The Experts below are selected from a list of 4320 Experts worldwide ranked by ideXlab platform

Joseph Jaja - One of the best experts on this subject based on the ideXlab platform.

  • An on-line variable-length Binary Encoding of text
    Information Sciences, 1996
    Co-Authors: Tinku Acharya, Joseph Jaja
    Abstract:

    Abstract We present a methodology for on-line variable-length Binary Encoding of a dynamically growing set of integers. Our Encoding maintains the prefix property that enables unique decoding of a string of integers from the set. In order to develop the formalism of this on-line Binary Encoding, we define a unique Binary tree data structure called the “phase in Binary tree.” To show the utility of this on-line variable-length Binary Encoding, we apply this methodology to encode the pointers generated by the LZW algorithm. The experimental results obtained illustrate the superior performance of our algorithm compared to the most widely used algorithms. This on-line variable-length Binary Encoding can be applied in other dictionary-based text compression schemes as well to effectively encode the output pointers to enhance the compression ratio.

  • Data Compression Conference - Enhancing Lempel-Ziv codes using an on-line variable length Binary Encoding
    Proceedings of Data Compression Conference - DCC '96, 1996
    Co-Authors: Tinku Acharya, Joseph Jaja
    Abstract:

    Summary form only given. The LZW algorithm is the most popular dictionary-based adaptive text compression scheme [Welch 1984]. In the LZW algorithm, a changing dictionary contains common strings that have been encountered so far in the text. The motivation for the present research is to explore an on-line variable-length Binary Encoding. We apply this Encoding to LZW codes for remedy of the problem that we discussed in Acharya and Mukherjee [1995]. We call it the LZWAJ algorithm.

  • Enhancing Lempel-Ziv codes using an on-line variable length Binary Encoding
    Proceedings of Data Compression Conference - DCC '96, 1996
    Co-Authors: Tinku Acharya, Joseph Jaja
    Abstract:

    Summary form only given. The LZW algorithm is the most popular dictionary-based adaptive text compression scheme [Welch 1984]. In the LZW algorithm, a changing dictionary contains common strings that have been encountered so far in the text. The motivation for the present research is to explore an on-line variable-length Binary Encoding. We apply this Encoding to LZW codes for remedy of the problem that we discussed in Acharya and Mukherjee [1995]. We call it the LZWAJ algorithm.

R. Van De Walle - One of the best experts on this subject based on the ideXlab platform.

  • Enhancing RSS feeds: eliminating overhead through Binary Encoding
    Third International Conference on Information Technology and Applications (ICITA'05), 2005
    Co-Authors: R. De Sutter, S. Lerouge, D. De Schrijver, R. Van De Walle
    Abstract:

    While RSS feeds increase in popularity as a mean to stay up to date with the most recent changes on a Web site, its XML representation is causing bandwidth related problems. These issues relate to the verbosity and the indivisible nature of the XML language. As such, bandwidth is wasted twofold as: (1) an RSS feed has no compact representation due to the plain text XML representation; and (2) previously received information about the RSS feed is discarded every time the RSS viewer retrieves the feed. Compression (Binary Encoding) of the XML data becomes relevant to eliminate the overhead. In this paper, we demonstrate the usefulness of the MPEG-7 Binary format for metadata to address both overhead issues. We validate its usability in a typical RSS scenario by calculating the byte size reduction and by comparing the processing speed of creating and parsing Binary encoded RSS feeds to traditional RSS feeds.

  • ICITA (1) - Enhancing RSS feeds: eliminating overhead through Binary Encoding
    Third International Conference on Information Technology and Applications (ICITA'05), 2005
    Co-Authors: R. De Sutter, S. Lerouge, D. De Schrijver, R. Van De Walle
    Abstract:

    While RSS feeds increase in popularity as a mean to stay up to date with the most recent changes on a Web site, its XML representation is causing bandwidth related problems. These issues relate to the verbosity and the indivisible nature of the XML language. As such, bandwidth is wasted twofold as: (1) an RSS feed has no compact representation due to the plain text XML representation; and (2) previously received information about the RSS feed is discarded every time the RSS viewer retrieves the feed. Compression (Binary Encoding) of the XML data becomes relevant to eliminate the overhead. In this paper, we demonstrate the usefulness of the MPEG-7 Binary format for metadata to address both overhead issues. We validate its usability in a typical RSS scenario by calculating the byte size reduction and by comparing the processing speed of creating and parsing Binary encoded RSS feeds to traditional RSS feeds.

Klaus Pawelzik - One of the best experts on this subject based on the ideXlab platform.

  • second order phase transition in neural rate coding Binary Encoding is optimal for rapid signal transmission
    Physical Review Letters, 2003
    Co-Authors: Matthias Bethge, David Rotermund, Klaus Pawelzik
    Abstract:

    : Here, we derive optimal tuning functions for minimum mean square reconstruction from neural rate responses subjected to Poisson noise. The shape of these tuning functions strongly depends on the length T of the time window within which action potentials (spikes) are counted in order to estimate the underlying firing rate. A phase transition towards pure Binary Encoding occurs if the maximum mean spike count becomes smaller than approximately three. For a particular function class, we prove the existence of a second-order phase transition. The analytically derived critical decoding time window length is in precise agreement with numerical results. Our analysis reveals that Binary rate Encoding should dominate in the brain wherever time is the critical constraint.

Tinku Acharya - One of the best experts on this subject based on the ideXlab platform.

  • An on-line variable-length Binary Encoding of text
    Information Sciences, 1996
    Co-Authors: Tinku Acharya, Joseph Jaja
    Abstract:

    Abstract We present a methodology for on-line variable-length Binary Encoding of a dynamically growing set of integers. Our Encoding maintains the prefix property that enables unique decoding of a string of integers from the set. In order to develop the formalism of this on-line Binary Encoding, we define a unique Binary tree data structure called the “phase in Binary tree.” To show the utility of this on-line variable-length Binary Encoding, we apply this methodology to encode the pointers generated by the LZW algorithm. The experimental results obtained illustrate the superior performance of our algorithm compared to the most widely used algorithms. This on-line variable-length Binary Encoding can be applied in other dictionary-based text compression schemes as well to effectively encode the output pointers to enhance the compression ratio.

  • Data Compression Conference - Enhancing Lempel-Ziv codes using an on-line variable length Binary Encoding
    Proceedings of Data Compression Conference - DCC '96, 1996
    Co-Authors: Tinku Acharya, Joseph Jaja
    Abstract:

    Summary form only given. The LZW algorithm is the most popular dictionary-based adaptive text compression scheme [Welch 1984]. In the LZW algorithm, a changing dictionary contains common strings that have been encountered so far in the text. The motivation for the present research is to explore an on-line variable-length Binary Encoding. We apply this Encoding to LZW codes for remedy of the problem that we discussed in Acharya and Mukherjee [1995]. We call it the LZWAJ algorithm.

  • Enhancing Lempel-Ziv codes using an on-line variable length Binary Encoding
    Proceedings of Data Compression Conference - DCC '96, 1996
    Co-Authors: Tinku Acharya, Joseph Jaja
    Abstract:

    Summary form only given. The LZW algorithm is the most popular dictionary-based adaptive text compression scheme [Welch 1984]. In the LZW algorithm, a changing dictionary contains common strings that have been encountered so far in the text. The motivation for the present research is to explore an on-line variable-length Binary Encoding. We apply this Encoding to LZW codes for remedy of the problem that we discussed in Acharya and Mukherjee [1995]. We call it the LZWAJ algorithm.

Xiaohua Tong - One of the best experts on this subject based on the ideXlab platform.

  • A Probability-Based Improved Binary Encoding Algorithm for Classification of Hyperspectral Images
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
    Co-Authors: Xiaohua Tong
    Abstract:

    This paper presents a probability-based improved Binary Encoding algorithm (PIBE) for classification of hyperspectral imagery. In the proposed PIBE method, the spectral, texture and shape information from hyperspectral images as well as height information from digital elevation models (if available) are combined to form a Binary code. Based on this, a probability-based approach is further introduced to match the constructed Binary code to the corresponding one obtain from target classes (or training data set). Some experiments on a pair of hyperspectral images confirm the effectiveness of the proposed PIBE method.

  • An inproved Binary Encoding algorithm for classification of hyperspectral images
    2012 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2012
    Co-Authors: Xiaohua Tong
    Abstract:

    Binary Encoding is a standard technique in classifying hyperspectral images. In this paper, an improved Binary Encoding (IBE) approach is proposed to integrate spectra, texture, shape and height (if it is possible) information of hyperspectral image data into a Binary Encoding algorithm for automatically deriving class cover information. First connected regions are extracted from the hyperspectral data by applying a segmentation algorithm. The mean spectrum per region is considered representative for the region. Predefined parameters were used to describing the texture and shape of the region. Together with the spectral information these parameters and the corresponding height values from the height source are converted into a Binary code. This code is then matched to that of a training data set for classification.

  • WHISPERS - An inproved Binary Encoding algorithm for classification of hyperspectral images
    2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), 2012
    Co-Authors: Xiaohua Tong
    Abstract:

    Binary Encoding is a standard technique in classifying hyperspectral images. In this paper, an improved Binary Encoding (IBE) approach is proposed to integrate spectra, texture, shape and height (if it is possible) information of hyperspectral image data into a Binary Encoding algorithm for automatically deriving class cover information. First connected regions are extracted from the hyperspectral data by applying a segmentation algorithm. The mean spectrum per region is considered representative for the region. Predefined parameters were used to describing the texture and shape of the region. Together with the spectral information these parameters and the corresponding height values from the height source are converted into a Binary code. This code is then matched to that of a training data set for classification.

  • Object-based Binary Encoding algorithm -an integration of hyperspectral data and DSM
    2009 Joint Urban Remote Sensing Event, 2009
    Co-Authors: Xiaohua Tong, Christian Heipke, Peter Lohmann, Uwe Sorgel
    Abstract:

    The advent of advanced processing techniques and high speed computers have led to the possibility of supplementary hyperspectral data with information about different kinds of object features that can be observed in the images, for example, shape and size. Other data sources, e.g., digital surface model from airborne laser scanning data, can provide height information for the object features. In this paper an improved Binary Encoding method (IBE) is proposed to integrate such additional information into the Binary Encoding matching method. The original Binary Encoding method proceeded spectral information pixel by pixel; IBE method is based on object-based classification. The hyperspectral and DSM data were corporately used in the method. During the method, the information of target objects was represented by 280 Binary codes according to IBE rules, practical experiences and user requirements. We applied the proposed method to classify the test area. The results show that the proposed method needs less training data, lower computation cost and can gain higher classification accuracy. It is beneficial especially for limited spatial extent and great variation of the ground contents.