  Correctly identifying the nature and properties of outgoing particles from
high energy collisions at the Large Hadron Collider is a crucial task for all
aspects of data analysis. Classical calorimeter-based classification techniques
rely on shower shapes -- observables that summarize the structure of the
particle cascade that forms as the original particle propagates through the
layers of material. This work compares shower shape-based methods with computer
vision techniques that take advantage of lower level detector information. In a
simplified calorimeter geometry, our DenseNet-based architecture matches or
outperforms other methods on $e^+$-$\gamma$ and $e^+$-$\pi^+$ classification
tasks. In addition, we demonstrate that key kinematic properties can be
inferred directly from the shower representation in image format.
