Finding complex patterns using template matching

Abstract

Complex patterns that combine pixels of different brightness and colour in a fixed spatial layout pose a challenge for the objectoriented approach to image analysis. This is because the pixels that make up the pattern cannot readily be linked together into an object as they do not share spectral or textural properties. The goal of this study was to determine whether template matching techniques can be used to identify those patterns without a prior segmentation of the image. We studied three different scenarios that pose specific challenges: the application of template matching is most straightforward in (1) the detection of symbols in scanned maps where different occurrences of a given pattern strongly resemble one another; most notably, symbols always have the same size and orientation. (2) The detection of trees in aerial images of oil palms is more challenging, since the characteristic tree pattern exhibits significant variations from tree to tree. Finally, in the (3) detection of pavement markings in aerial images objects could appear at arbitrary orientations in the image. Our results show that template matching successfully identifies many target regions. Once target locations are known, objects can be created and the full power of object-based image analysis approaches can be applied to eliminate remaining false positives.

Vision inspection and monitoring of wind turbine farms in emerging smart grids

“…as the frequency of blade rotation. Finally, the velocity of the blade can be calculated by simply replacing W i * =0.1789 and f S = 40 in equation (16) then V B = 52.68RPM will be estimated for simulated scenario. This value means that our model successfully estimate blade velocity without any error.…”

“…In a TM approach, it is sought the point in which it is presented the best possible resemblance between a sub image known as a template and its coincident region within a source image [15]. There are a lot of methods for pattern and template matching [15][16][17][18] but for simplicity, we use correlation coefficient [16] template matching to find a hub in an input image. So we benefits Pearson's correlation coefficient as below [16]:…”

Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery

“…Templates are then compared to the imagery requiring analysis and the results of a cross-correlation are output as a layer displaying the level of similarity across the image, with larger values representing areas of greater similarity and likely to be banana crowns. To increase the robustness of the detection method to changes in illumination between the template and analysis datasets, they were normalised prior to correlation calculations [65]. For banana crown detection, template-sample generation was based on the dataset collected on 28 August using banana crown centre-point locations identical to those utilized for the CNN model to provide a comparative representation of samples using a template size of 36 × 36 pixels.…”

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

“…Different algorithms are in common use for this method, with the measurement of similarity carried out by using the squared differences between a template and an image region being one of the most simple approaches (Jasvilis et al, 2016). Template matching has been successfully applied such as for the detection of individual trees of palm plantations featuring accuracies of almost 90% (e.g.…”