ICBV Course Final Project Sergey Tyrin Itamar Barkai

The main goal of this research is to develop an image analysis system capable of locating grapes in a 2-dimensional image with no additional information

1. Shape inference using circular Hough transform 2. Hue and Green colour separation 3. Local minima search and Clustering

Circular Hough transform parameters: The range of radii to search for. The perfectness threshold of a circle.

Playing with perfectness threshold : Low thresholdHigh threshold

Combining thresholds : Threshold combination

Playing with radii threshold : Too high range of radiiMany false detections

All of the vine grape berries have a yellow-greenish hue, which is easily separated by the human vision system from the reddish hue of the trunks and from the almost white colour of the grass in the background.

The leaves have commonly a very similar range of green hue, and a human observer wouldn’t possibly know to separate the grapes from the leaves if he hadn’t had an a-priori knowledge of the shape. Green hue layer of the RGBHue layer of the HSV

Look for more information in other colour spaces separation...

Solution for this was found in the yellow hue layer of the CMYK representation. T he sun-highlighted centres of the berries stand out.

I. Local minima extraction Finding areas of local minima, cutting of single point minima.

II. Removal of noise detections by spot size Filtering out where spot size > % probability of being part of the background.

III. Clustering Looking for condensed groups of spots.

III. Clustering Looking for condensed groups of spots.

IV. Removal of edge lines Making segments more oval by removing one-pixel-width lines.

V. Filtering of a cluster as one entity Using RGB, Magenta, and Black colour spaces to calculate median values of clusters as total and comparing these values to each segment median values. Removing segments where median max value and min value are too far away. Removing segments where magenta is too high or black is too low (trunk or background). Applying this filter to the cluster as whole.

VI. Calculation of a hull Joining segments into real clusters by finding convex hull – inserts non-grape parts into clusters.

VI. Calculation of a hull Solution – calculating Delaunay triangulation and cutting off too long edges.

VII. Generating final mask Removal of non-grape triangles, hull filling and final rounding of the segmentation mask.

Due to the highly saturated and unrestricted lighting conditions, it is impossible to detect each and every grape in the image. For normalized lighting conditions, a satisfying result can be achieved. For the different lighting conditions, using two different sets of threshold parameters, increases the successful detection rate significantly.

Is grapes detection solved?

Bright/dark areas – apply noise filtering thresholds to smaller regions. Smarter cluster filtering – comprehensive filtering decisions. Learning process – clusters and grapes shape, lighting conditions.