NULL

NULL

NULL

Komombo Basin is located in Upper Egypt about 570 km southeast of Cairo; it is an asymmetrical half graben and the first oil producing basin in Upper Egypt. The Six Hills Formation is of Early Cretaceous age and subdivided into seven members from base to top (A–G); meanwhile the B member is of Hauterivian–Early Barremian and it is the only source rock of Komombo Basin. Therefore, a detailed study of the SR should be carried out, which includes the determination of the main structural elements, thickness, facies distribution and characterization of the B member SR which has not been conducted previously in the study area. Twenty 2D seismic lines were interpreted with three vertical seismic profiles (VSP) to construct the depth structure-tectonic map on the top of the B member and to highlight the major structural elements. The interpretation of depth structure contour map shows two main fault trends directed towards the NW-SE and NE to ENE directions. The NW-SE trend is the dominant one, creating a major half-graben system. Also the depth values range from −8400 ft at the depocenter in the eastern part to −4800 ft at the shoulder of the basin in the northwestern part of the study area. Meanwhile the Isopach contour map of the B member shows a variable thickness ranging between 300 ft to 750 ft. The facies model shows that the B member SR is composed mainly of shale with some sandstone streaks.

The B member rock samples were collected from Al Baraka-1 and Al Baraka SE-1 in the eastern part of Komombo Basin. The results indicate that the organic matter content (TOC) has mainly good to very good (1–3.36 wt %), The B member samples have HI values in the range 157–365 (mg HC/g TOC) and dominated by Type II/III kerogen, and is thus considered to be oil-gas prone based on Rock-Eval pyrolysis, Tmax values between 442° and 456° C therefore interpreted to be mature for hydrocarbon generation. Based on the measured vitrinite equivalent reflectance values, the B member SR samples have a range 0.7–1.14%Ro, in the oil generation window.

Komombo Basin is located in Upper Egypt about 570 km southeast of Cairo; it is an asymmetrical half graben and the first oil producing basin in Upper Egypt. The Six Hills Formation is of Early Cretaceous age and subdivided into seven members from base to top (A–G); meanwhile the B member is of Hauterivian–Early Barremian and it is the only source rock of Komombo Basin. Therefore, a detailed study of the SR should be carried out, which includes the determination of the main structural elements, thickness, facies distribution and characterization of the B member SR which has not been conducted previously in the study area. Twenty 2D seismic lines were interpreted with three vertical seismic profiles (VSP) to construct the depth structure-tectonic map on the top of the B member and to highlight the major structural elements. The interpretation of depth structure contour map shows two main fault trends directed towards the NW-SE and NE to ENE directions. The NW-SE trend is the dominant one, creating a major half-graben system. Also the depth values range from −8400 ft at the depocenter in the eastern part to −4800 ft at the shoulder of the basin in the northwestern part of the study area. Meanwhile the Isopach contour map of the B member shows a variable thickness ranging between 300 ft to 750 ft. The facies model shows that the B member SR is composed mainly of shale with some sandstone streaks.

The B member rock samples were collected from Al Baraka-1 and Al Baraka SE-1 in the eastern part of Komombo Basin. The results indicate that the organic matter content (TOC) has mainly good to very good (1–3.36 wt %), The B member samples have HI values in the range 157–365 (mg HC/g TOC) and dominated by Type II/III kerogen, and is thus considered to be oil-gas prone based on Rock-Eval pyrolysis, Tmax values between 442° and 456° C therefore interpreted to be mature for hydrocarbon generation. Based on the measured vitrinite equivalent reflectance values, the B member SR samples have a range 0.7–1.14%Ro, in the oil generation window.

Text detection from scene images is a challenging topics because of low resolution, complex background and font/font size variations. In this paper, we design a method to detect text based on Naïve Bayes classifier and connected component analysis. We used Naïve Bayes classifier to convert original gray level image into binary image, then connected component analysis is used to identify candidate text regions. In the last step we use empirical rules to determine threshold which used to discard non-text regions and keep the text regions. The proposed method compares between three classifiers outcome; the first is based on Otsu method, the second classifier outcome is derived using Naïve Bayes classifier based on mean feature and standard deviation feature, we named this method Bayes_Two_Features or shortly Bayes2. The last classifier outcome is derived using Naïve Bayes classifier based on just the mean feature, we named this method Bayes_Single_Feature or shortly Bayes1. Otsu’s method is used to convert grayscale image to binary image by assuming that image contains two classes; foreground and background.
Experimental results show that Bayes2 classifier outperforms the other two methods, in the case of big letters especially when these letters are in non-horizontal and skewed form.

Text detection from scene images is a challenging topics because of low resolution, complex background and font/font size variations. In this paper, we design a method to detect text based on Naïve Bayes classifier and connected component analysis. We used Naïve Bayes classifier to convert original gray level image into binary image, then connected component analysis is used to identify candidate text regions. In the last step we use empirical rules to determine threshold which used to discard non-text regions and keep the text regions. The proposed method compares between three classifiers outcome; the first is based on Otsu method, the second classifier outcome is derived using Naïve Bayes classifier based on mean feature and standard deviation feature, we named this method Bayes_Two_Features or shortly Bayes2. The last classifier outcome is derived using Naïve Bayes classifier based on just the mean feature, we named this method Bayes_Single_Feature or shortly Bayes1. Otsu’s method is used to convert grayscale image to binary image by assuming that image contains two classes; foreground and background.
Experimental results show that Bayes2 classifier outperforms the other two methods, in the case of big letters especially when these letters are in non-horizontal and skewed form.

Text detection from scene images is a challenging topics because of low resolution, complex background and font/font size variations. In this paper, we design a method to detect text based on Naïve Bayes classifier and connected component analysis. We used Naïve Bayes classifier to convert original gray level image into binary image, then connected component analysis is used to identify candidate text regions. In the last step we use empirical rules to determine threshold which used to discard non-text regions and keep the text regions. The proposed method compares between three classifiers outcome; the first is based on Otsu method, the second classifier outcome is derived using Naïve Bayes classifier based on mean feature and standard deviation feature, we named this method Bayes_Two_Features or shortly Bayes2. The last classifier outcome is derived using Naïve Bayes classifier based on just the mean feature, we named this method Bayes_Single_Feature or shortly Bayes1. Otsu’s method is used to convert grayscale image to binary image by assuming that image contains two classes; foreground and background.
Experimental results show that Bayes2 classifier outperforms the other two methods, in the case of big letters especially when these letters are in non-horizontal and skewed form.

Text extraction from scene images is a defy subject in light of low resolution, complex background and textual style/text size varieties. In this paper, we design a scheme to detect text based on shape features like Euler Number, a number of pixels for each region which candidate to be a character and vertical distances as a geometric feature between these regions. We divide these features into base features to collect the text regions, and the other features as a filter to discard the non-text regions. We use some threshold with the features either those to extract text regions or to discard non-text regions. The proposed method outperforms some existed method through the basis metric.

Text extraction from scene images is a defy subject in light of low resolution, complex background and textual style/text size varieties. In this paper, we design a scheme to detect text based on shape features like Euler Number, a number of pixels for each region which candidate to be a character and vertical distances as a geometric feature between these regions. We divide these features into base features to collect the text regions, and the other features as a filter to discard the non-text regions. We use some threshold with the features either those to extract text regions or to discard non-text regions. The proposed method outperforms some existed method through the basis metric.