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Ron Cohen Ramy Ben-Aroya Ben-Gurion University ICBV 2009 Final Project.

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Presentation on theme: "Ron Cohen Ramy Ben-Aroya Ben-Gurion University ICBV 2009 Final Project."— Presentation transcript:

1 Ron Cohen Ramy Ben-Aroya Ben-Gurion University ICBV 2009 Final Project

2  Grape Detection in Vineyards is a very complex problem. As far as we know its an unsolved problem till these days. There are some different ways to try deal with this problem.

3 We chose to hybrid two different techniques  The first one is a statistic filtering, which take measures from the picture’s gradient and compares measures we gathered from grapes images only.  The second way is partial circles detection, which tries to determine if a pixel is the center of a partial circle with fixed radius.

4  During the course we learned about the gradient and it’s power, which can tell us a lot about the edges in the pictures.  Our filter components are:  Measuring the average gray scale of all the pixels of the picture.  Filtering windows that their average gray scale is much greater from the whole image average. Statistic Filtering

5  Measuring the average gradient magnitude of dark and bright grape’s picture (only grapes from different pictures).  Mapping dark and bright areas in the image  Filtering windows that their average gradient magnitude is far from the measure we gathered.

6  As you can see in a bright picture below:

7  And in a darker picture below:

8 Here we can see the final rough filtering

9  We used Canny’s edge detector  We thought how to overcome the threshold values problem.  A pair of threshold can get reliable results when applied on a certain picture, while on another picture it could be a disaster.  We found that applying Canny’s ED on small parts of the image and then combining the result to one image can yield averagely better results.

10  Here you can see the different: Canny's results with no window partitioningCanny's results with window 100x100 partitioning

11  After getting edge elements we can scan the edge map for partial circles.  Our method is basically looking for pixels that are surrounded by edge pixels with a fixed distance (radius).

12  The pseudo-code of the pixel p checking is:  radiuses  vector of fixed radius  sum  vector at the size 1x length(radiuses)  sum(i)  sum of the pixels with distance radiuses(i) from p  avgs  vector at the size 1x length(radiuses)  avgs(i)  sum(i)/6/radiuses(i)  maxAvg, maxAvgIndex  max(avgs)  if maxAvg > fixedRatio* then mark p as a center with the corresponding radius : radiuses(maxAvgIndex ) *fixed ratio – the minimum part of a circle

13  We can see the results when looking for partial circle in the next image: 10 pixels Radius Found Centers 10 pixel radius quarter circle Found Centers

14  Here we can see the application of the method on the image

15  Now all we need to do is to look for centers only in the filtered areas.  After getting the centers we color around the center by their corresponding radius.

16 Original ImageDetected Grapes

17  Building a dictionary which hold a set of measures of different types of grapes, stems, leaves etch’.  Detecting branches as linear lines and trying to detect grapes on areas close to the lines  Detection by texture segmentation.  Adjust the measures to specific conditions like lightning and sun position and even grapes age, which can indicate grape radius

18  One method can never suit for every image. Therefore several methods must be combined  Circles identification is an efficient method for detecting grapes in vineyard.  Lighting conditions are critical for accurate identification and can be a major obstacle. Predicting these conditions like sun position and image brightness analysis can get better results.


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