Computer Vision News - July 2019

less accurate results on ImageNet. However, these results are also interpretable and sometimes enable to identify the objects in the image. You can appreciate the results in the following figure: In the figure above, the number of iteration axes refer to the number of iterations in the optimization scheme. Although the reconstructions are not entirely identical to the source images, it can be seen that they capture the main object, some details of the object and this gives some abstract representation of the image. The last set of results is the reconstruction from imagination. In this task, the subjects were instructed to imagine an image that they saw in the experiment. The reconstruction, in this case, has been done based on the imagined image. This of course is much more challenging task as the signal to noise ratio is dramatically increased. In the following figure you can see the results of this experiment: Conclusion Image reconstruction from brain signal is a fascinating task that several teams around the globe are working hard to solve. The high signal to noise ratio, as well as the high dimensionality of both the fMRI signal and the image, are the greatest challenges in this field. The results presented above can be considered very good results in this field. As you can see, there is a lot of research to do on this task, many methods to try, which will hopefully bring us to better solutions for the problem. There is no doubt that the ability to reconstruct images from the brain will have a tremendous influence on humanity and on science in general. Research 12 Research Computer Vision News