In stereo algorithms with more than two cameras, the improvement of accuracy is often reported since they are robust against noise. However, another important aspect of the polynocular stereo, that is the ability of occlusion detection, has been paid less attention. We intensively analyzed the occlusion in the camera matrix stereo (SEA) and developed a simple but effective method to detect the presence of occlusion and to eliminate its effect in the correspondence search. By considering several statistics on the occlusion and the accuracy in the SEA, we derived a few base masks which represent occlusion patterns and are effective for the detection of occlusion. Several experiments using typical indoor scenes showed quite good performance to obtain dense and accurate depth maps even at the occluding boundaries of objects.