Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can pre

Background: Sprite coding is a very effective technique for clarifying the background video object. The sprite generation is an open issue because of the foreground objects which prevent the precision of camera motion estimation and blurs the created sprite. Objective: In this paper, a quick and basic static method for sprite area detection in video data is presented. Two statistical methods are applied; the mean and standard deviation of every pixel (over all group of video frame) to determine whether the pixel is a piece of the selected static sprite range or not. A binary map array is built for demonstrating the allocated sprite (as 1) while the non-sprite (as 0) pixels valued. Likewise, holes and gaps filling strategy was utilized to re

<p>Generally, The sending process of secret information via the transmission channel or any carrier medium is not secured. For this reason, the techniques of information hiding are needed. Therefore, steganography must take place before transmission. To embed a secret message at optimal positions of the cover image under spatial domain, using the developed particle swarm optimization algorithm (Dev.-PSO) to do that purpose in this paper based on Least Significant Bits (LSB) using LSB substitution. The main aim of (Dev. -PSO) algorithm is determining an optimal paths to reach a required goals in the specified search space based on disposal of them, using (Dev.-PSO) algorithm produces the paths of a required goals with most effi

Today, urban Stormwater management is one of the main concerns of municipalities and stakeholders. Drought and water scarcity made rainwater harvesting one of the main steps toward climate change adaptation. Due to the deterioration of the quality of urban runoff and the increase of impermeable urban land use, the treatment of urban runoff is essential. Best Management Practice (BMP) and Low Impact Development (LID) approaches are necessary to combat climate change consequences by improving the quantity and quality of water resources. The application of Bioswales along urban streets and roadways can reduce the stress on water resources, recharge groundwater and prevent groundwater pollution. While Sulaymaniyah City has a

Several directional wells have been drilled in Majnoon oilfield at wide variation in drilling time due to different drilling parameters applied for each well. This technical paper shows the importance of proper selection of the bit, Mud type, applied weight on Bit (WOB), Revolution per minute (RPM), and flow rate based on the previous wells drilled. Utilizing the data during drilling each section for directional wells that's significantly could improve drilling efficiency presented at a high rate of penetration (ROP). Based on the extensive study of three directional wells of 35 degree inclination (MJ-51, MJ-52, and MJ-54) found that the applied drilling parameters for MJ-54 and the bit type within associated drilling parameters to drill

This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe

The study was conducted at the fields of the Department of Horticulture and Landscape Gardening,College of Agriculture, University of Baghdad during the growing seasons of 2013- 2014 .forPerformance of Evaluation Vegetative growth and yield traits and estimate some important geneticparameter on seven selected breed of tomato which (S1-S7 ) Pure line. the results found significantdifferences between breeds in all study trails except clusters flowering number .S1 significantly plantlength which reached 227.3 .Also S1,S2 and S4 were significantly increased the number fruit for plant,Fruit weight Increased in S3 ,S6 and plant yield. Increased in S1, S4 ,S5. Genetic variation valueswere low in Floral clusters , TSS and fruit firmest and medium i