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Scale Invariant Single Face Tracking Using Particle Filtering With Skin Color
Adhitama, Perdana,Kim, Soo Hyung,Na, In Seop The Korea Contents Association 2013 International Journal of Contents Vol.9 No.3
In this paper, we will examine single face tracking algorithms with scaling function in a mobile device. Face detection and tracking either in PC or mobile device with scaling function is an unsolved problem. Standard single face tracking method with particle filter has a problem in tracking the objects where the object can move closer or farther from the camera. Therefore, we create an algorithm which can work in a mobile device and perform a scaling function. The key idea of our proposed method is to extract the average of skin color in face detection, then we compare the skin color distribution between the detected face and the tracking face. This method works well if the face position is located in front of the camera. However, this method will not work if the camera moves closer from the initial point of detection. Apart from our weakness of algorithm, we can improve the accuracy of tracking.
A Sustainable Thermography Imaging Assay to Determine Plant Biotic and Abiotic Stresses
( Reza Adhitama Putra Hernanda ),( Hoonsoo Lee ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2
Plant is considered as a natural resource for humans and animals. It is rich in micro and macronutrients, such as carbohydrates, protein, vitamins, minerals, and bioactive compounds. Furthermore, a new life trend, a plant-based meal, is the reason for elevating the demand rate. Thus, maintaining an optimum yield for agricultural products is required. However, naturally, plants suffer from stresses that might occur during growth, namely biotic and abiotic stresses. Various literature reported that it is undoubtedly decreasing plant productivity and even death, contrary to the global food demand. The plant acts through physical and biochemical aspects to respond to any experienced stresses. Researchers use those responses as a parameter evaluation to diagnose plant status, such as pigment contents, stomatal conductance, antioxidant activity, and gas exchange. However, these methods are conducted destructively, time-consuming, labour-intensive, requiring sample preparation, and unsustainable. Nevertheless, temperature has been considered a suitable parameter for assessing plant stress. Researchers have favoured thermographic assay by thermal imaging because of its easiness, non-contact, and sustainability. Thermal imaging offers a spatial temperature profile, that is, described by each pixel. Our objective is to establish a systematic literature review based on the recent publications spreading over the network. Based on our study, abiotic and biotic stress showed different temperatures in plant leaves. This presented work reveals that a thermal camera is proven to be a non-destructive assay to determine plant stress equipped by various method techniques, such as thermal infrared index, machine learning, and even deep learning.