Differential white blood cell counts in apparently
healthy Eritrean blood donors at the National Blood
Transfusion Center, Asmara, Eritrea

Abdon, Faris Mohamed Awad; Adgoy, Elias Teages; Berhane, Nahom; Ibrahim, Senet Awolker; Khalil, Adil; Seyoum, Yemane; Abbadi, Osama Sharafeldin

doi:10.18332/popmed/120071

Cited by 1 publication

(2 citation statements)

References 8 publications

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“…For the second challenge, as for the inter-frame displacement, it was a large (5), where the video frames were used, which included a very noticeable change in the position of the sun. The proposed algorithm overcame this challenge by increasing the capture range of the ADFAM as well as increasing the force of ADF force field in the video frames (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Note Table (2).…”

Section: Rigid Object With Still Backgroundmentioning

confidence: 99%

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Object Tracking Using Adaptive Diffusion Flow Active Model

Alwan

Almarsoomi

2021

Int. J. Onl. Eng.

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Object tracking is one of the most important topics in the fields of image processing and computer vision. Object tracking is the process of finding interesting moving objects and following them from frame to frame. In this research, Active models–based object tracking algorithm is introduced. Active models are curves placed in an image domain and can evolve to segment the object of interest. Adaptive Diffusion Flow Active Model (ADFAM) is one the most famous types of Active Models. It overcomes the drawbacks of all previous versions of the Active Models specially the leakage problem, noise sensitivity, and long narrow hols or concavities. The ADFAM is well known for its very good capabilities in the segmentation process. In this research, it is adopted for segmentation and tracking purposes. The proposed object tracking algorithm is initiated by detecting the target moving object manually. Then, the ADFAM convergence of the current video frame is reused as an initial estimation for the next video frame and so on. The proposed algorithm is applied to several video sequences, different in terms of the nature of the object, the nature of the background, the speed of the object, object motion direction, and the inter-frame displacement. Experimental results show that the proposed algorithm performed very well and successfully tracked the target object in all different cases.

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Section: Rigid Object With Still Backgroundmentioning

confidence: 99%

“…White blood cells move within a moving background that includes other objects, such as (plasma, platelets, red blood cells, etc.) [18,28]. Three challenges are facing the tracking of the white blood cell motion.…”

Section: Non-rigid Object With Moving Backgroundmentioning

confidence: 99%

Object Tracking Using Adaptive Diffusion Flow Active Model

Alwan

Almarsoomi

2021

Int. J. Onl. Eng.

View full text Add to dashboard Cite

show abstract

Differential white blood cell counts in apparently healthy Eritrean blood donors at the National Blood Transfusion Center, Asmara, Eritrea

Cited by 1 publication

References 8 publications

Object Tracking Using Adaptive Diffusion Flow Active Model

Object Tracking Using Adaptive Diffusion Flow Active Model

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