Machine vision: studying surface textures
[Audio Version]
Following is another in my series of ad hoc journal entries I've been keeping of my thoughts on machine vision.
It seems that one can't escape the complexities that come with texture. Previously, I had experimented with very low resolution images because they can blur a texture into a homogeneous color blob. There's a terrible tradeoff, though. The texture smoothes out while the edges get blocky and less linear. Too much information is lost. What's more, a lower resolution image will likely have more uneven distribution of similar but different colored pixels. A ball goes from having a texture with lots of local color similarity to a small number of pixels with unique colors.
Moreover, it's a struggle for me with my own excellent visual capabilities to really understand what's in such low resolution images. It can't be that good of a technique if the source images aren't intelligible to human eyes.
I think I will have to revisit the subject of studying textures. An appropriate venue would be a scene with a simple white or black backdrop and uniform-texture objects moving around in close proximity to the video camera. Objects might include a tennis ball, various rocks, pieces of fabric, plastic sheets, and so on. The goal would be to get an agent to "understand" such textures. One critical aspect of understanding would be that it could later identify a texture it has studied. The moving around of an object with a given texture is important. It's not enough to use a still image of a texture to really understand it. Textured surfaces tend to have wide variation in their appearances as they are moved about and reshaped. To recognize a texture requires that it be abstracted in a way that can overcome such variations.
Following is another in my series of ad hoc journal entries I've been keeping of my thoughts on machine vision.
It seems that one can't escape the complexities that come with texture. Previously, I had experimented with very low resolution images because they can blur a texture into a homogeneous color blob. There's a terrible tradeoff, though. The texture smoothes out while the edges get blocky and less linear. Too much information is lost. What's more, a lower resolution image will likely have more uneven distribution of similar but different colored pixels. A ball goes from having a texture with lots of local color similarity to a small number of pixels with unique colors.
Moreover, it's a struggle for me with my own excellent visual capabilities to really understand what's in such low resolution images. It can't be that good of a technique if the source images aren't intelligible to human eyes.
I think I will have to revisit the subject of studying textures. An appropriate venue would be a scene with a simple white or black backdrop and uniform-texture objects moving around in close proximity to the video camera. Objects might include a tennis ball, various rocks, pieces of fabric, plastic sheets, and so on. The goal would be to get an agent to "understand" such textures. One critical aspect of understanding would be that it could later identify a texture it has studied. The moving around of an object with a given texture is important. It's not enough to use a still image of a texture to really understand it. Textured surfaces tend to have wide variation in their appearances as they are moved about and reshaped. To recognize a texture requires that it be abstracted in a way that can overcome such variations.
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