Gradient Feature Extract for the Quantification of Complex Defects Using Topographic Primal Sketch in Magnetic Flux Leakage

Defect quantification is a very important aspect in nondestructive testing (NDT) as it helps in the analysis and prediction of a structure's integrity and lifespan. In this paper, we propose a gradient feature extraction for the quantification of complex defect using topographic primal sketch (TPS) in magnetic flux leakage (MFL) testing. This method uses four excitation patterns so as to obtain MFL images from experiment; a mean image is then produced, assuming it has 80–90% the properties of all four images. A gradient manipulation is then performed on the mean image using a novel least-squares minimization (LSM) approach, for which, pixels with large gradient values (considered as possible defect pixels) are extracted. These pixels are then mapped so as to get the actual defect geometry/shape within the sample. This map is now traced using a TPS for a precise quantification. Results have shown the ability of the method to extract and quantify defects with high precision given its perimeter, area, and depth. This significantly eliminates errors associated with output analysis as results can be clearly seen, interpreted, and understood.

Classification and Change Detection of Tirupati Urban Area using Erosion and Dilation Based PCA Transform

In remote sensing, the identification of the land use and land cover (LULC) changes in the global and local region are developed by classification and detection algorithms. This classification system can be developed to meet the needs of state agencies, and Federal for an up-to-date analyze of LULC throughout the entire selected of region area. The multispectral images have multiple low-resolution bands due to lack of sensory acquisition problem, haze-covered on earth objects and atmospheric distributions. So difficult to analyze the full information, the user wrongly interprets the information. Image processing applications can be done for compress and enhance the details of land surface details. The Principal Component Analysis and Morphological operations are implemented for compressing and feature extract the color and earth object values with good accuracy level. Change Detection between the time difference of the proposed enhanced images for land objects classes was computed. The most extensive land cover change category identification of the Tirupati urban Agricultural and forest area for the last 14 years. The change analyzed by using the image differencemethod for obtaining the changing level of the forest and urban development areas between two-timeintervals.

A Novel Convolutional Neural Network Based Localization System for Monocular Images

The authors present a robust and extendable localization system for monocular images. To have both robustness toward noise factors and extendibility to unfamiliar scenes simultaneously, our system combines traditional content-based image retrieval structure with CNN feature extraction model to localize monocular images. The core model of the system is a deep CNN feature extraction model. The feature extraction model can map an image to a d-dimension space where image pairs in the real word have smaller Euclidean distances. The feature extraction model is achieved using a deep Convnet modified from GoogLeNet. A special way to train the feature extraction model is proposed in the article using localization results from Cambridge Landmarks dataset. Through experiments, it is shown that the system is robust to noise factors supported by high level CNN features. Furthermore, the authors show that the system has a powerful extendibility to other unfamiliar scenes supported by a feature extract model's generic property and structure.

Improved Sparse Representation Super-Resolution algorithm for Remote Sensing Image

In order to obtain higher quality super-resolution reconstruction (SRR) of remote sensing images, an improved sparse representation remote sensing images SRR method is proposed in this paper. First, low-resolution image is processed by improved feature extract operator. The high-resolution image and low-resolution image blocks have the same sparse representation coefficient, so the SRR image with higher spatial resolution can be derived from the sparse representation coefficients which have been obtained from low-resolution image. The improved feature extraction operator is a method to get more detail and texture information from the training images. Experiment results show that more texture details can be obtained in the result of SRR remote sensing images subjectively. At the same time, the objective evaluation parameters are improved greatly. The peak PSNR is increased about 2.50dB and 0.50 dB, RMSE is decreased about 2.80 and 0.3 compared with bicubic interpolation algorithm and Ref[8] algorithm respectively.