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1 Computational Vision CSCI 363, Fall 2012 Lecture 5 The Retina.

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Presentation on theme: "1 Computational Vision CSCI 363, Fall 2012 Lecture 5 The Retina."— Presentation transcript:

1 1 Computational Vision CSCI 363, Fall 2012 Lecture 5 The Retina

2 2 The Eye

3 3 The Optic Disk Axons (neural fibers) of the retinal ganglion cells leave the eye by way of the Optic Disk. There are no photoreceptors in this region, so there is a gap in the visual field, known as the blind spot. The brain fills in the space in the blind spot, so we are unaware of the gap. Demonstration. This "filling in" of gaps by the brain also takes place for people who have gaps in vision due to strokes, etc.

4 4 Major Cell Types in Retina Major cell types: Photoreceptors Bipolar Cells Horizontal Cells Amacrine Cells Retinal Ganglion Cells

5 5 Photoreceptors Photoreceptors sense light through phototransduction. They are located at the back of the eye. Two major types: Rods: Rod shaped, very sensitive to light, low acuity, slow temporal response. Cones: Cone shaped, lower light sensitivity, high acuity. Three types of pigments that are sensitive to three wavelengths of light (red, green, blue) for color vision.

6 6 The Fovea The fovea is a specialized region in the center of the eye. It measures about 0.5 mm in diameter Within this region, cones are closely packed together. This region gives us the greatest acuity, allowing reading and other fine visual judgments. There are no rods in the fovea. Outside the fovea there is a mixture of rods and cones. The ratio of rods to cones increases toward the periphery. Can see fainter light with your peripheral vision If fovea bleached (by looking at a bright light), you cannot read until it recovers.

7 7 Schematic of the Fovea

8 8 Other retinal cells Bipolar cells: Provide straight pathway from photoreceptors to retinal ganglion cells. Horizontal Cells: Present in the layer between the photoreceptors and the bipolar cells. Have long horizontal connections. Amacrine Cells: Present in the layer between the bipolar cells and the retinal ganglion cells. Also have horizontal connections. There are lots of different shapes. Retinal Ganglion Cells: Final cells in the retinal pathway. The first cells in the pathway to fire action potentials. They send signals to the LGN by way of their axons, which form the optic nerve.

9 9 Response of Retinal Ganglion Cells Retinal Ganglion cells and bipolar cells have receptive fields that exhibit a center-surround structure. Question: What is the center-surround structure good for?

10 10 Why Find Edges? Want to find meaningful objects or surfaces Usually the border of an object is defined by a change in light intensity. Changes in intensity can also signal a change in depth or orientation of the surface. Must first find the intensity changes. Then must find what led to the intensity change.

11 11 Human Psychophysics Humans can detect sharp intensity changes: Craik-Obrien-Cornsweet illusion #1 Intensity Distance Demos: http://viperlib.york.ac.uk/http://viperlib.york.ac.uk/ Humans are not good at detecting gradual intensity changes.

12 12 How do we find sharp edges? Edges have a variety of contrasts. The image has features at a variety of sizes (spatial scales).

13 13 Stages for Edge Detection Detecting Edges: Smoothing--Eliminates noise. Determines spatial scale. Differentiation--Localizes the intensity change Feature Extraction: Determine the feature that caused the intensity change.

14 14 Smoothing

15 15 Localization of an edge Changes in intensity are not instantaneous, particularly in a smoothed image. Humans can localize edges to within a few seconds of arc (a few mm for a line a distance of 1 meter from the observer). Accurate localization is necessary for stereo vision. Differentiation allows us to find the location of the most rapid intensity change. The first derivative gives a peak at the location of the most rapid change. The second derivative gives a zero at this location. Marr and Hildreth suggested using these zero crossings to indicate edges.

16 16 Intensity Derivative

17 17 Zero Crossings and Edges Image Image after smoothing and second derivative Black = Negative White = Positive Zero Crossings


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