Due to the advent of computer technology image-processing techniques have become increasingly important in a wide variety of applications. Image segmentation is a classic subject in the field of image processing and also is a hotspot and focus of image processing techniques. Several general-purpose algorithms and techniques have been developed for image segmentation. Since there is no general solution to the image segmentation problem, these techniques often have to be combined with domain knowledge in order to effectively solve an image segmentation problem for a problem domain. This paper presents a comparative study of the basic Block-Based image segmentation techniques.
Figure-ground segmentation referred as a target or foreground other part is called background is an important problem i.e., extract and separate them in order to identify and analyze object, in image processing [2, 3]. Segmentation is the process that subdivides an image into its constituent parts or objects [1...22]. The level to which this subdivision is carried out depends on the problem being solved, i.e., the segmentation should stop when the objects of interest in an application have been isolated.
Image Engineering illustrates the level of the image segmentation in image processing. Image Engineering can be divided into three levels [1, 3] as shown in Fig. 1. Image processing is low-level operations; it operated on the pixel-level. Starts with one image and produces a modified version, image into another form, of the same, or the transformation between the images and improves the visual effects of image. Image processing following three stages each is subdivided into different categories [1, 3]:
1) Reconstruction (Correction)
a. Restoration: Removal or minimization of image degradations. Two types: Radiometric and Geometric.
b. Reconstruction: Derive an image, two or higher dimensional, of inside view from several one-dim projections.
c. Mosaic: Combining of two or more patches of image. Required to get the view of the entire area.
a. Contrast stretching: Homogeneous images which do not have much change in their levels.
b. Noise filtering: to filter the unnecessary information. Filters like low pass, high pass, mean, median etc...
c. Histogram modification: E.g., Histogram Equalization.
d. Data compression: Higher compressed each pixel by: DCT by JPEG or Wavelet for with minimum loss.
e. Rotation: In mosaic to match with the second image. 3-pass shear is a common.
a. Segmentation: Subdivides an image into its objects depends on the problem.
b. Classification: Pixels labeling based on its grey value. Types of 'Spectral Analysis', in Remote Sensing imagery, are: Supervised are the known types of land while Unsupervised are the unknown ones .
Image analyses, the middle-level, it focuses on measuring. Principal Components Analysis (PCA) produces a new set of images from a given set. Image understanding is high-level operation which is further study on the nature of each target and the linkage of each other as well explanation of original image. Image segmentation is a key step from the image processing to image analysis; it, the segmentation, is the target expression and has important effect on the feature measurement and it is possible to make high-level image analysis and understanding [1, 3].
(2) Methods for Image Segmentation
Image segmentation techniques or methods are classified into two main categories Layer-Based Segmentation Methods and Block-Based Segmentation Methods [4, 10, 21] see Fig. 2.
Layer-Based Segmentation Methods Layered model: for object detection and image segmentation that composites the output of a bank of object detectors in order to define shape masks and explain the appearance, depth ordering, and that evaluates both class and instance segmentation [10, 21]. This type didn't discuss in this paper. And Block-Based Segmentation Methods which is based on various features found in the image. This might be colour information that is used to create histograms, or information about the pixels that indicate edges or boundaries or texture information [3...22]. Block-Based Image Segmentation methods are categorized on two properties: discontinuity and similarity into three groups:
• Region Based Methods: based on discontinuities.
• Edge or Boundary Based Methods: based on similarity [3...14].
• Hybrid Techniques .
These are the methods which were discussed in this paper, while there are two additional Block-Based Image Segmentation methods or categories : Pixel-Based Segmentation: or Point-Based Segmentation [6, 7, 11]. And Model-Based Segmentation: The human vision system has the ability to recognize objects even if they are not completely represented. It can be applied if the exact shape of the objects in the image is known [6, 7]. Segmentation is a process that divides an image into its regions or objects that have similar features or characteristics [2…22].
1. Region Based Methods: Divide the entire image into sub regions or clusters, e.g. all the pixels with same grey level in one region. [3...22].