The Experts below are selected from a list of 4215 Experts worldwide ranked by ideXlab platform

Shyichyi Cheng - One of the best experts on this subject based on the ideXlab platform.

  • fusion of Color edge detection and Color Quantization for Color image watermarking using principal axes analysis
    Pattern Recognition, 2007
    Co-Authors: Chentsung Kuo, Shyichyi Cheng
    Abstract:

    In the past few years, many gray-level image watermarking schemes have been proposed, although the extension to the Color case is rare and regularly accomplished by marking the image luminance, or by processing each Color channel separately. This paper presents a new Color image watermarking scheme that combines Color edge detection and Color Quantization using principal axes analysis in three-dimensional Color space. The watermark is hidden within the data by modifying a subset of carefully selected edge points to resist both geometric distortion and signal processing attacks. Experimental results show the robustness of the proposed scheme to resist common attacks.

  • a fast and novel technique for Color Quantization using reduction of Color space dimensionality
    Pattern Recognition Letters, 2001
    Co-Authors: Shyichyi Cheng, Chenkuei Yang
    Abstract:

    Abstract This paper describes a fast and novel technique for Color Quantization using reduction of Color space dimensionality. The Color histogram is repeatedly sub-divided into smaller and smaller classes. The Colors of each class are projected on a carefully selected line, such that the Color dis-similarities are preserved. Instead of using the principal axis of each class, the line is defined by the mean Color vector and the Color of the largest distance away from the mean Color. The vector composed of the projection values for each class is then used to cluster the Colors into two representative palette Colors. As a result, the computation in the Quantization process is fast. A fast pixel mapping algorithm based on the proposed data clustering algorithm is also presented in this paper. Experimental results show that the proposed algorithms quantize images with high image quality efficiently.

Adel Nadjaran Toosi - One of the best experts on this subject based on the ideXlab platform.

  • Color Quantization using modified artificial fish swarm algorithm
    Australasian Joint Conference on Artificial Intelligence, 2011
    Co-Authors: Danial Yazdani, Hadi Nabizadeh, Elyas Mohamadzadeh Kosari, Adel Nadjaran Toosi
    Abstract:

    Color Quantization (CQ) is one of the most important techniques in image compression and processing. Most of Quantization methods are based on clustering algorithms. Data clustering is an unsupervised classification technique and belongs to NP-hard problems. One of the methods for solving NP-hard problems is applying swarm intelligence algorithms. Artificial fish swarm algorithm (AFSA) fits in the swarm intelligence algorithms. In this paper, a modified AFSA is proposed for performing CQ. In the proposed algorithm, to improve the AFSA's efficiency and remove its weaknesses, some modifications are done on behaviors, parameters and the algorithm procedure. The proposed algorithm along with other multiple known algorithms has been used on four well known images for doing CQ. Experimental results comparison shows that the proposed algorithm has acceptable efficiency.

Yuchou Chang - One of the best experts on this subject based on the ideXlab platform.

  • matching book spine images for library shelf reading process automation
    Conference on Automation Science and Engineering, 2008
    Co-Authors: Dahjye Lee, Yuchou Chang, James Archibald, C Pitzak
    Abstract:

    Machine vision has become an important visual inspection technology for many automation applications. Using machine vision for automation can reduce operating costs and increase efficiency and accuracy. This paper presents an image matching technique designed specifically for improving the library inventory (shelf-reading) process. In contrast with more complex Color image matching techniques, the proposed method quantizes Color images of book spines into a limited number of Color indices and performs image matching on the quantized Color index images. This approach simplifies and speeds up the processing and improves the overall inventory process. The potential performance of this robust Color Quantization and image matching technique is demonstrated by the results of preliminary experiments.

  • A robust Color image Quantization algorithm based on knowledge reuse of K-means clustering ensemble
    Journal of Multimedia, 2008
    Co-Authors: Yuchou Chang, Jenny K. Archibald, Yi Hong, Dong Liang
    Abstract:

    This paper presents a novel Color image Quantization algorithm. This algorithm improves Color image Quantization stability and accuracy using clustering ensemble. In our approach, we firstly adopt manifold single k-means clusterings for the Color image to form a preliminary ensemble committee. Then, in order to avoid inexplicit correspondence among clustering groups, we use the original Color values of each clustering centroid directly to construct a final ensemble committee. A mixture model based on the expectation-maximization (EM) algorithm is used as a consensus function to combine the clustering groups of the final ensemble committee to obtain Color Quantization results. Experimental results reveal that the proposed Color Quantization algorithm is more stable and accurate than k-means clustering. The preprocessing step of the algorithm, k-means clustering, can be implemented and executed in parallel to improve processing speed.

  • Color Quantization and image analysis for automated fruit quality evaluation
    4th IEEE Conference on Automation Science and Engineering CASE 2008, 2008
    Co-Authors: D J Lee, Yuchou Chang, James K. Archibald, Christopher G. Greco
    Abstract:

    Machine vision has become an important non-destructive visual inspection technology for automation in the past two decades. Using machine vision for production automation can reduce operating costs and increase product value and quality. For agricultural products, Color is often a good indicator of product quality and maturity. This paper presents a novel image-dependent Color Quantization technique designed specifically for real-time Color evaluation in production automation applications. In contrast with more complex Color space conversion techniques, the proposed method makes it easy for a human operator to specify and adjust Color-preference settings for different Color groups representing distinct quality or maturity levels. The performance of this robust Color Quantization and image analysis technique in evaluating fruit maturity and detecting skin delamination defects is demonstrated using Medjool date samples collected from field testing. ©2008 IEEE.

Danial Yazdani - One of the best experts on this subject based on the ideXlab platform.

  • Color Quantization using modified artificial fish swarm algorithm
    Australasian Joint Conference on Artificial Intelligence, 2011
    Co-Authors: Danial Yazdani, Hadi Nabizadeh, Elyas Mohamadzadeh Kosari, Adel Nadjaran Toosi
    Abstract:

    Color Quantization (CQ) is one of the most important techniques in image compression and processing. Most of Quantization methods are based on clustering algorithms. Data clustering is an unsupervised classification technique and belongs to NP-hard problems. One of the methods for solving NP-hard problems is applying swarm intelligence algorithms. Artificial fish swarm algorithm (AFSA) fits in the swarm intelligence algorithms. In this paper, a modified AFSA is proposed for performing CQ. In the proposed algorithm, to improve the AFSA's efficiency and remove its weaknesses, some modifications are done on behaviors, parameters and the algorithm procedure. The proposed algorithm along with other multiple known algorithms has been used on four well known images for doing CQ. Experimental results comparison shows that the proposed algorithm has acceptable efficiency.

Angela Y Wu - One of the best experts on this subject based on the ideXlab platform.

  • an efficient k means clustering algorithm analysis and implementation
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002
    Co-Authors: Tapas Kanungo, David M Mount, Nathan S Netanyahu, Christine D Piatko, Ruth Silverman, Angela Y Wu
    Abstract:

    In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's (1982) algorithm. We present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in Color Quantization, data compression, and image segmentation.