We propose an approach for detecting objects in large-scale range datasets that combines bottom-up and top-down processes. In the bottom-up stage, fast-to-compute local descriptors...
Alexander Patterson, Philippos Mordohai, Kostas Da...
Histograms have been widely used for fast estimation of query result sizes in query optimization. In this paper, we propose a new histogram method, called the Skew-Tolerant Histog...
Yohan J. Roh, Jae Ho Kim, Yon Dohn Chung, Jin Hyun...
In this paper, we present a texture descriptor which hinges in the use of the local image statistics so as to recover a compact representation of the texture under study. To this ...
Pattaraporn Khuwuthyakorn, Antonio Robles-Kelly, J...
In this paper, we propose an object detection approach using spatial histogram features. As spatial histograms consist of marginal distributions of an image over local patches, th...
Co-occurrence histograms of oriented gradients (CoHOG) are powerful descriptors in object detection. In this paper, we propose to utilize a very large pool of CoHOG features with ...