Higher weight factors result in a more intense level of Gaussian Blurring being applied. A Weight Factor determines the blur intensity observed in result images after having applied image convolution. Weight Values – The sample application calculates Gaussian matrix kernels and in doing so implements a weight factor.In addition, the edges detected in source/input images will generally be expressed as thicker gradient edges in resulting images. Larger matrix kernels can be computationally expensive to compute as kernel sizes increase. Smaller matrix kernels are faster to compute and generally result in image edges detected in the source/input image to be expressed through thinner gradient edges. Kernel Size – This option relates to the size of the matrix kernels that is to be implemented when performing Gaussian Blurring through image convolution.If desired, the sample application enables users to save resulting images to the local file system through clicking the Save Image button. Load/Save Images – When executing the sample application users are able to load source/input images from the local file system through clicking the Load Image button.The configuration options exposed through the sample application’s user interface can be detailed as follows: The sample application user interface enables the user to configure and control the implementation of a Difference of Gaussians Edge Detection Image filter.
The sample application serves as a practical implementation of the concepts explored throughout this article. This article relies on a sample application included as part of the accompanying sample source code.
This article is accompanied by a sample source code Visual Studio project which is available for download here. This article extends the conventional implementation of Difference of Gaussian algorithms through the application of equally sized matrix kernels only differing by a weight factor.įrog: Kernel 5×5, Weight1 0.1, Weight2 2.1 It is the purpose of this article to illustrate the concept of Difference of Gaussians Edge Detection.