necessary to know the position of the stump. This is a rotting part of the cabbage so it has to cut off.
Witte Kool stam detection First step in this process is the detection of the stump, followed by rotating the cabbage in the required cutting position. A fast cutting process can cut of a small piece of the stump.
Next the cabbage is cut in small slices.
The color image on the left top is the acquired camera image, grey image (BW Image) on the right top is the intensity image.
The blue colored cabbage in the left bottom image is the result of the JAIMS developed algorithm. The white area is the detected stump. The red cross in the grey images shows the center of the detected stump.

On the right bottom the normalized intensity profile of the grey image (red line) and the results of the processed image (white line). The intensity line is taken at the position of the horizontal pixel line (see grey image).
The contrast of the stump in the processed image is much better than the contrast in the grey image. So a reliable detection after processing the image is possible. Actually No detection of the stump is possible using the grey image.

White Cabbage stump and stem detection. (Dutch: Witte Kool stam en stengel detectie)

Witte Kool stam and stem detection With this technique it is possible to detect the stem but also a certain color of the leaves as shown in the images. The color images (left top) and the grey images (right top) are visible.
On the left bottom the processed image for stump detection is shown and on the right bottom the processed image for the leave detection is shown.




White Cabbage rot spot detection. (Dutch: Witte Kool rotte plek detectie)

Witte Kool rot detection Very interesting of this technique is also the possibility of detecting the rot spots of the cabbage.
Here two settings are shown, on the left bottom the processed image, the very dark and very decayed spots and area are detected. On the right bottom the processed image of a light decayed area is shown.




Bacon and fat detection.(Dutch: speklap)

Bacon detection In these images the difference between fat and meat is shown. On the left bottom the processed image of fat and on the right bottom the processed image of the meat of the bacon is shown.
In this situation it is possible using basic vision algorithms to detect the amount of fat and or the amount of meat.



Pork steak.(Dutch: hamlap)

Pork steak detection This technique is also useful for detection of a small quality difference between meat.
In this case the meat quality detection is based on the color difference between a good and a lower quality piece of meat.
The lower quality is still within the quality limits. And of course with this very good contrast between both qualities it is possible to do the necessary measurements on the steak image.