Hubel and Wiesel (1962), in their study of the primary visual cortex in the cat, found that visual cortical cells re- spond in a more complex manner than ganglion cells and cells in the lateral geniculate nucleus. receptive fields all have an elongated shape, in contrast to the circular receptive fields of the on-off and off-on cells. They respond positively to light on one side of a line and neg- atively to light on the other side. They respond most if there is an edge of light lined up so as to fall at the boundary point. They respond positively to light in the center and negatively to light at the periphery, or vice versa. Thus, a bar with a positive center will respond most if there is a bar of light just covering its center. No single on-off or off-on cell is sufficient to elicit a response from a detector cell; instead, the detector cell responds to patterns of input from the on-off and off-on cells. Even at this low level, we see the nervous system processing information in terms of patterns of neural activation.
Both edge and bar detectors are spe- cific with respect to position, orientation, and width. That is, they respond only to stimulation in a small area of the visual field, to bars and edges in a small range of orientations, and to bars and edges of certain widths. Different detectors are tuned to different widths and orienta- tions. Any bar or edge anywhere in the visual field, at any orientation, will elicit a maximum response from some subset of detectors.
Hubel and Wiesel’s (1977) hypercolumn representa- tion of cells in the primary visual cortex. They found that the visual cortex is divided into 2 × 2 mm regions, which they called hypercolumns. Each hyper- column represents a particular region of the visual field. The organization of the visual cortex is topographic, and so adjacent areas of the visual field are represented in adjacent hypercolumns. Each hypercolumn itself has a two-dimensional (2-D) organization. Along one dimension, alternating rows receive input from the right and left eyes. Along the other dimension, the cells vary in the orientation to which they are most sensitive, with cells in adjacent rows representing similar orientations. This or- ganization should impress upon us how much information is encoded about the visual scene. Hundreds of regions of space are represented separately for each eye, and within these regions many different orientations are represented. In addition, different cells code for different sizes and widths of line.Thus, an enormous amount of information has been extracted from the visual signal before it even leaves the first cortical areas.
In addition to this rich representation of line orientation, size, and width, the visual system extracts other information from the visual signal. For in- stance, we can also perceive the colors of objects and whether they are moving. Livingstone and Hubel (1988) proposed that the visual system processes these various dimensions (form, color, and movement) separately. Many different visual pathways and many different areas of the cortex are devoted to visual processing (32 visual areas in the count by Van Essen & DeYoe, 1995). Different pathways have cells that are differentially sensitive to color, movement, and orientation. Thus, the visual system ana- lyzes a stimulus into many independent features in specific locations. Such spatial representations of visual features are called feature maps (Wolfe, 1994), with separate maps for color, orientation, and movement. Thus, if a vertical red bar is moving at a particular location, there are separate feature maps representing that it is red, vertical, and moving in that location. The maps for color, orientation, and movement are separate.
tree will suddenly become clear, because the images of nearby leaves and branches will move across the im- ages of more distant ones, providing clear information about depth.
Although it is easy to demonstrate the importance to depth perception of such cues as texture gradient, stereopsis, and motion parallax, it has been a chal- lenge to understand how the brain actually processes such information. A number of researchers in the area of computational vision have worked on the problem. For instance, David Marr (1982) has been influential in his proposal that these various sources of information work together to create what he calls a 2½-D sketch that identifies where various visual features are located relative to the viewer. While it required a lot of informa- tion processing to produce this 2½-D sketch, a lot more is required to convert that sketch into actual perception of the world. In particular, such a sketch represents only parts of surfaces and does not yet identify how these parts go together to form images of objects in the environment. Marr used the term 3-D model to refer to a later represen- tation of objects in a visual scene.
■Cues such as texture gradient, stereopsis, and motion parallax com- bine to create a representation of the locations of surfaces in 3-D space.
Both edge and bar detectors are spe- cific with respect to position, orientation, and width. That is, they respond only to stimulation in a small area of the visual field, to bars and edges in a small range of orientations, and to bars and edges of certain widths. Different detectors are tuned to different widths and orienta- tions. Any bar or edge anywhere in the visual field, at any orientation, will elicit a maximum response from some subset of detectors.
Hubel and Wiesel’s (1977) hypercolumn representa- tion of cells in the primary visual cortex. They found that the visual cortex is divided into 2 × 2 mm regions, which they called hypercolumns. Each hyper- column represents a particular region of the visual field. The organization of the visual cortex is topographic, and so adjacent areas of the visual field are represented in adjacent hypercolumns. Each hypercolumn itself has a two-dimensional (2-D) organization. Along one dimension, alternating rows receive input from the right and left eyes. Along the other dimension, the cells vary in the orientation to which they are most sensitive, with cells in adjacent rows representing similar orientations. This or- ganization should impress upon us how much information is encoded about the visual scene. Hundreds of regions of space are represented separately for each eye, and within these regions many different orientations are represented. In addition, different cells code for different sizes and widths of line.Thus, an enormous amount of information has been extracted from the visual signal before it even leaves the first cortical areas.
In addition to this rich representation of line orientation, size, and width, the visual system extracts other information from the visual signal. For in- stance, we can also perceive the colors of objects and whether they are moving. Livingstone and Hubel (1988) proposed that the visual system processes these various dimensions (form, color, and movement) separately. Many different visual pathways and many different areas of the cortex are devoted to visual processing (32 visual areas in the count by Van Essen & DeYoe, 1995). Different pathways have cells that are differentially sensitive to color, movement, and orientation. Thus, the visual system ana- lyzes a stimulus into many independent features in specific locations. Such spatial representations of visual features are called feature maps (Wolfe, 1994), with separate maps for color, orientation, and movement. Thus, if a vertical red bar is moving at a particular location, there are separate feature maps representing that it is red, vertical, and moving in that location. The maps for color, orientation, and movement are separate.
tree will suddenly become clear, because the images of nearby leaves and branches will move across the im- ages of more distant ones, providing clear information about depth.
Although it is easy to demonstrate the importance to depth perception of such cues as texture gradient, stereopsis, and motion parallax, it has been a chal- lenge to understand how the brain actually processes such information. A number of researchers in the area of computational vision have worked on the problem. For instance, David Marr (1982) has been influential in his proposal that these various sources of information work together to create what he calls a 2½-D sketch that identifies where various visual features are located relative to the viewer. While it required a lot of informa- tion processing to produce this 2½-D sketch, a lot more is required to convert that sketch into actual perception of the world. In particular, such a sketch represents only parts of surfaces and does not yet identify how these parts go together to form images of objects in the environment. Marr used the term 3-D model to refer to a later represen- tation of objects in a visual scene.
■Cues such as texture gradient, stereopsis, and motion parallax com- bine to create a representation of the locations of surfaces in 3-D space.
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