Iris segmentation using a non-decimated wavelet transform

This paper presents an iris segmentation algorithm. The proposed technique applies a histogram based method on the input eye image extracting a point within the pupil. The image is then intensity sampled over M equiangular radial scan line, generating M 1-dimensional signals. A Fuzzy multi-scale edge detection algorithm is then applied to each of the resulting radii signals, to accurately detect and locate one positive edge point from the signal. A uniform cubic B-spline approximation method is further applied to the detected edges determining the iris outer boundary. The histogram of the area within the extracted outer iris bondary of the eye image is finaly used to extract the pupil outer bondary. Experimental results on a number of eye test images taken under visible wavelenght from UBIRISv.1 and UBIRISv.2 databases show that the proposed segmentation method accurately extracts the iris boundaries

Digital Rock Reconstruction And Property Calculation Of Fractured Shale Rock Samples

As the preferential flow channels in the shale reservoir, the fracture systems including the natural micro-cracks and hydraulic fractures have received great attention from the whole energy industry worldwide. However, it is challenging to quantify the fracture systems in the shale rocks precisely because most of well-developed “histogram-based” image processing techniques cannot handle the case of small target segmentation. Because the fracture apertures are very thin, the over-segmentation or insufficient segmentation would lead to significant error in the quantification, including the fracture porosity, aperture, length, tortuosity etc., which would lead to serious mistakes to the property calculation. In this research, two novel image processing methods are proposed. The self-adaptive image enhancement method employs incomplete beta function and simulated annealing algorithm to modify the grayscale intensity histogram. The contrast between the target and the background of the transformed gray image reaches the maximum. Also, “self-adaptive” means the enhancement process is specified by the input images. The comparison of segmentation results before and after the image enhancement show that the target becomes more obvious to the naked eyes and the precise fracture porosity of the test image is 4.02 %. The multi-stage image segmentation (MSS) method combines the global and local information of the image to finish the segmentation. The generated three-dimensional model provides visualization of the fracture systems existing in the core. Also, the important parameters of the fractures can be obtained, including aperture, length, tortuosity, and porosity. Compared with the real permeability from the core-flooding experiments, the permeability calculated from the MSS method has the minimum error of 22.1 %. The results show that the proposed methods in this research can be effective tools for the precise quantification of the thin fracture systems

Quantification of sub-resolution porosity in carbonate rocks by applying high-salinity contrast brine using X-ray microtomography differential imaging

Characterisation of the pore space in carbonate reservoirs and aquifers is of utmost importance in a number of applications such as enhanced oil recovery, geological carbon storage and contaminant transport. We present a new experimental methodology that uses high-salinity contrast brine and differential imaging acquired by X-ray tomography to non-invasively obtain three-dimensional spatially resolved information on porosity and connectivity of two rock samples, Portland and Estaillades limestones, including sub-resolution micro-porosity. We demonstrate that by injecting 30 wt% KI brine solution, a sufficiently high phase contrast can be achieved allowing accurate three-phase segmentation based on differential imaging. This results in spatially resolved maps of the solid grain phase, sub-resolution micro-pores within the grains, and macro-pores. The total porosity values from the three-phase segmentation for two carbonate rock samples are shown to be in good agreement with Helium porosity measurements. Furthermore, our flow-based method allows for an accurate estimate of pore connectivity and a distribution of porosity within the sub-resolution pores

Query by String word spotting based on character bi-gram indexing

In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC). These attribute models are learned using linear SVMs over the Fisher Vector representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi- gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasetsComment: To be published in ICDAR201