Published Online: Pattern Similarity Score Based on One Dimensional Time-series Analysis
JPRR Article #744
Pattern recognition and matching operation includes parameters such as mean, correlation, mutual information etc. A novel procedure to quantify similarity in different images based on time-series analysis is reported here. The proposed technique consists of orderly application of various mathematical transformations on one dimensional time series obtained from a 2d-image array. These transformations include array to time-series conversion, local maxima detection-joining, and calculation of cumulative angle. The final calculated parameter is a direct pointer to the image similarity. The proposed technique has performed well against traditional image comparison techniques under specific circumstances. The technique can be also used to identify similar patterns in a single image. The simulation codes have been written on SCILAB platform.
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