Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology

An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of 0.094 s in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of 0.113 s . The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of 140 × 260 pixels could achieve high-precision vision navigation while the course deviation angle was not more than 7.5 ° . The maximum tractor speed of the optimal template and global template were 51.41 km / h and 27.47 km / h , respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect.

Navigation Algorithm Based on the Boundary Line of New and Old Soil Combined Using Guided Filtering and Improved Anti-noise Morphology

An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first the two key steps, Guided Filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and it’s application condition were studied for improving the image processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of 0.094 s in comparison with HSV, HIS and 2R-G-B color spaces. The Guided Filtering method can effectively distinguish the boundary between the new and old soil than other competing vanilla methods such as Tarel, Multi-scale Retinex, Wavelet-based Retinex and Homomorphic Filtering inspite of having the fastest processing speed of 0.113 s. The extracted soil boundary line of the improved anti-noise morphology algorithm has best precision and speed compared with other operators such as Sobel, Roberts, Prewitt and Log. After comparing different size of image template, the optimal template with the size of 140×260 pixels can meet high precision vision navigation while the course deviation angle is not more than 7.5°. The maximum tractor speed of the optimal template and global template are 51.41 km/h and 27.47 km/h respectively which can meet real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the new and old soil boundary line extracted by the proposed improved anti-noise morphology algorithm which has broad application prospect.

Navigation Algorithm Based on the Boundary Line of New & Old Soil Using Improved Anti-Noise Morphology

An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. Firstly, the two key steps, Guided Filtering and improved anti-noise morphology navigation line extraction, were addressed in detail. Then the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption, 0.094 s, compared with HSV, HIS and 2R-G-B color spaces. The Guided Filtering method can enhance the new & old soil boundary effectively than any other methods such as Tarel, Multi-scale Retinex, Wavelet-based Retinex and Homomorphic Filtering, meanwhile, has the fastest processing speed of 0.113 s. The extracted soil boundary line of the improved anti-noise morphology algorithm has best precision and speed compared with other operators such as Sobel, Roberts, Prewitt and Log. After comparing different size of image template, the optimal template with the size of 140×260 pixels can meet high precision vision navigation while the course deviation angle is not more than 7.5°. The maximum tractor speed of the optimal template and global template are 51.41 km/h and 27.47 km/h respectively which can meet real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the new & old soil boundary line extracted by the proposed improved anti-noise morphology algorithm which has broad application prospect.

Antitumor activity of indenoisoquinoline inhibitors of topoisomerase 1 (TOP1) via apoptosis and autophagocytosis pathways in animal models.

11588 Background: We performed pharmacodynamic biomarker analysis for response to a panel of three indenoisoquinolines (LMP776, LMP400 and NSC706744, J. Med. Chem. 49:7740, 2006) that have demonstrated anti-tumor activity in dogs. In preclinical xenograft models treated with indenoisoquinolines, we observed that gH2AX was not a useful biomarker for the biological activity of compound 706744, but was a reasonable biomarker of drug activity (Clin. Canc. Res. 16:5447, 2010) for the other two compounds, even though in vitro data indicated that all 3 compounds inhibited TOP1 and killed tumor cells. It has been reported that irinotecan activates autophagy (Pharm. Rev. 65:1162, 2013) which correlated with its metabolism to SN-38; we therefore developed, validated and tested an immunofluorescence microscopy assay for LC3 as a marker of autophagy. Methods: Assays were developed to evaluate apoptosis by co-localization of cleaved caspase 3 and gH2Ax, and autophagy by LC3 immunofluorescence on formalin fixed, paraffin-embedded tissue sections of xenografts models or lymphomas from outbred dogs. Percent positive cells containing LC3 puncta were quantitated using a spot morphology algorithm. Analysis of gH2AX and cleaved caspase 3 cellular co-localization was developed using a blebbing morphology algorithm (Definiens). Results: LC3 reported that indenoisoquinoline 706744 activates autophagy in vitro with the absence of cleaved caspase 3-dependent apoptosis while the -776 and -400 compounds do not activate autophagy, but instead demonstrate apoptosis in response to drug treatment. Results in animal models confirmed that both autophagy and apoptosis were active. Clinical readiness of the assays was confirmed on canine biopsy FFPE slides. Conclusions: 1,Structurally-related TOP1 inhibitors may trigger alternative pathways of cell destruction; 2,Autophagy may report drug anti-tumor activity or tumor drug resistance according to current literature. This assay may be useful for determination of pharmacodynamic pathways associated with anti-tumor activity to elucidate mechanism of action of investigational agents used in clinical trials.