We approach the problem around several main directions of video temporal segmentation and propose an intensity-based dissolve detection approach that is able to perform on animated video contents. It uses the hypothesis that during a dissolve the amount of fading-out and fading-in pixels should be significant compared with other visual transitions. We use this information as a visual discontinuity function. Instead of just applying a global threshold to filter these values, as most of the existing approaches do, we use a twin-thresholding approach and the shape analysis of the discontinuity measure. This allows us to reduce false detections caused by steep intensity fluctuations as well as to retrieve dissolves caught up in other visual transitions (e.g., caused by movement, color effects, etc.). Experimental tests conducted on more than 452 dissolve transitions show that whether classic approaches tend to fail, the proposed method is able to provide good performance achieving average precision and recall ratios above 94% and 79.6%, respectively.