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This paper discusses a new method for capturing the complete appearance of both synthetic and real world objects and scenes, representing this information, and then using this representation to render images of the object from new camera positions. Unlike the shape capture process traditionally used in computer vision and the rendering process traditionally(More)
With recent advances in mobile computing, the demand for visual localization or landmark identification on mobile devices is gaining interest. We advance the state of the art in this area by fusing two popular representations of street-level image data—facade-aligned and viewpoint-aligned— and show that they contain complementary information that can be(More)
Mobile phones have evolved into powerful image and video processing devices equipped with high-resolution cameras, color displays, and hardware-accelerated graphics. They are also increasingly equipped with a global positioning system and connected to broadband wireless networks. All this enables a new class of applications that use the camera phone to(More)
Detecting text in natural images is an important prerequisite. In this paper, we propose a novel text detection algorithm, which employs edge-enhanced Maximally Stable Extremal Regions as basic letter candidates. These candidates are then filtered using geometric and stroke width information to exclude non-text objects. Letters are paired to identify text(More)
Establishing visual correspondences is an essential component of many computer vision problems, and is often done with robust, local feature-descriptors. Transmission and storage of these descriptors are of critical importance in the context of mobile distributed camera networks and large indexing problems. We propose a framework for computing low bit-rate(More)
This paper develops artificial life patterned after animals as evolved as those in the superclass Pisces. It demonstrates a virtual marine world inhabited by realistic artificial fishes. Our algorithms emulate not only the appearance, movement, and behavior of individual animals, but also the complex group behaviors evident in many aquatic ecosystems. We(More)
We present a method that unifies tracking and video content recognition with applications to Mobile Augmented Reality (MAR). We introduce the Radial Gradient Transform (RGT) and an approximate RGT, yielding the Rotation-Invariant, Fast Feature (RIFF) descriptor. We demonstrate that RIFF is fast enough for real-time tracking, while robust enough for large(More)
We survey popular data sets used in computer vision literature and point out their limitations for mobile visual search applications. To overcome many of the limitations, we propose the Stanford Mobile Visual Search data set. The data set contains camera-phone images of products, CDs, books, outdoor landmarks, business cards, text documents, museum(More)
Many mobile visual search (MVS) systems transmit query data from a mobile device to a remote server and search a database hosted on the server. In this paper, we present a new architecture for searching a large database directly on a mobile device, which can provide numerous benefits for network-independent, low-latency, and privacy-protected image(More)
We have built an outdoors augmented reality system for mobile phones that matches camera-phone images against a large database of location-tagged images using a robust image retrieval algorithm. We avoid network latency by implementing the algorithm on the phone and deliver excellent performance by adapting a state-of-the-art image retrieval algorithm based(More)