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Home > Taxonomic Studies of Fossil Even and Odd- Toed Mammals from the Miocene Rocks of Dhok Bun Ameer Khatoon, District Chakwal, Punjab, Pakistan

Taxonomic Studies of Fossil Even and Odd- Toed Mammals from the Miocene Rocks of Dhok Bun Ameer Khatoon, District Chakwal, Punjab, Pakistan

Thesis Info

Access Option

External Link

Author

Samiullah, Khizar

Program

PhD

Institute

Quaid-I-Azam University

City

Islamabad

Province

Islamabad.

Country

Pakistan

Thesis Completing Year

2010

Thesis Completion Status

Completed

Subject

Natural Sciences

Language

English

Link

http://prr.hec.gov.pk/jspui/bitstream/123456789/2448/1/2555S.pdf

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676727544228

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Fossil site Dhok Bun Ameer Khatoon (32o 47'' 26.4" N, 72° 55'' 35.7" E) yielded a significant amount of mammalian assemblage including four families of even-toed fossil mammal (Suidae, Tragulidae, Giraffidae, and Bovidae) and one family of odd-toed (Rhinocerotidae) of the Late Miocene. This newly discovered site has well exposed Chinji and Nagri formation and has dated approximately 14.2 Ma-9.5 Ma. This age agrees with the divergence of different mammalian genera and is important Palaeoecologically, Palaeogeographically and Palaeoclimatologically. Sedimentological evidence of the site supports that this is deposited in locustrine or fluvial environment, as Chinji formation is composed primarily of mud-stone while the Nagri formation is sand dominated. Palaeoenvironmental data indicates that Miocene climate of Pakistan was probably be monsoonal as there is now a days. Mostly the genera recovered from this site resemble with the overlying younger Dhok Pathan formation of the Siwaliks while the size variation in dentition is taxonomically important for vertebrate evolutionary point of view and this is the main reason to conduct this study at this specific site to add additional information in the field of Palaeontology. A detailed study of fossils even and odd-toed mammals found in Miocene rocks exposed at Dhok Bun Ameer Khatoon was carried out. Over all one hundred and twenty specimens were collected during field trips from which forty two specimens are well preserved and identified and are described in this thesis. Two specimens belonging to Gaindatherium browni, five specimens belonging to Listriodon pentapotamiae, four specimens belonging to Dorcatherium majus, one specimen belonging to Dorcatherium cf minus, two specimens belonging to Giraffa priscilla, twenty one specimens belonging to Giraffokeryx punjabiensis, three specimens belonging to Gazella sp. and three specimens belonging to Eotragus sp. Dhok Bun Ameer Khatoon is the new locality which is discovered in detail first time in Pakistan by the present author. The collection comprises isolated upper and lower teeth and fragments of maxillae and mandibular ramii.
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Computer Vision Based Pattern Matching and Displacement Detection During Fabric Printing

There is an absolute need for the robust quality inspection system, which can take on the new challenges and overcome the human limitations to inspect the fabric production consistently. To address this gap, computer vision based techniques are employed by enterprises. Some of the beneficent manufacturers include ceramic, circuit board printer, paper printer and textiles. Excellent work has been done in the yarn and weaving production sections of textile industry, however fabric printing monitoring is being explored by the researchers. The referential approach is mostly adopted to monitor fabric printing for defect detection. It works by acquiring an error-free image and registering this image with subsequent images captured during the production. In this method, the major time-consuming problem is to find the design repeat in the sample image before further processing. The researchers suggested some methods like distance matching in RGB space, photo encoders, genetic algorithm along with recursive splitting, Fuzzy C-means clustering, and cross correlation algorithm. There are certain limitations with these methods, for instance, sensitive to small deformation, required extra hardware prone to mechanical fault and it is expensive in calculations. In this research, a method for finding the design repeat is proposed. The method is tuned up according to the textile printing domain. Since the fabric moves in one direction during printing process, therefore the image registration can be confined to the same direction. To further speed up the process, the image registration method is applied using few initial pixel columns of the reference image with the sample image. Such bunch of selected columns of the reference image is matched with the same number of columns selected from sample image by moving this bunch on the sample image column by column. The maximum matching position is marked as the start of the design repeat. As the repeat size is always fixed, therefore complete design can be extracted from acquired image for defect detection. The experimental results on different fabric designs using the above-mentioned method are promising. Moreover, this technique improves the image alignment speed which enhances defect detection system performance. The most common defect is the displacement or misregistration of a colour. This defect is caused by the misalignment of cylindrical screens of fabric printing machine. As every screen is responsible for a colour, the misaligned screen registers the colour on the incorrect position. It leads to defective production and contributes to a substantial loss of the material and time. Few researchers proposed the feature based solution which are either computationally expensive or do not provide detail information about the defect like location and colour. Further, some techniques depend on the additional algorithms to find defects. Mostly, these methods depend on the accuracy of reference and sample image alignment. So there is always a chance of wrong notification. An improved algorithm for the detection of displacement of a colour during fabric printing is proposed in this thesis. The algorithm concatenates red, green and blue pixel values of the RGB image to represent a colour and later produces a frequency distribution of different colours for both sample and reference images. The colours with low frequencies are removed considering noise. In the next step, colours are grouped depending upon their distances from each other. These colour groups are later used to detect any new colour in the sample image which is actually created by the displacement defect and colour variation. The proposed algorithm successfully detects displacement and colour variation defects when it is tested using different flawed printed fabric images. The result shows that the proposed method has almost same accuracy as stat of the art algorithm and more helpful to detect the colour displacement, and it can also overcome the shortcoming of repeat finding process.