Object Removal Software Test Automation

This paper considers the problem of designing a test method and robust test automation for evaluating the functionality of computer vision algorithms for object removal and image reconstruction of the removed area. An object removal algorithm is used to select and remove a region in an image. After the removal of the object, the object removal algorithm regenerates the area in the image from which the object was removed, similar to photoshop. We developed an algorithm in MATLAB that accesses the features of the macroblocks from the region that adjoins the removed object and correlates these macroblocks with the actual area that is regenerated. The SSIM (structural similarity index) score is calculated by comparing the ground truth with the image generated from image reconstruction after removal of the object. Finally, we calculate the artifact area present in the regenerated region to determine the algorithm quality. To validate this method, the authors compare a 3
^rd
-party object removal solution with an in-house developed solution using the proposed approach. This comparison shows that the SSIM score is 1.55% higher than the 3
^rd
-party solution and 26.3% lower in artifact areas. Besides the results, this paper also describes in detail the procedure to automate the use case of object removal test automation to scale up the test coverage.