Psych 284 - Revision history

imported>Joyce: /* (C) Hyperspectral Imaging */

2012-04-09T05:50:36Z

← Older revision		Revision as of 05:50, 9 April 2012
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	*4. Landscape - separation of surface reflectance from scene illumination (including effects of haze)		*4. Landscape - separation of surface reflectance from scene illumination (including effects of haze)
	Answer the following questions		Answer the following questions
	*1. What parts of the hyperspectral signal are important for solving the classification problems		*1. What parts of the hyperspectral signal are important for solving the classification problems?
	*2. How would you design a sensor to optimally capture this information		*2. How would you design a sensor to optimally capture this information?
	*3. Once you have captured the relevant information, how do you optimally render it on the display		*3. Once you have captured the relevant information, how do you optimally render it on the display?

imported>Joyce: /* Introduction */

2012-04-09T05:50:02Z

Introduction

← Older revision		Revision as of 05:50, 9 April 2012
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	= Introduction =		= Introduction =

	The course project is to build a computational infrastructure for image sensing and processing. The project should include modeling ~~scene (~~visual stimuli) and sensors. The sensors can be part of a digital imaging system (including optics, color filter arrays, photodetector, image processing algorithms, etc.) or a human imaging system (including wavefront aberrations, cone photodetectors, retinal ganglion cell circuitry, and behavior).		The course project is to build a computational infrastructure for image sensing and processing. The project should include modeling visual stimuli and sensors. The sensors can be part of a digital imaging system (including optics, color filter arrays, photodetector, image processing algorithms, etc.) or a human imaging system (including wavefront aberrations, cone photodetectors, retinal ganglion cell circuitry, and behavior).

	= Software organization =		= Software organization =

imported>Joyce: /* Introduction */

2012-04-09T05:49:27Z

Introduction

← Older revision		Revision as of 05:49, 9 April 2012
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	= Introduction =		= Introduction =

	The course project is to build a computational infrastructure for image sensing and processing. The project should include modeling scene (visual stimuli) and sensors. The sensors can be part of a digital imaging system (including optics, color filter arrays, photodetector, etc.) or a human imaging system (including wavefront aberrations, cone photodetectors, retinal ganglion cell circuitry, and behavior).		The course project is to build a computational infrastructure for image sensing and processing. The project should include modeling scene (visual stimuli) and sensors. The sensors can be part of a digital imaging system (including optics, color filter arrays, photodetector, image processing algorithms, etc.) or a human imaging system (including wavefront aberrations, cone photodetectors, retinal ganglion cell circuitry, and behavior).

	= Software organization =		= Software organization =

imported>Joyce: /* Introduction */

2012-04-09T05:48:46Z

Introduction

← Older revision		Revision as of 05:48, 9 April 2012
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	= Introduction =		= Introduction =

	The course project is to build a computational infrastructure for image sensing and processing. The project should include modeling scene (visual stimuli) and sensors. The sensors can be part of a digital imaging system (including optics, color filter arrays, photodetector, etc.) or a human imaging system (including wavefront aberrations, cone photodetectors, retinal ganglion cell circuitry, ~~cortical visual circuits~~ and behavior).		The course project is to build a computational infrastructure for image sensing and processing. The project should include modeling scene (visual stimuli) and sensors. The sensors can be part of a digital imaging system (including optics, color filter arrays, photodetector, etc.) or a human imaging system (including wavefront aberrations, cone photodetectors, retinal ganglion cell circuitry, and behavior).

	= Software organization =		= Software organization =

imported>Joyce: /* Introduction */

2012-04-09T05:48:04Z

Introduction

← Older revision		Revision as of 05:48, 9 April 2012
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	= Introduction =		= Introduction =

	The course project is to build a computational infrastructure for image sensing and processing. The project should include modeling scene (visual stimuli)~~, optics~~ and sensors. The ~~optics and~~ sensors can be ~~components in~~ a digital imaging system (~~such as a camera~~) or a human imaging system (including visual circuits and behavior).		The course project is to build a computational infrastructure for image sensing and processing. The project should include modeling scene (visual stimuli) and sensors. The sensors can be part of a digital imaging system (including optics, color filter arrays, photodetector, etc.) or a human imaging system (including wavefront aberrations, cone photodetectors, retinal ganglion cell circuitry, cortical visual circuits and behavior).

	= Software organization =		= Software organization =

imported>Joyce: /* Introduction */

2012-04-09T05:45:26Z

Introduction

← Older revision		Revision as of 05:45, 9 April 2012
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	= Introduction =		= Introduction =

	The course project is to build a computational infrastructure for modeling visual circuits and behavior.		The course project is to build a computational infrastructure for image sensing and processing. The project should include modeling scene (visual stimuli), optics and sensors. The optics and sensors can be components in a digital imaging system (such as a camera) or a human imaging system (including visual circuits and behavior).

	= Software organization =		= Software organization =

imported>Joyce: /* Ideas */

2012-04-09T05:41:07Z

Ideas

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	*5. retinal ganglion cell image (there may be many, depending on how many cell types are created)		*5. retinal ganglion cell image (there may be many, depending on how many cell types are created)
	Then we can ask, how well can a standard image classification algorithm learn to distinguish image classes for each of the separate images. Does classification get easier or harder across these processing steps?		Then we can ask, how well can a standard image classification algorithm learn to distinguish image classes for each of the separate images. Does classification get easier or harder across these processing steps?

			===(C) Hyperspectral Imaging ===

			Select one of the following image data problem domains
			*1. Porcine tissue - classification of tissue type (spleen, stomach, etc.) or tissue change (ischemia)
			*2. Paintings - classification of painting pigment
			*3. Faces - classification of skin pigment components (melanin and hemoglobin)
			*4. Landscape - separation of surface reflectance from scene illumination (including effects of haze)
			Answer the following questions
			*1. What parts of the hyperspectral signal are important for solving the classification problems
			*2. How would you design a sensor to optimally capture this information
			*3. Once you have captured the relevant information, how do you optimally render it on the display

imported>Wandell: /* Crazy ideas */

2011-04-06T20:56:26Z

Crazy ideas

← Older revision		Revision as of 20:56, 6 April 2011
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	= Related work =		= Related work =

	= ~~Crazy ideas~~ =		= Ideas =

	Project ideas:		Project ideas:

imported>Winawer: /* Crazy ideas */

2011-04-06T20:52:03Z

Crazy ideas

← Older revision		Revision as of 20:52, 6 April 2011
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	Generate images containing noise patterns with particular amplitude distributions in the Fourier domain. For example a series of noise patterns such that the amplitude of the spatial frequency components in pixel space can be described by a function 1/f^n, with n spanning a range of values, such as 0 (white noise) 1 (pink noise), 2 (highly blurred) and so on. Then we calculate the amplitude spectrum for a series of images as they are processed through the visual system, including:		Generate images containing noise patterns with particular amplitude distributions in the Fourier domain. For example a series of noise patterns such that the amplitude of the spatial frequency components in pixel space can be described by a function 1/f^n, with n spanning a range of values, such as 0 (white noise) 1 (pink noise), 2 (highly blurred) and so on. Then we calculate the amplitude spectrum for a series of images as they are processed through the visual system, including:
	*1. the pixel image		*1. the pixel image
	*2. the irradiance image(assuming a typical LCD display)		*2. the irradiance image (assuming a typical LCD display)
	*3. the optical image ~~(after passing through the eye's optics)~~		*3. the radiant (optical) image
	*4. the cone absorption image		*4. the cone absorption image(s) (there are three)
	*5. retinal ganglion cell image		*5. retinal ganglion cell image (there may be many, depending on how many cell types are created)

	We would like to know, how doe the amplitude spectrum change across these different stages of processing?		We would like to know, how doe the amplitude spectrum change across these different stages of processing?
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	Take a series of standardized images of object categories (e.g., Caltech 101?), and transform each into a series of new images, so that we have (again)		Take a series of standardized images of object categories (e.g., Caltech 101?), and transform each into a series of new images, so that we have (again)
	*1. the pixel image		*1. the pixel image
	*2. the irradiance image(assuming a typical LCD display)		*2. the irradiance image (assuming a typical LCD display)
	*3. the optical image ~~(after passing through the eye's optics)~~		*3. the radiant (optical) image
	*4. the cone absorption image		*4. the cone absorption image (there are three)
	*5. retinal ganglion cell image		*5. retinal ganglion cell image (there may be many, depending on how many cell types are created)
	Then we can ask, how well can a standard image classification algorithm learn to distinguish image classes for each of the separate images. Does classification get easier or harder across these processing steps?		Then we can ask, how well can a standard image classification algorithm learn to distinguish image classes for each of the separate images. Does classification get easier or harder across these processing steps?

imported>Winawer: /* (B) Image Classification */

2011-04-06T20:46:54Z

(B) Image Classification

← Older revision		Revision as of 20:46, 6 April 2011
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	Take a series of standardized images of object categories (e.g., Caltech 101?), and transform each into a series of new images, so that we have (again)		Take a series of standardized images of object categories (e.g., Caltech 101?), and transform each into a series of new images, so that we have (again)
			*1. the pixel image
			*2. the irradiance image(assuming a typical LCD display)
			*3. the optical image (after passing through the eye's optics)
			*4. the cone absorption image
			*5. retinal ganglion cell image
			Then we can ask, how well can a standard image classification algorithm learn to distinguish image classes for each of the separate images. Does classification get easier or harder across these processing steps?