presentation

1. FRUIT DETECTION USING
MORPHOLOGICAL IMAGE
PROCESSING TECHNIQUE
Done By Ahmad Taweel
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2. 1. Outline
• Introduction
• Image Acquisition
• Image Preprocessing
• Color Feature
• Texture Feature
• Result
• Conclusion
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3. 2. Introduction
• Quality inspection
• Manual
• Not accurate
• Time consuming
• Expensive
• Fruit quality grading is mandatory condition
• New techniques in fruit quality assessment is necessary
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4. 2. Introduction
• Locating fruit from tree made
• Harvesting more efficient
• Analysis more easier
• Food appearance  Evaluation of quality of fresh food
• Packinghouses demanded a system
• Detect fruit skin defects
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5. 3.1 Image Acquisition
• Our first step here is to collect sample images of fruits
which are going to be decided the number of fruit present
on the tree.
• Store all the images in .Jpg format
• Resolution 429*322 pixels
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6. 3.1 Image Acquisition
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7. 3.2 Image Preprocessing
• The Aim of this process is to
• Improve image data
• Enhance some of its features
• Approaches:
• Image Enhancement
• Noise remove
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8. 3.2.1 Image Enhancement
• Improve visibility
• Remove any flickering
• Improve contrast
• Approaches:
• Spatial domain
• Frequency domain
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9. 3.2.2 Noise Remove
• To remove noise we use Masking
• Consist of:
• Filter mask
• Point to point
• Filter values are predefined
• Used:
• Classify
• Grade
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10. 3.2 Image Preprocessing
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11. 3.3 Feature Extraction
• Methods:
• Color Feature
• Color space conversion method
• Texture Feature
• Canny edge detection method
• Dilation method
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12. 3.4 Image Segmentation
• Method:
• Segmentation Partition clustering algorithm
• K partition each partition represent a cluster
• Drawback
• Poor result when a point is close to the center of another cluster
• Overlapping
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13. 3.4 Image Segmentation
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14. 3.5 Canny Edge Detection
• What is Edges?
• Significant transitions in pixels
• Canny Edge Detection ?
• Multi-stage algorithm
• Subject to noise in the image
• How does it work?
• Remove the noise
• Canny Edge Detection
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15. 3.5 Canny Edge Detection
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16. 3.6 Morphology (Dilation)
• Morphological Image Processing relay only on the relative
ordering of pixel values, not on their numerical values
• Dilation adds pixels to the boundaries of objects in an
image
• The number of pixels added depends on the size and
shape of the structuring element
• Applied:
• Gray scale images
• Binary image
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17. 3.6 Morphology (Dilation)
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18. RESULT (1/2)
• Counting objects is done
• Manually
• By costly electronic systems
• Image Processing using MATLAB
• Effective
• Quick
• Low cost
• No costly equipment
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19. RESULT (2/2)
• Accuracy depends on size of the disk structuring element
• Big objects  more counting accuracy
• Small objects  less counting accuracy
• Accuracy can be increased by separating conglutination
among the objects
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21. CONCLUSION (1/2)
• Quality inspection is done
• Manually
• highly inconsistence in accuracy
• Time consuming
• Boring
• Expensive
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22. CONCLUSION (2/2)
• Image Processing algorithms
• Automatically count
• Distinguish fruits
• Packinghouses can use this system to guarantee quality
of products.
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23. Thank You
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