The Experts below are selected from a list of 51 Experts worldwide ranked by ideXlab platform

Khaldoon Dhou - One of the best experts on this subject based on the ideXlab platform.

  • a new Chain Coding mechanism for compression stimulated by a virtual environment of a predator prey ecosystem
    Future Generation Computer Systems, 2020
    Co-Authors: Khaldoon Dhou
    Abstract:

    Abstract In this paper, the researcher introduces a new Chain Coding mechanism that employs a predator–prey agent-based modeling simulation with some modifications and simplifications and uses it in compression. In the proposed method, an image is ultimately represented by a virtual world consisting of three agent types: wolves, sheep, and paths. While sheep and paths do not change their locations during the program execution, wolves search for sheep to prey on. With each step, a wolf can determine its path by choosing between seven pertinent moves depending on the encountered information. The algorithm keeps track of the wolves’ initial locations and their movements and uses this as a new image representation. Additionally, the researcher introduces the ‘Lengthy Advance Move,’ the purpose of which is to group particular consecutive codes and further reduce the Chain. This, in turn, allows the researcher to experiment with different variations of the algorithm. Finally, the researcher applies arithmetic Coding on the series of movements for extra compression. The experimental results reveal that the current algorithm generates higher compression ratios than many standardized algorithms, including JBIG family algorithms. Most importantly, paired-sample t-tests reveal significant differences between the findings of the wolf-sheep predation algorithm and the other algorithms used as benchmarks for comparisons.

  • an innovative design of a hybrid Chain Coding algorithm for bi level image compression using an agent based modeling approach
    Applied Soft Computing, 2019
    Co-Authors: Khaldoon Dhou
    Abstract:

    Abstract In this article, the researcher introduces a hybrid Chain code for shape enCoding, as well as lossless and lossy bi-level image compression. The lossless and lossy mechanisms primarily depend on agent movements in a virtual world and are inspired by many agent-based models, including the Paths model, the Bacteria Food Hunt model, the Kermack–McKendrick model, and the Ant Colony model. These models influence the present technique in three main ways: the movements of agents in a virtual world, the directions of movements, and the paths where agents walk. The agent movements are designed, tracked, and analyzed to take advantage of the arithmetic Coding algorithm used to compress the series of movements encountered by the agents in the system. For the lossless mechanism, seven movements are designed to capture all the possible directions of an agent and to provide more space savings after being encoded using the arithmetic Coding method. The lossy mechanism incorporates the seven movements in the lossless algorithm along with extra modes, which allow certain agent movements to provide further reduction. Additionally, two extra movements that lead to more substitutions are employed in the lossless and lossy mechanisms. The empirical outcomes show that the present approach for bi-level image compression is robust and that compression ratios are much higher than those obtained by other methods, including JBIG1 and JBIG2, which are international standards in bi-level image compression. Additionally, a series of paired-samples t-tests reveals that the differences between the current algorithms’ results and the outcomes from all the other approaches are statistically significant.

Ernesto Bribiesca - One of the best experts on this subject based on the ideXlab platform.

  • A measure of tortuosity based on Chain Coding
    Pattern Recognition, 2013
    Co-Authors: Ernesto Bribiesca
    Abstract:

    A measure of tortuosity for 2D curves is presented. Tortuosity is a very important property of curves and has many applications, such as: how to measure the tortuosity of retinal blood vessels, intracerebral vasculature, aluminum foams, etc. The measure of tortuosity proposed here is based on a Chain code called Slope Chain Code (SCC). The SCC uses some ideas which were described in [A geometric structure for 2D shapes and 3D surfaces, Pattern Recognition 25 (1992) 483-496]. The SCC of a curve is obtained by placing straight-line segments of constant length around the curve (the endpoints of the straight-line segments always touching the curve), and calculating the slope changes between contiguous straight-line segments scaled to a continuous range from -1 to 1. The SCC of a curve is independent of translation, rotation, and optionally, of scaling, which is an important advantage for computing tortuosity. Also, the minimum and maximum values of tortuosity for curves and a measure of normalized tortuosity are described. Finally, an application of the proposed measure of tortuosity is presented which corresponds to the computation of retinal blood vessel tortuosity.

  • A method for representing 3D tree objects using Chain Coding
    Journal of Visual Communication and Image Representation, 2008
    Co-Authors: Ernesto Bribiesca
    Abstract:

    We describe a method for representing 3D (three-dimensional) tree objects by means of a Chain code. These 3D tree objects correspond to natural existing 3D tree structures, such as: blood vessels, plants, live trees, and so on. Thus, trees are digitalized and represented by a notation called the unique tree descriptor. The unique tree descriptor is invariant under translation and rotation. Furthermore, this descriptor is starting vertex normalized via the unique path in the tree. Also, it is possible to obtain the mirror image of any tree with ease. This unique tree descriptor preserves the shape of trees (and the shape of their branches), allows us to know their geometrical and topological properties. To determine if two 3D tree objects have the same shape, it is only necessary to see if their descriptors are equal. In this manner, graph comparisons and tree searches are eliminated. Also, the proposed tree descriptor is a good tool for storing of 3D tree objects. Finally, in order to prove our method for representing 3D tree objects, we obtain some tree descriptors of objects on real images.

L. Vecci - One of the best experts on this subject based on the ideXlab platform.

  • Fast Chain Coding of region boundaries
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
    Co-Authors: Primo Zingaretti, M. Gasparroni, L. Vecci
    Abstract:

    A fast single-pass algorithm to convert a multivalued image from a raster-based representation into Chain codes is presented. All Chain codes are obtained in linear time with respect to the number of Chain segments that are generated at each raster according to a set of templates. A formal statement and the complexity and performance analysis of the algorithm are given.

Roy Jefferis - One of the best experts on this subject based on the ideXlab platform.

Junjian Liu - One of the best experts on this subject based on the ideXlab platform.

  • identifying low level sequence variants via next generation sequencing to aid stable cho cell line screening
    Biotechnology Progress, 2015
    Co-Authors: Sheng Zhang, Lisa Bartkowiak, Bernard Nabiswa, Pratibha Mishra, John Fann, David Ouellette, Ivan Correia, Dean Regier, Junjian Liu
    Abstract:

    Developing stable Chinese hamster ovary (CHO) cell lines for biotherapeutics is an irreversible process and therefore, key quality attributes, such as sequence variants, must be closely monitored during cell line development (CLD) to avoid delay in the developmental timeline, and more importantly, to assure product safety and efficacy. Sequence variants, defined as unintended amino acid substitution in recombinant protein primary structure, result from alteration at either the DNA or the protein level. Here, for the first time, we report the application of transcriptome sequencing (RNAseq) in an IgG1 monoclonal antibody (mAb) CLD campaign to detect, identify, and eliminate cell lines containing low-level point mutations in recombinant Coding sequence. Among the top eleven mAb producers chosen from transfectant, clone or subclone stages, three of the cell lines contained either missense or nonsense point mutations at a low level of less than 2%. Subsequent LC/MS/MS characterization detected ∼3% sequence variants with an amino acid change from Ser to Leu at residue 117 in the heavy Chain of transfectants 11 and 27. This substitution is consistent with the RNAseq finding of a C/T mutation located at 407 base pair (TCA→TTA) in the heavy Chain Coding sequence. Here we demonstrate that RNAseq is a rapid and highly sensitive method to identify low-level genetic mutation de novo corresponding to the amino acid substitution that elicits sequence variant(s). Its implementation in CLD constitutes an early and effective step in identifying desired CHO expression cell lines. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1077–1085, 2015