Image segmentation is an important step in several computer vision applications. The segmentation of images into homogeneous and meaningful regions is a fundamental technique for image analysis. Textures occupy a vital role in a wide range of computer vision research fields; from microscopic images to images sent down to earth by satellites, from the analysis of multi-spectral scan images to outdoor scenes, all consist of texture. Although several methods have been proposed, less work has been done in developing suitable techniques for segmentation of texture images. After a careful and in-depth survey on wavelet transforms, the present study found that efficient numerical solutions in the signal processing applications can be found using Stationary Wavelet Transform (SWT). SWT is redundant, linear and shift invariant, that’s why it gives a better approximation than the DWT. In this paper a novel texture segmentation method based on “SWT and Textural Properties” is proposed. Multi scale SWT with Textural Properties and morphological treatment is used in the present study to detect fine edges from texture images for a fine segmentation.

The present paper presents a new age classification method by integrating the features derived from Grey Level Co-occurrence Matrix (GLCM) with a new structural approach derived from four distinct LBP's (4-DLBP) on a 3 x 3 image. The present paper derived four distinct patterns called Left Diagonal (LD), Right diagonal (RD), vertical centre (VC) and horizontal centre (HC) LBP's. For all the LBP's the central pixel value of the 3 x 3 neighbourhood is significant. That is the reason in the present research LBP values are evaluated by comparing all 9 pixels of the 3 x 3 neighbourhood with the average value of the neighbourhood. The four distinct LBP's are grouped into two distinct LBP's. Based on these two distinct LBP's GLCM is computed and features are evaluated to classify the human age into four age groups i.e: Child (0-15), Young adult (16-30), Middle aged adult (31-50) and senior adult (>50). The co-occurrence features extracted from the 4-DLBP provides complete texture information about an image which is useful for classification. The proposed 4-DLBP reduces the size of the LBP from 6561 to 79 in the case of original texture spectrum and 2020 to 79 in the case of Fuzzy Texture approach.

Automatic recognition of facial expressions can be an important component of natural human-machine interfaces; it may also be used in behavioural science and in clinical practice. Although humans recognise facial expressions virtually without effort or delay, reliable expression recognition by machine is still a challenge. This paper, presents recognition of facial expression by integrating the features derived from Grey Level Co-occurrence Matrix (GLCM) with a new structural approach derived from distinct LBP’s (DLBP) ona 3 x 3 First order Compressed Image (FCI). The proposed method precisely recognizes the 7 categories of expressions i.e.: neutral, happiness, sadness, surprise, anger, disgust and fear. The proposed method contains three phases. In the first phase each 5 x 5 sub image is compressed into a 3 x 3 sub image. The second phase derives two distinct LBP’s (DLBP) using the Triangular patterns between the upper and lower parts of the 3 x 3 sub image. In the third phase GLCM is constructed based on the DLBP’s and feature parameters are evaluated for precise facial expression recognition. The derived DLBP is effective because it integrated with GLCM and provides better classification performance. The proposed method overcomes the disadvantages of statistical and formal LBP methods in estimating the facial expressions. The experimental results demonstrate the effectiveness of the proposed method on facial expression recognition.

The present paper proposes an innovative technique that classifies human age group in to five categories i.e 0 to 12, 13 to 25, 26 to 45, 46 to 60, and above 60 based on the Topological Texture Features (TTF) of the facial skin.  Most of the existing age classification problems in the literature usually derive various facial features on entire image and with large range of gray level values in order to achieve efficient and precise classification and recognition. This leads to lot of complexity in evaluating feature parameters. To address this, the present paper derives TTF’s on Second Order image Compressed and Fuzzy Reduced Grey level (SICFRG) model, which reduces the image dimension from 5 x 5 into 2 x 2 and grey level range without any loss of significant feature information. The present paper assumes that bone structural changes do not occur after the person is fully grown that is the geometric relationships of primary features do not vary. That is the reason secondary features i.e TTF’s are identified and exploited. In the literature few researchers worked on TTF for classification of age, but so far no research is implemented on reduced dimensionality model.  The proposed Second order Image Compressed and Fuzzy Reduced Grey level (SICFRG) model reduces overall complexity in recognizing and finding histogram of the TTF on the facial skin.  The experimental evidence on FG-NET aging database and Google Images clearly indicates the high classification rate of the proposed method.

The present paper provides a new mechanism with two stages for efficient authentication based on Honey Comb Polynomial Interpolation (HCPI) and Morphological Border Sorted Pixel Value Difference (MBSPVD) scheme. A simple polynomial interpolation technique on new hexagonal structure called Honey Comb structure (HCS) is used for generating the key of the digital watermark. The polynomial interpolation gives a high secured key, which is difficult to break. HCS is used in the present paper to select pixel positions for generating the Digital Watermark key (DWK). The significant factor of the present method is, the digital watermark is generated by using DWK. The importance of HCS representation is that it possesses special computational features that are pertinent to the vision process. The HCS has features of higher degree of circular symmetry, uniform connectivity, greater angular resolution, and which leads to reduce storage and computation in image processing operations. The DWK is placed in the image by using MBSPVD method. Its guarantees high authentication, robustness, security and copyright protection. The Lagrange Polynomial interpolation (LPI) is used for retrieving the digital watermark from the DWK. The LPI accomplish the aim of image authentication and protection without reducing the image quality. The proposed HCPI-MBSPVD is tested with various attacks and compared with various existing image authentication and copyright protection methods. The comparisons and results indicate the efficacy of the proposed method.

Digital images make up a large component in the multimedia information. Hence Image authentication has attained a great importance and lead to the development of several image authentication algorithms. This paper proposes a block based watermarking scheme for image authentication based on the edge information extracted from each block. A signature is calculated from each edge block of the image using simple hash function and inserted in the same block. The proposed local edge based content hash (LECH) scheme extracts the original image without any distortion from the marked image after the hidden data have been extracted. It can also detect and localize tampered areas of the watermarked image. Experimental results demonstrate the validity of the proposed scheme.

Texture Analysis plays an important role in the interpretation, understanding and recognition of terrain, biomedical or microscopic images. To achieve high accuracy in classification the present paper proposes a new method on textons. Each texture analysis method depends upon how the selected texture features characterizes image. Whenever a new texture feature is derived it is tested whether it precisely classifies the textures. Here not only the texture features are important but also the way in which they are applied is also important and significant for a crucial, precise and accurate texture classification and analysis. The present paper proposes a new method on textons, for an efficient rotationally invariant texture classification. The proposed Texton Features (TF) evaluates the relationship between the values of neighboring pixels. The proposed classification algorithm evaluates the histogram based techniques on TF for a precise classification. The experimental results on various stone textures indicate the efficacy of the proposed method when compared to other methods.

Classification and recognition of objects is interest of many researchers. Shape is a significant feature of objects and it plays a crucial role in image classification and recognition. The present paper assumes that the features that drastically affect the adulthood classification system are the Shape features (SF) of face. Based on this, the present paper proposes a new technique of adulthood classification by extracting feature parameters of face on Integrated Texton based LBP (IT-LBP) images. The present paper evaluates LBP features on facial images. On LBP Texton Images complex shape features are evaluated on facial images for a precise age classification.LBP is a local texture operator with low computational complexity and low sensitivity to changes in illumination. Textons are considered as texture shape primitives which are located with certain placement rules. The proposed shape features represent emergent patterns showing a common property all over the image. The experimental evidence on FGnet aging database clearly indicates the significance and accuracy of the proposed classification method over the other existing methods.