I am beginning an image analysis project to recognize images with a particular object centered on the image. If the object is at the center, I give the image a positive label, and if it is anywhere else, or simply not in the image, I give the image a negative label. The object, itself, has a complex pattern, such that statistical methods and basic image processing techniques are not able to detect it. The human eye, however, has no trouble detecting this object. Therefore, I am opting to develop a convolutional network that can parse the complexity of this pattern. The only issue, however, is that convolutional networks are inherently designed to be spatially invariant. Therefore, is it even possible to train the network to focus on the importance of the object being at the center simply by feeding the network many negative examples containing the object anywhere else but the center? Furthermore, is there perhaps a better or more direct way to go about incorporating this spatial aspect into the network's functionality?