Image Stitching Algorithm Based on Embedded System

Shi, Zhengde; Yu, Hong Juan; Kong, Liuyang

doi:10.1109/icma.2018.8484530

Cited by 1 publication

(1 citation statement)

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“…This approach will easily be implemented on a computer system. However, for systems that have an embedded platform [22]- [25], there will be many problems to implement, plus the face pattern must be used as a mobile robot motion navigation pattern. A reliable method that can be used in systems with embedded platforms is a ram base neural network [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

Robot movement controller based on dynamic facial pattern recognition

Nurmaini

Zarkasi

Stiawan

et al. 2021

IJEECS

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In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the Upper and Lower frame areas, resulting in an accuracy rate of 95%, then for the Right and Left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms).

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