Computer vision is the study of analysis of pictures andvideos in order to achieve results similar to those as by men.Thus human vision acts as a lower bound on our ambitions withregard to computational image analysis (Turing Test for computervision). The field of computer vision has inspired a large numberof researchers in computer science, engineering, mathematics andeven though we are still far from achieving this ultimate goal,we have gathered a great amount of work and knowledge in theprocess and the techniques developed are widely used in the areassuch as medical imaging, video surveillance, computer graphics,video compression etc.
In his book on vision published in 1982, David Marr claimed that bottom-up or neurophysiological approaches to cognition had failed to live up to expectation, and were unable, in effect and in principle, to explain cognition. He advocated a top-down approach where formalization of cognitive operations was to serve as a starting point, followed by implementation of cognitive models, followed only in the last instance by neurophysiological investigation. Other commentators have followed suit. This presentation is an attempt to refute this position by showing that the relationship between neuroscience and cognitive science is in effect, and in principle, a two way street. The validity of bottom-up illumination of cognitive theorizing is demonstrated with numerous examples.
David Marr Vision Pdf 28
Marr proposed three distinct levels, given above, upon whichan information processing task must be understood. Thus we may conclude that the study of thecomputational theory, representation and algorithms involved in vision gives us an insightinto its challenges and allows us to obtain a deeper understanding whether it be in the contextof human or machine vision.
Besides intellectual curiosity, from the viewpoint of an engineer there is a strong motivationfor understanding vision in order to create technologies which may useful in their own right.Currently there is a great demand for 3D models of objects in the world; in particular we arenoticing the appearance of new 3D display technologies which will create the visualisations andinterfaces of the future and change the way we are able to access and interpret informationheld on computers. Computer vision offers the possibility of providing this 3D informationfor many applications that in turn provide their own motivation. The most significant currentand future applications of 3D model acquisition include:
The human visual system has little problem performing the tasks of recovering structure and interpreting scenes around us and this would lead us to assume that the task should be a relatively simple one for a computer. In fact this was the assumption, or perhaps optimism, of the early computer vision researchers who estimated time frames in the order of months for visual reconstruction tasks. We have yet to produce a fully automated visual system over the intervening years, however we have attained a much greater understanding of the problems involved and are able to explain how complex and challenging the task actually is.
The study of computer vision looks to attempt the inversion of the imaging process by studying the constraints on the interplay of the factors that make up an image and then to generate (either explicitly or in a learning framework) cues and priors which may be combined with image measurements to resolve the information loss and thus the ambiguities. For example, a general scene has a very high dimensionality in terms of the freedom in geometry and reflectance, so there will be insufficient information to estimate these values without strong priors on both the object shape and the surface reflectance. The study of different scene representations and the establishment of techniques which enforce different assumptions lies at the heart of 3D computer vision research.
All these algorithms are intended to operate on images captured on standard digital cameras and the processing is performed automatically. This means that the system may be used without any need for computer vision training or knowledge.
As we reminisced after Francis's death, we discovered that Francis had spoken with each of us on these molecular methods, across a twenty-year interval. In the mid-1980s, Francis spoke with Ralph, pressing him to consider how he might do highly specific lesions of single neuron types in motion cortex using molecular identifiers. At the time, the only tools imaginable were some sort of killer antibody approach. Twenty years later, Ed recalls Francis continuing to encourage this cross-disciplinary molecular and systems approach. It was absolutely imperative to Francis's vision of the maturation of neuroscience that there would be a conjoining of molecular biology and systems neuroscience. We are sure we were not unique in hearing this call; with how many others had he shared his vision? 2ff7e9595c
Comments