International Journal of Image, Graphics and Signal Processing
International Journal of Image, Graphics and Signal Processing (IJIGSP) is a peer reviewed journal in the field of Image, Graphics and Signal Processing. The journal is published 12 issues per year by the MECS Publisher from 2012. All papers will be blind reviewed. Accepted papers will be available on line (free access) and in printed version. No publication fee.
JIGSP is publishing refereed, high quality original research papers in all areas of Image, Graphics and Signal Processing.
IJIGSP has been indexed by several world class databases: Google Scholar, Microsoft Academic Search, CrossRef, DOAJ, IndexCopernicus, INSPEC(IET), EBSCO, JournalSeek, ULRICH's Periodicals Directory, WordCat, Scirus, Academic Journals Database, Stanford University Libraries, Cornell University Library, UniSA Library, CNKI Scholar, ProQuest, J-Gate, ZDB, BASE, OhioLINK, iThenticate, Open Access Articles, Open Science Directory, National Science Library of Chinese Academy of Sciences, The HKU Scholars Hub, etc...
The journal publishes original papers in the field of Image, Graphics and Signal Processing which covers, but not limited to the following scope:
Neurophysiology image processing
Image quantification and image codes
Image reconstruction and image enhancement
Image segmentation and feature extraction
Image fusion and ultra resolution
Information hiding and digital watermarking
Content-based Image Information Retrieval
Video transmission and analysis
Remote sensing imagery processing and medicine imagery processing
Image processing in industry and agriculture
Artificial Intelligent System
Data Mining and Knowledge Discovery
Graph and Image Processing
Computer Application Technology
Modern Education & Computer Science Press
As one of the fundamental approaches of digital image processing, image segmentation is the premise of feature extraction and pattern recognition. This paper enumerates and reviews main image segmentation algorithms, then presents basic evaluation methods for them, and finally discusses the prospect of image segmentation. Some valuable characteristics of image segmentation come out based on a large number of comparative experiments.
This paper presents a new spline filter named approximating spline filter for surface metrology. The purpose is to provide a new approach of Gaussian filter and evaluate the characteristics of an engineering surface more accurately and comprehensively. First, the configuration of approximating spline filter is investigated, which describes that this filter inherits all the merits of an ordinary spline filter e.g. no phase distortion and no end distortion. Then, the approximating coefficient selection is discussed, which specifies an important property of this filter-the convergence to Gaussian filter. The maximum approximation deviation between them can be controlled below 4.36% , moreover, be decreased to less than 1% when cascaded. Since extended to 2 dimensional (2D) filter, the transmission deviation yields within -0.63% : +1.48% . It is proved that the approximating spline filter not only achieves the transmission characteristic of Gaussian filter, but also alleviates the end effect on a data sequence. The whole computational procedure is illustrated and applied to a work piece to acquire mean line whereas a simulated surface to mean surface. These experimental results indicate that this filtering algorithm for 11200 profile points and 2000 × 2000 form data, only spends 8ms and 2.3s respectively.
This paper describes a novel 3 dimensional color measurement system. After 3 kinds of geometrical features are analyzed, the line features were selected. A calibration board with right-angled triangle outline was designed to improve the calibration precision. For this system, two algorithms are presented. One is the calibration algorithm between 2 dimensional laser range finder (2D LRF), while the other is for 2D LRF and the color camera. The result parameters were obtained through solving the constrain equations by the correspond data between the 2D LRF and other two sensors. The 3D color reconstruction experiments of real data prove the effectiveness and the efficient of the system and the algorithms.
In this paper we propose a color image segmentation algorithm based on perceptual color vision model. First, the original image is divide into image blocks which are not overlapped; then, the mean and variance of every image back was calculated in CIEL*a*b* color space, and the image blocks were divided into homogeneous color blocks and texture blocks by the variance of it. The initial seed regions are automatically selected depending on calculating the homogeneous color blocks' color difference in CIEL*a*b* color space and spatial information. The color contrast gradient of the texture blocks need to calculate and the edge information are stored for regional growing. The fuzzy region growing algorithm and coloredge detection to obtain a final segmentation map. The experimental segmentation results hold favorable consistency in terms of human perception, and confirm effectiveness of the algorithm.