In this paper, we present an object detection technique that uses scale invariant local edgel structures and their properties to locate multiple object categories within a range image in the presence of partial occlusion, cluttered background, and significant scale changes. The fragmented local edgels (key-edgel
) are efficiently extracted from a 3D edge map by separating them at their corner points. The 3D edge maps are reliably constructed by combining both boundary and fold edges of 3D range images. Each key-edgel is described using our scale invariant descriptors that encode local geometric configuration by joining the edgel to adjacent edgels at its start and end points. Using key-edgels and their descriptors, our model generates promising hypothetical locations in the image. These hypotheses are then verified using more discriminative features. The discriminative feature consists of a bag-of-words histogram constructed by key-edgels and their descriptors, and a pyramid histogram of orientation gradients. To find the similarities between different feature types in a discriminative stage, we use an exponential χ2
merging kernel function. Our merging kernel outperforms the conventional rbf
kernel of the SVM classifier. The approach is evaluated based on ten diverse object categories in a real-world environment.