Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications
نویسندگان
چکیده
منابع مشابه
3d Vision Techniques and Applications to Photo- Realistic Scene Reconstruction
We present some novel concepts for the scene geometry recovery by a multi-camera system. A new global appearance measure is introduced for a novel generalized scene recovery methodology, called “the appearance-cloning.” For the efficient calibration of the multi-camera system, we investigate the projective properties of the concentric circle pattern. Specifically, two algebraic constraints from...
متن کاملComputer Vision for Microscopy Applications
The tremendous growth in digital imagery has introduced the need for accurate image analysis and classification. The applications include content based image retrieval in the World Wide Web and digital libraries (Dong & Yang, 2002; Heidmann, 2005; Smeulders et al., 2000; Veltkamp et al., 2001) scene classification (Huang et al., 2005; Jiebo et al., 2005), face recognition (Jing & Zhang, 2006; P...
متن کاملRealistic Mobility Simulator For Smart Traffic Systems And Applications
Cars have become essential elements of modern life. But nowadays the increasing number of cars also leads to problems: pollution, traffic jams, wasted time spent in traffic because of traffic bottlenecks, etc.. Traffic cannot cope anymore with the rate of car usage today. Fortunately, in the last years various Intelligent Transportation Systems (ITS) demonstrate innovative services relating to ...
متن کاملRobust Computer Vision ROBUST COMPUTER VISION Theory and Applications
non-real) objects: Plane geometric forms, solid geometric forms, and projected forms. The first class is the “real" class consisting of objects from the real world. The second class are representations of real objects. The third class are abstractions that can be represented using symbols but do not correspond to real objects (because they have no corresponding stimulus in the real world). Marr...
متن کاملDomain Adaptations for Computer Vision Applications
A basic assumption of statistical learning theory is that train and test data are drawn from the same underlying distribution. Unfortunately, this assumption doesn’t hold in many applications. Instead, ample labeled data might exist in a particular ‘source’ domain while inference is needed in another, ‘target’ domain. Domain adaptation methods leverage labeled data from both domains to improve ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computer Vision
سال: 2018
ISSN: 0920-5691,1573-1405
DOI: 10.1007/s11263-018-1073-7