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In medical world, Glaucoma is a vision threatening retinal disease. This leads to the research scope of detecting glaucoma at early stages for better treatments. Manual approach for detection by ophthalmologists is tiresome and time-consuming, hence an image processing approach of determining the ratio of optic cup with respect to disc (CDR) is focused. This research work proposes a neutrosophic approach for segmentation by transforming the input retinal images to neutrosophic domain. In this neutrosophic approach, the intensity values are represented as truth, indeterminacy and falsity membership. Next, an intensification operation on the resultant images, followed by score function is done to result in a comprehensive outcome. Finally, the clustering technique IFCM is used for segmentation of the optic cup boundary and the optic disc. Experimental result analysis shows that the effectiveness of the proposed methodology is demonstrated since it provides unbiased results of average accuracy of 92.38% on DRISHTI-GS dataset and 91.67% on RIM-ONE dataset. [ABSTRACT FROM AUTHOR]

Underwater acoustic images are acquired using sonar instrument that uses sound propagation to navigate and map the sea floor. The sonar devices are effectively used to create images of large area of the seabed. However, the visual perception of the object in the acoustic image depends on refraction, which is a function of changes in the speed of sound in successive layers of water. And refraction depends mainly on temperature, slightly on salinity and hydrostatic pressure. The quality and resolution of sonar imaging of the bottom depends on many other factors such as pitch, yaw and heave of the side scan sonar, the presence of volume scatterers in the water body, the distance of the sonar from the bottom and orientation of the object. Generally, the objects in an acoustic image would be of small size compared to their normal size as the distance between the sonar and object is larger. To detect and recognize the objects in the images, the resolution should be enhanced. In this paper, we propose an efficient edge preserving interpolation method for underwater acoustic image resolution enhancement which preserves the edge sharpness. The method handles the diagonal pixels in the first pass, in turn fills the horizontal and vertical pixels in the second pass. The results obtained are compared with the state-of-the-art interpolation techniques and the performance measures such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measurement (SSIM) shows an improved result. [ABSTRACT FROM AUTHOR]

The images involved in the content-based image retrieval (CBIR) applications are collectively represented by features such as color, texture and shape. The precision of the CBIR application relies on the key features used in image representation and its similarity measure. In CBIR, dominant color feature extraction is affected by the predefined intervals used in color quantization. The proposed work mainly concentrates on extracting the dominant color information of the image using the clustering process. The clustering process is initiated by the proposed seed point's selection approach. This approach derives the number of seed points using the first order statistical measure and maximum range of the distributed pixel values. Moreover, this work gives equal priority to dominant color and its occurrence information in calculating the similarity between query and database images. Finally, the standard databases such as SIMPLIcity, Corel-10k, OT-scene, Oxford flower and GHIM are taken to investigate the performance of the proposed dominant color based image retrieval application. [ABSTRACT FROM AUTHOR]

To get the result if satellite image classification in precisely the image fusion method is widely used. The satellite images at different spectral and spatial resolutions with the guide of image handling strategies can improve the idea of data. For the most part image fusion is important to expel the spatial data from two images of different spatial, spectral and worldly images of a comparable zone. A task of image examination, for instance, image order on fused images gives better results in contrast with one of a kind data. In this work versatile unsupervised vector zone (VUVZ) method have been used for image fusion. The resultant images have been classified using the supervised classification with Intensive Pragmatic Blossoms (IPB) rule for information extraction and comparison between them regarding their accuracy. In This work describes a case study of micro seepage signals detection in the land, using multi-sensor satellite time series based on linear iterative segmentation (LIS) and Intensive Pragmatic Blossoms (IPB). Results show that the spectral anomalies identified from a satellite are closely correlated to the known oilfields and that the micro seepage maps can produce new high-quality data to reduce exploration risk. The simulation work classification accuracy of 94.12% which is much better than past outcomes in this engaged field of research. The execution of the usage is analyzed, an examination is likewise influenced concerning the clustering precision, time complexity, and false proportion are introduced. [ABSTRACT FROM AUTHOR]