Survey of Object Detection Methods in Camouflaged Image

Camouflage is an attempt to conceal the signature of a target object into the background image. Camouflage detection methods or Decamouflaging method is basically used to detect foreground object hidden in the background image. In this research paper authors presented survey of camouflage detection methods for different applications and areas

Analysis of Pancreas Histological Images for Glucose Intolerance Identification using Wavelet Decomposition

Subtle structural differencescan be observed in the islets of Langer-hans region of microscopic image of pancreas cell of the rats having normal glucose tolerance and the rats having pre-diabetic(glucose intolerant)situa-tions. This paper proposes a way to automatically segment the islets of Langer-hans region fromthe histological image of rat's pancreas cell and on the basis of some morphological feature extracted from the segmented region the images are classified as normal and pre-diabetic.The experiment is done on a set of 134 images of which 56 are of normal type and the rests 78 are of pre-diabetictype. The work has two stages: primarily,segmentationof theregion of interest (roi)i.e. islets of Langerhansfrom the pancreatic cell and secondly, the extrac-tion of the morphological featuresfrom the region of interest for classification. Wavelet analysis and connected component analysis method have been used for automatic segmentationof the images. A few classifiers like OneRule, Naïve Bayes, MLP, J48 Tree, SVM etc.are used for evaluation among which MLP performed the best

Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification

Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform.  The multi-layered perceptron neural network is employed as an image texture classifier.  The proposed method tested on a high-resolution database of UMD texture. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for discrete wavelet transform features that lead to the best classification accuracy performance

A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Matrix Features for Stone Texture Classification

The human eye can easily identify the type of textures in flooring of the houses and in the digital images visually.  In this work, the stone textures are grouped into four categories. They are bricks, marble, granite and mosaic. A novel approach is developed for decreasing the dimension of stone image and for reducing the gray level range of the image without any loss of significant feature information. This model is named as “Decreased Dimension and Reduced Gray level Range Matrix (DDRGRM)” model. The DDRGRM model consists of 3 stages.  In stage 1, each 5×5 sub dimension of the stone image is reduced into 2×2 sub dimension without losing any important qualities, primitives, and any other local stuff.  In stage 2, the gray level of the image is reduced from 0-255 to 0-4 by using fuzzy concepts.  In stage 3, Co-occurrence Matrix (CM) features are derived from the DDRGRM model of the stone image for stone texture classification.  Based on the feature set values, a user defined algorithm is developed to classify the stone texture image into one of the 4 categories i.e. Marble, Brick, Granite and Mosaic. The proposed method is tested by using the K-Nearest Neighbor Classification algorithm with the derived texture features.  To prove the efficiency of the proposed method, it is tested on different stone texture image databases.  The proposed method resulted in high classification rate when compared with the other existing methods

Texture Segmentation Using Gabor Filters and Wavelets

The present work deals with image segmentation which results in the subdivision of an image into its constituent regions or objects. The result of image segmentation is a set of segments that collectively cover the entire image or a set of contours extracted from the image. Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity or texture. Specifically this project deals with texture segmentation of an image to find out the different types of textures present in the image. In this project different type of procedures have been followed to carry out texture segmentation. Procedures starting from fundamental filter transforms till multi-resolution technique using wavelet transform have been considered. Many texture-segmentation schemes are based on a filter-bank model, where the filters called Gabor filters are derived from Gabor elementary functions. Both linear and circular Gabor filters are studied and analyzed in this aspect and how these filters are better in comparison to linear filters is also analyzed. Different types of wavelet transform techniques like Haar transform, S transform, etc. are followed and their performance regarding texture segmentation is being studied