Depth estimation of inner wall defects by means of infrared thermography

There two common methods dealing with interpreting data from infrared thermography: qualitatively and quantitatively. On a certain condition, the first method would be sufficient, but for an accurate interpretation, one should undergo the second one. This report proposes a method to estimate the defect depth quantitatively at an inner wall of petrochemical furnace wall. Finite element method (FEM) is used to model multilayer walls and to simulate temperature distribution due to the existence of the defect. Five informative parameters are proposed for depth estimation purpose. These parameters are the maximum temperature over the defect area (Tmax-def), the average temperature at the right edge of the defect (Tavg-right), the average temperature at the left edge of the defect (Tavg-left), the average temperature at the top edge of the defect (Tavg-top), and the average temperature over the sound area (Tavg-so). Artificial Neural Network (ANN) was trained with these parameters for estimating the defect depth. Two ANN architectures, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were trained for various defect depths. ANNs were used to estimate the controlled and testing data. The result shows that 100% accuracy of depth estimation was achieved for the controlled data. For the testing data, the accuracy was above 90% for the MLP network and above 80% for the RBF network. The results showed that the proposed informative parameters are useful for the estimation of defect depth and it is also clear that ANN can be used for quantitative interpretation of thermography data

Adaptive thresholding in dynamic scene analysis for extraction of fine line

This paper presents an adaptive threshold method whereby a fine thin line of one-pixel width lines could be detected in a gray level images. The proposed method uses the percentage difference between the mean of the pixels within a window and the center pixel. The minimum threshold value however is heuristically set to 32. If the percentage difference is greater than 40% then the threshold value will be set to the difference value. This method has been applied in detecting moving objects with fine lines and the results showed that the method was able to pickup straight thin edges that belong to the moving objec

Fingerprint center point location using directional field

This paper presents a reliable fingerprint center point (CP) location algorithm for the alignment of fingerprints to construct a shift invariant fingerprint recognition system. The proposed algorithm is based on Alteration Tracking (AT) and CP estimation (CPE). AT is proposed to extract a track that records the transition from one quantized direction to another. CPE is aimed to find the bending point with highest transition of direction from the transition track. This algorithm is tested against fingerprints captured from SAGEM MSO100 optical scanner and the second database from University of Bologna. Experimental result shows that the proposed algorithm is capable of reliably locating fingerprint CP

Object's shadow removal with removal validation

We introduce in this paper, a shadow detection and removal method for moving objects especially for humans and vehicles. An effective method is presented for detecting and removing shadows from foreground figures. We assume that the foreground figures have been extracted from the input image by some background subtraction method. A figure may contain only one moving object with or without shadow. The homogeneity property of shadows is explored in a novel way for shadow detection and image division technique is used. The process is followed by filtering, removal, boundary removal and removal validation

A heuristic automatic and robust ROI detection method for medical image warermarking

This paper presents an automatic region of interest (ROI) segmentation method for application of watermarking in medical images. The advantage of using this scheme is that the proposed method is robust against different attacks such as median, Wiener, Gaussian, and sharpening filters. In other words, this technique can produce the same result for the ROI before and after these attacks. The proposed algorithm consists of three main parts; suggesting an automatic ROI detection system, evaluating the robustness of the proposed system against numerous attacks, and finally recommending an enhancement part to increase the strength of the composed system against different attacks. Results obtained from the proposed method demonstrated the promising performance of the method

Adaptive fuzzy switching noise reduction filter for iris pattern recognition

Noise reduction is a necessary procedure for the iris recognition systems. This paper proposes an adaptive fuzzy switching noise reduction (AFSNR) filter to reduce noise for iris pattern recognition. The proposed low complexity AFSNR filter removes noise pixels by fuzzy switching between an adaptive median filter and the filling method. The threshold values of AFSNR filter are calculated on the basis of the histogram statistics of eyelashes, pupils, eyelids, and light illumination. The experimental results on the CASIA V3.0 iris database, with genuine acceptance rate equals 99.72%, show the success of the proposed method

An image registration technique to enhance PCB inspection algorithms with real images

It is well known that real PCD image inspection based on referential approaches faces misalignment problems in detecting defects between a template image and a defective image. Hence, a reliable image registration technique is needed to align these two images perfectly. Hence, a registration technique which incorporates affine transformation and bi-cubic interpolation has been proposed. Experimental results have shown that this registration technique is suitable to be employed to obtain well-aligned defective images before detection algorithm takes place in PCD inspection

Partial purification of the molybdenum-reducing enzyme from Bacillus pumilus strain Lbna

Molybdenum is an emerging pollutant. Bioremediation of this heavy metal is possible by the mediation of Mo-reducing bacteria. These bacteria contain the Mo-reducing enzymes that can conver toxic soluble molybdenum into molybdenum blue; a less soluble and less toxic form of the metal. To date only the enzyme has been purified from only one bacterium. The aim of this study is to purify the Mo-reducing enzyme from a previously isolated Mo-reducing bacterium Bacillus pumilus strain Lbna using ammonium sulphate fractionation followed by ion exchange and then gel filtration. Two clear bands were obtained after the gel filtration step with molecular weights of 70 and 100 kDa. This indicates that further additional purification methods need to be used to get a purified fraction. Hence, additional steps of chromatography such as hydroxyapatite or chromatofocusing techniques can be applied in the future

Hemodynamic study on upper extremity: simulation on straight reverse saphenous vein graft

Artery reconstruction in upper extremities is rare performed compare to the incidence of reconstruction in lower extremities. In many cases, primary vascular repair was performed, whenever, otherwise, the interposition vein graft or venous bypass grafting were used in order to alleviate vascular occlusion. However, after grafting technique are applied, one or more of the digital arteries are blocked or severely narrowed because of mismatch of end-to-side or end-to-end reverse saphenous vein graft. The objective of this study was to understand the end-to-end blood flow influence on reverse saphenous vein graft with small diameter. The finite volume method was employed to model the 3-D blood flow pattern to determine the velocity, pressure gradient, flow, wall shear stress, flow resistance and longitudinal impedance (ZL). We expected that reverse saphenous vein graft behave hydraulically like provide straight graft. Furthermore longitudinal impedance modulus (ZL) is expected to be inverse proportional to small diameter