Presentation on theme: "Image Understanding Roxanne Canosa, Ph.D.. Introduction Computer vision Give machines the ability to see The goal is to duplicate the effect of human."— Presentation transcript:
Introduction Computer vision Give machines the ability to see The goal is to duplicate the effect of human visual processing We live in a 3-D world, but camera sensors can only capture 2-D information. Flip side of computer graphics?
Introduction Image processing The goal is to present the image to the system in a useful form image capture and early processing remove noise detect luminance differences detect edges enhance image
Introduction Image analysis The goal is to extract useful information from the processed image identify boundaries find connected components label regions segment parts of objects group parts together into whole objects
Introduction Image understanding The goal is to make sense of the information. Draw qualitative, or semantic, conclusions from the quantitative information. make a decision about the quantitative information classify the parts recognize objects understand the objects’ usage and the meaning of the scene
Low-level Representations Low-level: little knowledge about the content of the image The data that is manipulated usually resembles the input image. For example, if the image is captured using a CCD camera (2-D), the representation can be described by an image function whose value is brightness depending on 2 parameters: the x-y coordinates of the location of the brightness value.
Low-Level Mechanisms Low-level vision only takes us to the sophistication of a very expensive digital camera
High-level Representations High-level: extract meaningful information from the low-level representation. Image may be mapped to a formalized model of the world (model may change dynamically as new information becomes available) Data to be processed is dramatically reduced: instead of dealing with pixel values, deal with features such as shape, size, relationships, etc Usually expressed in symbolic form
High-Level Mechanisms High-level vision and perception requires brain functions that we do not fully understand yet
Bottom-up vs. Top-down Bottom-up: processing is content-driven Top-down: processing is context-driven Goal: combine knowledge about content as well as context. Goals, plans, history, expectations Imitate human cognition and the ability to make decisions based on extracted information
Bottom-up v. Top-down Top-Down?Bottom-up? Information flow
The Human Visual System Optical information from the eyes is transmitted to the primary visual cortex in the occipital lobe at the back of the head.
The Human Visual System - 20 mm focal length lens - iris controls amount of light entering eye by changing the size of the pupil
The Human Visual System Light enters the eye through the cornea, aqueous humor, lens, and vitreous humor before striking the light-sensitive receptors of the retina. After striking the retina, light is converted into electrochemical signals that are carried to the brain via the optic nerve.
The Human Visual System image from www.photo.net/photo/edscott/vis00010.htm
A biological neural network in which neurons inhibit spatially neighboring neurons. Architecture of first few layers of retina. Input light level Layer n Layer n + 1 10 5 5 5 Output perception 3 3 2 7 6 6 10-2-2 = 10-2-1 = 5-2-1 = 5-1-1 = +1 -0.2 10 5
Simultaneous Contrast Two regions that have identical spectra result in different color (lightness) perceptions due to the spectra of the surrounding regions Background color can visibly affect the perceived color of the target
Original Painting Task-Oriented Vision Free ViewingEstimate the economic level of the people Judge their ages Guess what they had been doing before the visitor’s arrival Remember the clothes worn by the people
Change Blindness Lack of attention to an object causes failure to perceive it People find it difficult to detect major changes in a scene if those changes occur in objects that are not the focus of attention Our impression that our visual capabilities give us a rich, complete, and detailed representation of the world around us is a grand illusion!
Possible M.S. Projects Comparison of saliency map generation techniques Feature-based Graph-based Information theory-based Object detection from salient keypoints SIFT features Multi-resolution images