Adhi Prahara
Informatics Department, Universitas Ahmad Dahlan

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Parallel Approach of Adaptive Image Thresholding Algorithm on GPU Adhi Prahara; Andri Pranolo; Nuril Anwar; Yingchi Mao
Knowledge Engineering and Data Science Vol 4, No 2 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v4i22021p69-84


Image thresholding is used to segment an image into background and foreground using a given threshold. The threshold can be generated using a specific algorithm instead of a pre-defined value obtained from observation or experiment. However, the algorithm involves per pixel operation, histogram calculation, and iterative procedure to search the optimum threshold that is costly for high-resolution images. In this research, parallel implementations on GPU for three adaptive image thresholding methods, namely Otsu, ISODATA, and minimum cross-entropy, were proposed to optimize their computational times to deal with high-resolution images. The approach involves parallel reduction and parallel prefix sum (scan) techniques to optimize the calculation. The proposed approach was tested on various sizes of grayscale images. The result shows that the parallel implementation of three adaptive image thresholding methods on GPU achieves 4-6 speeds up compared to the CPU implementation, reducing the computational time significantly and effectively dealing with high-resolution images. 
Hierarchical long short-term memory for action recognition based on 3D skeleton joints from Kinect sensor Nur Awal Hidayanto; Adhi Prahara; Riky Dwi Puriyanto
Jurnal Informatika Vol 15, No 1 (2021): January 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i1.a20106


Action recognition has been used in a wide range of applications such as human-computer interaction, intelligent video surveillance systems, video summarization, and robotics. Recognizing action is important for intelligent agents to understand, learn and interact with the environment. The recent technology that allows the acquisition of RGB+D and 3D skeleton data and a deep learning model's development significantly increases the action recognition model's performance. In this research, hierarchical Long Sort-Term Memory is proposed to recognize action based on 3D skeleton joints from Kinect sensor. The model uses the 3D axis of skeleton joints and groups each joint in the axis into parts, namely, spine, left and right arm, left and right hand, and left and right leg. To fit the hierarchically structured layers of LSTM, the parts are concatenated into spine, arms, hands, and legs and then concatenated into the body. The model crosses the body in each axis into a single final body and fed to the final layer to classify the action. The performance is measured using cross-view and cross-subject evaluation and achieves accuracy 0.854 and 0.837, respectively, from the 10 action classes of the NTU RGB+D dataset.