Computer Vision Courses, Measurement, and Perception

Computer Vision Courses, Measurement, and Perception - Selamat datang di situs media global terbaru Xivanki, Pada halaman ini kami menyajikan informasi tentang Computer Vision Courses, Measurement, and Perception !! Semoga tulisan dengan kategori academics !! computer science !! computer vision !! paradigm !! perception !! philosophy !! ini bermanfaat bagi anda. Silahkan sebarluaskan postingan Computer Vision Courses, Measurement, and Perception ini ke social media anda, Semoga rezeki berlimpah ikut dimudahkan Allah bagi anda, Lebih jelas infonya lansung dibawah -->


The new semester began at CMU and I'm happy to announce that I'm TAing my advisor's 16-721 Learning Based Methods in Vision this semester. I'm also auditing Martial Hebert's Geometry Based Methods in Vision.

This semester we're trying to encourage students of 16-721 LBMV09 to discuss papers using a course discussion blog. Quicktopic has been used in the past, but this semester we're using Google's Blogger.com for the discussion!

In the first lecture of LBMV, we discussed the problem of Measurement versus Perception in a Computer Vision context. The idea is that while we could build vision systems to measure the external world, it is percepts such as "there is a car on the bottom of the image" and not measurements such as "the bottom of the image is gray" that we are ultimately interested in. However, the line between measurement and perception is somewhat blurry. Consider the following gedanken experiment: place a human in a box and feed him an image and the question "is there a car on the bottom of the image?". Is it legitimate to call this apparatus as a measurement device? If so, then isn't perception a type of measurement? We would still have the problem of building a second version of this measurement device -- different people have different notions of cars and when we start feeding two apparatuses examples of objects that are very close to trucks/buses/vans/cars then would would loss measurement repeatability.

This whole notion of measurement versus perception in computer vision is awfully similar to the theory and observation problem in philosophy of science. Thomas Kuhn would say that the window through which we peer (our scientific paradigm) circumscribes the world we see and thus it is not possible to make theory-independent observations. For a long time I have been a proponent of this post modern view of the world. The big question that remains is: for computer vision to be successful how much consensus must there be between human perception and machine perception? If according to Kuhn Aristotelian and Galilean physicists would have different "observations" of an experiment, then should we expect intelligent machines to see the same world that we see?

Demikian info Computer Vision Courses, Measurement, and Perception, Semoga dengan adanya postingan ini, Anda sudah benar benar menemukan informasi yang memang sedang anda butuhkan saat ini. Bagikan informasi Computer Vision Courses, Measurement, and Perception ini untuk orang orang terdekat anda, Bagikan infonya melalui fasilitas layanan Share Facebook maupun Twitter yang tersedia di situs ini.

Previous Post Next Post