Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier

Islam, Shayla; Hannan, M. A.; Basri, Hassan; Hussain, Aini; Arebey, Maher

doi:10.1016/j.wasman.2013.10.030

Cited by 62 publications

(26 citation statements)

References 33 publications

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“…First, the bin area is detected since garbage level classification is only meaningful if the bin is located correctly. The performance of the proposed method in locating the bin is compared against that of the DTW approach used by Islam et al [17]. Both methods use an empty bin template, but in the DTW method the template is simply moved in 20 pixels step across the image to find an area that best matches the template.…”

Section: Resultsmentioning

confidence: 99%

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Rotation invariant bin detection and solid waste level classification

et al. 2015

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Section: Resultsmentioning

confidence: 99%

“…Islam et al [17], came up with a solution for shifted bin using template matching and Dynamic Time Warping (DTW). DTW is a pattern matching algorithm which finds the warping path of two patterns from their distance metric [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Rotation invariant bin detection and solid waste level classification

et al. 2015

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“…The problem of time-consumption is when using DTW for large-scale time series clustering [14], [29]. A novel approximation for DTW in which the DTW distances can be bound between LB-Keogh (LB) and Euclidian Distance (ED) functions.…”

Section: Methodsmentioning

confidence: 99%

Modified Dynamic Time Warping for Hierarchical Clustering

Sammour

Othman

Rus

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Time series clustering is the process of grouping sequential correspondences in similar clusters. The key feature behind clustering time series data lies on the similarity/distance function used to identify the sequential matches. Dynamic Time Warping (DTW) is one of the common distance measures that have demonstrated competitive results compared to other functions. DTW aims to find the shortest path in the process of identifying sequential matches. DTW relies on dynamic programming to obtain the shortest path where the smaller distance is being computed. However, in the case of equivalent distances, DTW is selecting the path randomly. Hence, the selection could be misguided in such randomization process, which significantly affects the matching quality. This is due to randomization may lead to the longer path which drifts from obtaining the optimum path. This paper proposes a modified DTW that aims to enhance the dynamic selection of the shortest path when handling equivalent distances. Experiments were conducted using twenty UCR benchmark datasets. Also, the proposed modified DTW result has been compared with the state of the art competitive distance measures which is based on precision, recall and f-measure including the original DTW, Minkowski distance measure and Euclidean distance measure. The results showed that the proposed modified DTW reveal superior results in compared to the standard DTW, either using Minkowski or Euclidean. This can demonstrate the effectiveness of the proposed modification in which optimizing the shortest path has enhanced the performance of clustering. The proposed modified DTW can be used for having good clustering method for any time series data.

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“…Images have various features, entropy, energy, power etc. The features are selected based on the output of the features how they keep differences within various images (Islam et al, 2014). There have many methods for feature extraction like subtraction, gray level co-occurrence matrix (GLCM), Gabor filter, Hough transform, basic gray level aura matrix (BGLAM) etc.…”

Section: Imaging Technologymentioning

confidence: 99%