This paper explains the task of land cover classification using reformed fuzzy C means. Clustering is the assignment of objects into groups called clusters so that objects from the same cluster are more similar to each other than objects from different clusters. The most basic attribute for clustering of an image is its luminance amplitude for a monochrome image and colour components for a colour image. Since there are more than 16 million colours available in any given image and it is difficult to analyse the image on all of its colours, the likely colours are grouped together by clustering techniques. For that purpose reformed fuzzy C means algorithm has been used. The segmented images are compared using image quality metrics. The image quality metrics used are peak signal to noise ratio (PSNR), error image and compression ratio. The time taken for image segmentation is also used as a comparison parameter. The techniques have been applied to classify the land cover.

Time of Flight Diffraction Technique is one of the NDE methods, used in weld inspection to identify the weld defects. The classification of defects using the TOFD technique depends on the knowledge and experience of the operator. The classification reliability of defects detected by this technique can be improved by applying the Artificial Neural Network. In this work, four austenitic stainless steel weldments with defects viz, Lack of Fusion, Lack of Penetration, Slag, Porosity and one with out any Defect were fabricated. TOFD experiment is conducted on these weldments. Discrete wavelet transform based denoising methods were applied to denoise the resultant A scan signals. Time scale features are extracted from the denoised signals. A multi layer feed forward network with Resilient Back Propagation algorithm has been applied for classification of the signals. The number of hidden layers in the network are increased from 0 to 6. Various performance functions are also employed to achieve a better classification efficiency. The results are promising to proceed the automatic defect classification by TOFD technique.

Infrared Thermography (IRT) is the widely used Non-Destructive Testing (NDT) technique for health monitoring of buildings. IRT uses an IR camera that captures the temperature variations and maps it into thermographs. Under normal conditions, temperature distribution is uniform. On the other hand an abnormality appears either as hot or cold spot. Hence interpretation of these thermographs provides information about the abnormality. Various image segmentation techniques are cited in literature for abnormality detection in visible images. However an efficient domain specific segmentation algorithm for thermographs is yet to be evolved. Local Intensities Operation (LIO) based segmentation is already proposed for thermal image segmentation. However, it has failed miserably for low contrast thermographs. Hence an enhanced approach is proposed in this paper that performs contrast stretching based LIO for segmentation. From subjective analysis, it is found that the proposed technique provides better results even for low contrast thermal images.

This paper explains the task of segmenting any given colour image using competitive neural network. Image segmentation refers to the division pixels into homogeneous classes or clusters so that items in the same class are as similar as possible and items in different classes are as dissimilar as possible. . The most basic attribute for segmentation is image luminance amplitude for a monochrome image and color components for a color image. Since there are more than 16 million colours available in any given image and it is difficult to analyse the image on all of its colours, the likely colours are grouped together by image segmentation. For that purpose competitive neural network has been used. Competitive Neural Networks are groups of neurons compete for the right to become active. The activation of the node with the largest net is set equal to 1, and the remaining nodes are set equal to 0. It works on the principle of "Winner Takes All". First, the color image of interest is read as a three dimensional matrix. It is then converted into a two-dimensional matrix. Weight matrix is randomly initialized. Competitive neural network is then created. Then the neural network is trained using the two-dimensional image matrix. This weight matrix is reconstructed to form the segmented image. Quality of the reconstructed image is determined by calculating the Peak Signal to Noise Ratio and found to be reasonable. This work finds vast applications in medical imaging, satellite imaging, military applications and non destructive testing of products in industries.