a. javaheri

Cadastral boundary detection deals with locating the boundary of the ownership and use of land. Recently, there has been high demand for accelerating and improving the automatic detection of cadastral mapping. As this problem is in its starting point, there are few researches using deep learning algorithms. In this paper, we develop an algorithm with a Mask R-CNN core followed with geometric post-processing methods that improve the quality of the output. Many researches use classification or semantic segmentation but our algorithm employs instance segmentation. Our algorithm includes two parts, each of which consists of a few phases. In the first part, we use Mask R-CNN with the backbone of a pre-trained ResNet-50 on the ImageNet dataset. In the second part, we apply three geometric post-processing methods to the output of the first part to get better overall output. Here, we also use computational geometry to introduce a new method for simplifying lines which we call pocket-based simplification algorithm. We used 3 google map images with sizes 4963 × 2819, 3999 × 3999, and 5520 × 3776 pixels. And divide them to overlapping and non-overlapping 400×400 patches used for training the algorithm. Then we tested it on a google map image from Famenin region in Iran. To evaluate the performance of our algorithm, we use popular metrics Recall, Precision, and F-score. The highest Recall is 95%, which also maintains a high precision of 72%. This results in an F-score of 82%. The idea of semantic segmentation to derive boundary of regions, is new. We used Mask R-CNN as the core of our algorithm, that is known as a very suitable tools for semantic segmentation. Our algorithm performs geometric post-process improves the f-score by almost 10 percent. The scores for a region in Iran containing many small farms is very good.

Marek´s disease (MD) is a viral disease caused by the penetration and proliferation of lymphoid cells in parietal nerves and some other organs of birds and in fact as is considered, one of the ordinary and important infectious diseases in poultry. MD viruses cause immunosuppression and predispose poultry to other infectious diseases. The purpose of this study was diagnosis of Marek's disease virus as one of the causative agents for visceral tumors in chickens using PCR. In order to perform this study, tumoral tissue sampling was collected from slaughter houses of Chaharmahal & Bakhtiari province. After DNA extraction from samples, Polymerase Chain Reaction (PCR) was performed using specific primers detecting 132bp tandem repeat and antigen Agene of MDV. Finally electrophoresis of PCR products was done on 1.5% agarose gel. The results showed that 62.86% of the total 280 nodul skin samples, tested for the presence of A genes of mark´s disease, were positive. In 40% of samples, 132 bp tandem repeat was also indentified. Since all the samples were tested have clinical symptoms of Marek´s disease, but the diagnosis of 62.86 percent infection based on PCR, it seems to be that other factors, except the MDV, have a role in tumor lesions in poultry.