Traži Video
Visual
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Korisnik: pocketchihiro |
"El Visual Kei no es Emo"Actualmente, por culpa de los medios de comunicación, han estado confundiendo el Visual Kei con el emo. Este pequeño documental habla brevemente sobre esto....al igual que aquellas personitas que no respetan ni al Visual kei ni al emo. Ya que dicen ser emos pero visten visual kei...si realmente eres Emo, respeta tu estilo y no copies o te acredites un estilo visual que no te pertenece...n_n VISUAL KEI HOY, OTAKU X SIEMPRE BYE BYEEE Por cierto....Emo es una cosa, Visual kei otra...no confundan. aaa y también EL OTAKU TAMPOCO ES VISUAL KEI....yo no se por que lo mencionan tanto, si yo no digo que es lo mismo LOL P.D. Visual kei hoy, otaku x siempre. Tagovi: El visual kei no es emo Visual Kei Emo Antiemo Manga Anime Naruto Cosplay consejo comunidad parejas personales aleatori |
Korisnik: FlameMasterAJ |
JRock Visual Kei BandOne of their latest singles. Tagovi: JRock Visual Kei |
Korisnik: googletechtalks |
Visual Perception with Deep LearningGoogle Tech Talks April, 9 2008 ABSTRACT A long-term goal of Machine Learning research is to solve highy complex "intelligent" tasks, such as visual perception auditory perception, and language understanding. To reach that goal, the ML community must solve two problems: the Deep Learning Problem, and the Partition Function Problem. There is considerable theoretical and empirical evidence that complex tasks, such as invariant object recognition in vision, require "deep" architectures, composed of multiple layers of trainable non-linear modules. The Deep Learning Problem is related to the difficulty of training such deep architectures. Several methods have recently been proposed to train (or pre-train) deep architectures in an unsupervised fashion. Each layer of the deep architecture is composed of an encoder which computes a feature vector from the input, and a decoder which reconstructs the input from the features. A large number of such layers can be stacked and trained sequentially, thereby learning a deep hierarchy of features with increasing levels of abstraction. The training of each layer can be seen as shaping an energy landscape with low valleys around the training samples and high plateaus everywhere else. Forming these high plateaus constitute the so-called Partition Function problem. A particular class of methods for deep energy-based unsupervised learning will be described that solves the Partition Function problem by imposing sparsity constraints on the features. The method can learn multiple levels of sparse and overcomplete representations of data. When applied to natural image patches, the method produces hierarchies of filters similar to those found in the mammalian visual cortex. An application to category-level object recognition with invariance to pose and illumination will be described (with a live demo). Another application to vision-based navigation for off-road mobile robots will be described (with videos). The system autonomously learns to discriminate obstacles from traversable areas at long range. This is joint work with Y-Lan Boureau, Sumit Chopra, Raia Hadsell, Fu-Jie Huang, Koray Kavakcuoglu, and Marc'Aurelio Ranzato. Speaker: Yann Le Cun Computational and Biological Learning Lab, Courant Institute of Mathematical Sciences, New York University. Tagovi: google techtalks techtalk engedu talk talks googletechtalks education |
Korisnik: googletechtalks |
Visual Thinking with Graph NetworkGoogle Tech Talks March, 13 2008 ABSTRACT Many visual perception tasks are fundamentally NP-hard computational problems. Solving these problems robustly requires thinking through combinatorially many hypothesis. Despite this, our human visual system performs these tasks effortlessly. How is this done? I would like to make two points on this topic. First, formulating visual thinking as NP-hard computation tasks has an important advantage: visual routines can be analyzed precisely to identify their behaviors independently of their implementations. Second, I will show there is a class of graph optimization problems which can be implemented using a distributed network system with physical (and plausible biological) interpretation. I will demonstrate this graph based approach for: 1) image segmentation using Normalized Cuts with explanations for illusory contours, visual pop out and attention; 2) salient contour grouping Speaker: Jianbo Shi Jianbo Shi was born in Shanghai, China. Since then he has been moving. He studied Computer Science and Mathematics as an undergraduate at Cornell University where he received his B.A. in 1994. He received his Ph.D. degree in Computer Science from University of California at Berkeley in 1998, for his thesis on Normalize Cuts image segmentation algorithm. He joined The Robotics Institute at Carnegie Mellon University in 1999 as a research faculty, where he lead the Human Identification at Distance(HumanID) project, developing vision techniques for human identification and activity inference. In January 2003, he joined the Department of Computer & Information Science at University of Pennsylvania as an Assistant Professor. His current research focus on human behavior analysis and image recognition-segmentation. His other research interests include image/video retrieval, and vision based desktop computing. His long-term interests center around a broader area of machine intelligence, he wishes to develop a "visual thinking" module that allows computers not only to understand the environment around us, but also to achieve higher level cognitive abilities such as machine memory and learning. Tagovi: google techtalks techtalk engedu talk talks googletechtalks education |
Korisnik: Orthros05 |
The Orthodox Church - A visual journeyThis video presents some sights and sounds of the Orthodox Church. Tagovi: Orthodox Christian |
Korisnik: xFu510nx |
iPhone Visual Voicemail Demohttp://whoismatt.com Demo of the iPhone's visual voicemail capabilities! Myspace: http://www.myspace.com/mattissocool Tagovi: Mac Macworld iPhone Video Demo Macintosh Apple Itunes Phone Keynote Computers Voicemail Computer Steve Jobs Apple.com |
Korisnik: NantoVision |
Visual Thinking #1Kermit gets a lesson in visual thinking from Harry the Hipster. Tagovi: Muppets Jim Henson |
Korisnik: googletechtalks |
The Visual Wiki: a new metaphor for knowledge access and managementGoogle Tech Talks June 4, 2008 ABSTRACT Successful knowledge management results in a competitive advantage in today's information- and knowledge-rich industries. The elaboration and integration of emerging web-based tools and services has proven suitable for collecting and organizing intellectual property. Due to an increasing information overload, information and knowledge visualization have become an effective method for representing complex bodies of knowledge in an alternative fashion by using visual languages. The focus of this research is the development of a "Visual Wiki", which combines the notion of a textual and a visual representation of knowledge. A Visual Wiki model has been proposed which provides a unified framework to design and discuss different approaches. Three prototypes of Visual Wikis have been implemented and evaluated according to the improvements to knowledge management applications that they facilitate. This is joint work with Christian Hirsch and John Grundy Tagovi: google techtalks techtalk engedu talk talks googletechtalks education |
Korisnik: NantoVision |
Visual Thinking #2A 1966 remake of "Visual Thinking", with Kermit taking the part of Harry the Hipster, and Grump replacing Kermit as the square. Tagovi: Muppets Jim Henson Jerry Juhl |
Korisnik: gee3peeo |
Ethan Fowler Stereo's "A Visual Sound"Ethan Fowler Stereo's "A Visual Sound" Tagovi: Ethan Fowler Stereo's "A Visual Sound" |
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Mobile Visual Search Engine on the Apple iPhoneArray Tagovi: |
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