Augmented reality is a research area that tries to embody an electronic information space within the real world, through computational devices. A crucial issue within this area, is the recognition of real world objects or situations. In natural language processing, it is much easier to determine interpretations of utterances, even if they are ill-formed, when the context or situation is fixed. We therefore introduce robust, natural language processing into a system of augmented reality with situation awareness. Based on this idea, we have developed a portable system, called the Ubiquitous Talker. This consists of an LCD display that reflects the scene at which a user is looking as if it is a transparent glass, a CCD camera for recognizing real world objects with color-bar ID codes, a microphone for recognizing a human voice and a speaker which outputs a synthesized voice. The Ubiquitous Talker provides its user with some information related to a recognized object, by using the display and voice. It also accepts requests or questions as voice inputs. The user feels as if he she is talking with the object itself through the system.
Ubiquitous computing @cite_4 proposes that very small computational devices (i.e., ubiquitous computers) be embedded and integrated into physical environments in such a way that they operate seamlessly and almost transparently. These devices are aware of their physical surroundings. In contrast to ubiquitous computers, our barcode (color-code) system is a low cost and reliable solution to making everything a computer. Suppose that every page in a book has a unique barcode. When the user opens a page, its page ID is detected by the system, so it can supply specific information regarding the page. When the user adds some information to the page, the system stores it with the page ID tagged for later retrieval. This is almost the same as having a computer in every page of the book without the cost. Our ID-aware system is better than ubiquitous computers from the viewpoint of reliability and cost-performance, since it does not require batteries and never breaks down.
@cite_4
1975697571 2084069552 2126349848 2104952406
There is increased interest in the use of color barcodes to encode more information per area unit than regular, black-and-white barcodes. For example, Microsoft's HCCB technology uses 4 or 8 colors per patch. Unfortunately, the observed color of a surface depends as much on the illuminant spectrum (and other viewing parameters) as on the surface reflectivity, which complicates the task of decoding the content of the barcode. A popular solution is to append to the barcode a “palette” with the reference colors. In this paper, we propose a new approach to color barcode decoding, one that does not require a reference color palette. Our algorithm decodes groups of color bars at once, exploiting the fact that joint color changes can be represented by a low-dimensional space. Decoding a group of bars (a “barcode element”) is thus equivalent to searching for the nearest subspace in a dataset. We also propose algorithms to select subsets of barcode elements that can be decoded with low error probability. Our experimental results show that our barcode decoding algorithm enables substantial information rate increase with respect to system that display a color palette, at a very low decoding error rate. Ubiquitous computing is the method of enhancing computer use by making many computers available throughout the physical environment, but making them effectively invisible to the user. Since we started this work at Xerox PARC in 1988, a number of researchers around the world have begun to work in the ubiquitous computing framework. This paper explains what is new and different about the computer science in ubiquitous computing. It starts with a brief overview of ubiquitous computing, and then elaborates through a series of examples drawn from various subdisciplines of computer science: hardware components (e.g. chips), network protocols, interaction substrates (e.g. software for screens and pens), applications, privacy, and computational methods. Ubiquitous computing offers a framework for new and exciting research across the spectrum of computer science. The use of colors increases the information storage capacities in barcodes. Increasing the number of colors to encode information makes the decoding a challenging task due to the dependency of the surface color on the illuminant spectrum, viewing parameters, printing device and material, color fading in addition to other nuisance parameters. A popular solution is the use of a color palette of reference colors printed with barcode. The decoding becomes more challenging if a mobile phone as a decoding device is used due to the capture of images from different distances as well as angles. In addition, the barcode images are often blurry because of incorrect focus or camera shake. We present an iterative decoding algorithm that decodes the colors of all barcode patches across the barcode by minimizing the overall observation error. Our method is able to decode colors in presence of blur using a small number of colors yet ensuring high information density. Camera cellphones have become ubiquitous, thus opening a plethora of opportunities for mobile vision applications. For instance, they can enable users to access reviews or price comparisons for a product from a picture of its barcode while still in the store. Barcode reading needs to be robust to challenging conditions such as blur, noise, low resolution, or low-quality camera lenses, all of which are extremely common. Surprisingly, even state-of-the-art barcode reading algorithms fail when some of these factors come into play. One reason resides in the early commitment strategy that virtually all existing algorithms adopt: The image is first binarized and then only the binary data are processed. We propose a new approach to barcode decoding that bypasses binarization. Our technique relies on deformable templates and exploits all of the gray-level information of each pixel. Due to our parameterization of these templates, we can efficiently perform maximum likelihood estimation independently on each digit and enforce spatial coherence in a subsequent step. We show by way of experiments on challenging UPC-A barcode images from five different databases that our approach outperforms competing algorithms. Implemented on a Nokia N95 phone, our algorithm can localize and decode a barcode on a VGA image (640 × 480, JPEG compressed) in an average time of 400-500 ms.