Conference Paper

Efficient error free chain coding of binary documents

Center for Image Process. & Integrated Comput., California Univ., Davis, CA
Conference: Data Compression Conference, 1995. DCC '95. Proceedings
Source: IEEE Xplore


Finite context models improve the performance of chain based
encoders to the point that they become attractive, alternative models
for binary image compression. The resulting code is within 4% of JBIG at
200 dpi and is 9% more efficient at 400 dpi

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    • "Several applications have motivated further study and development of this technique, since its introduction by Freeman in 1961 [1]. Among them, we point out the encoding of geographic maps containing terrain contours and coastlines [2], line drawings [3], bi-level images [4], gray-level images [5], object shapes [6] [7] [8] [9], and image partitions [10]. One of the most demanding applications has been region-based image coding. "
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    ABSTRACT: Recently, a new technique for the lossless encoding of boundary maps was introduced, which is based on the concept of "transition points". In this paper we show that, using a simple representation for the transition points, it is possible to use the JBIG image coding standard for the encoding of image partitions. Moreover, this new approach outperforms, in most cases, differential chain-coding both in efficiency and simplicity of implementation.
    Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing 01/1999; 6:3141-3144. DOI:10.1109/ICASSP.1999.757507
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    • "In this section we explain the geometric features which are extracted from the dominant hand of the signer without any glove [16]. The hand is tracked by the tracking method described in [17] and segmented by using a simple chain coding method [18]. The used tracking algorithm prevents taking possibly wrong local decisions because the tracking is done at the end of a sequence by tracing back the decisions to reconstruct the best path. "
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    ABSTRACT: Sign language is used by the deaf and hard of hearing people for communication. Automatic sign language recognition is a challenging research area since sign language often is the only way of communication for the deaf people. Sign language includes different components of visual actions made by the signer using the hands, the face, and the torso, to convey his/her meaning. To use different aspects of signs, we combine the different groups of features which have been extracted from the image frames recorded directly by a stationary camera. We combine the features in two levels by employing three techniques. At the feature level, an early feature combination can be performed by concatenating and weighting different feature groups, or by concatenating feature groups over time and using LDA to choose the most discriminant elements. At the model level, a late fusion of differently trained models can be carried out by a log-linear model combination. In this paper, we investigate these three combination techniques in an automatic sign language recognition system and show that the recognition rate can be significantly improved.
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    ABSTRACT: In this paper we develop and analyze basic geometric structures for the topographic representation of images. One component of the geometric description is based on the Morse structure of the image, while a second one is connected to its drainage structure. These fundamental descriptors could be used as building blocks for a geometric multiscale representation of images in general and Digital Elevation Models (DEM) in particular. The topographic significance of the Morse and drainage structures of DEMs suggests that they can be used as the basis of an efficient encoding scheme. Therefore, we combine this geometric representation with partial differential equations based interpolation algorithms and lossless data compression techniques to develop a compression scheme for DEM. This algorithm permits to obtain compression results while controlling the maximum error in the decoded elevation map, a property that is necessary for the majority of applications dealing with DEM. We present the underlying theory and compression results for standard DEM data. A geometric approach for encoding and compressing Digital Elevation Models (DEM) is proposed and stud- ied in this paper. The proposed approach is based on the computation of the singularities of the topographic structure of the data, a kind of topological Morse theory, and the computation of its drainage structures. Together, they lead to an efficient representation of the topographic structures of these images. These basic structures can also be considered as a building block for the geometric multiscale representation of DEM. The emphasis and novelty of this paper is in the mathematics behind this framework, while the applications were reported in (58). DEM data consist of a discrete digital representation of a surface terrain. Each cell in a DEM corresponds to a point in 3D space. We can think of as the coordinates in the image domain and the height
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