Change detection plays a very important role in many vision applications. Most change detection algorithms assume that the illumination on a scene will remain constant. Unfortunately, this assumption is not necessarily valid outside a well-controlled laboratory setting. The accuracy of existing algorithms diminishes significantly when confronted with image sequences in which the illumination is allowed to vary. In this note, we present two techniques for change detection that have been developed to deal with the more general scenario where illuination is not assumed to be constant. A detailed description of both new methods, the derivative model method and the shading model method, is provided. Results are presented for applying each of the techniques discussed to various image pairs. e 19x9 Academic press. hc.Detecting changes is fundamental to one's perception of the world. After all, the world we live in is dynamic, and the inputs to our senses are constantly changing. Many models have been proposed for detection of motion in the human visual system [13-171. In this note our concern is only change detection; motion detection may use change detection, but it is considered beyond the scope of this discussion. It is our aim is to develop robust change detection techniques for machine vision systems.It is not surprising that the process of change detection is fundamental to many machine vision applications. Systems that track moving objects [18], analyze cloud motion, mointor the growth of crops, or analyze traffic flow [2-41, are just a few examples of machine vision systems that use change detection algorithms. These algorithms provide the low level information that can be used by higher level algorithms to determine the information desired (the trajectory of an object, the growth of a tree, etc.). Therefore, for these systems to operate successfully, it is extremely important that change detection algorithms to accurate and robust.Change detection may take place either at the pixel level or at a higher level-by comparing features. We will address change detection at the pixel level. Our motivation in developing robust techniques for change detection at the pixel level is the possibility of very fast change detection for robotic applications.In this note, we present two new methods for change detection: the derivative model method and the shading model method. The derivative model method uses partial derivatives with respect to the pixel coordinates of a second order gray level surface model to compare regions and determine if a change has taken place. This

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Change detection plays a very important role in many vision applications. Most change detection algorithms assume that the illumination on a scene will remain constant. Unfortunately, this assumption is not necessarily valid outside a well-controlled laboratory setting. The accuracy of existing algorithms diminishes significantly when confronted with image sequences in which the illumination is allowed to vary. In this note, we present two techniques for change detection that have been developed to deal with the more general scenario where illuination is not assumed to be constant. A detailed description of both new methods, the derivative model method and the shading model method, is provided. Results are presented for applying each of the techniques discussed to various image pairs. e 19x9 Academic press. hc.Detecting changes is fundamental to one's perception of the world. After all, the world we live in is dynamic, and the inputs to our senses are constantly changing. Many models have been proposed for detection of motion in the human visual system [13-171. In this note our concern is only change detection; motion detection may use change detection, but it is considered beyond the scope of this discussion. It is our aim is to develop robust change detection techniques for machine vision systems.It is not surprising that the process of change detection is fundamental to many machine vision applications. Systems that track moving objects [18], analyze cloud motion, mointor the growth of crops, or analyze traffic flow [2-41, are just a few examples of machine vision systems that use change detection algorithms. These algorithms provide the low level information that can be used by higher level algorithms to determine the information desired (the trajectory of an object, the growth of a tree, etc.). Therefore, for these systems to operate successfully, it is extremely important that change detection algorithms to accurate and robust.Change detection may take place either at the pixel level or at a higher level-by comparing features. We will address change detection at the pixel level. Our motivation in developing robust techniques for change detection at the pixel level is the possibility of very fast change detection for robotic applications.In this note, we present two new methods for change detection: the derivative model method and the shading model method. The derivative model method uses partial derivatives with respect to the pixel coordinates of a second order gray level surface model to compare regions and determine if a change has taken place. This

Conventional approaches to recovering depth from gray-level imagery have involved obtaining two or more images, applying an "interest" operator, and solving the correspondence problem. Unfortunately, the computational complexity involved in feature extraction and solving the correspondence problem makes existing techniques unattractive for many real-world robotic applications. By approaching the problem from more of an engineering perspective, we have developed a new depth recovery technique that completely avoids the computationally intensive steps of feature selection and correspondence required by conventional approaches. The Intensity Gradient Analysis technique (IGA) is a depth recovery algorithm that exploits the properties of the MCSO (moving camera, stationary objects) scenario. Depth values are obtained by analyzing temporal intensity gradients arising from the optic flow field induced by known camera motion. In doing so, IGA avoids the feature extraction and correspondence steps of conventional approaches and is therefore very fast. A detailed description of the algorithm is provided along with experimental results from complex laboratory scenes.

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