Ryushi Ozaki

Object detection based on a robust and accurate statistical multi-point-pair model

In this paper, we propose a robust and accurate background model, called grayscale arranging pairs (GAP). The model is based on the statistical reach feature (SRF), which is defined as a set of statistical pair-wise features. Using the GAP model, moving objects are successfully detected under a variety of complex environmental conditions. The main concept of the proposed method is the use of multiple point pairs that exhibit a stable statistical intensity relationship as a background model. The intensity difference between pixels of the pair is much more stable than the intensity of a single pixel, especially in varying environments. Our proposed method focuses more on the history of global spatial correlations between pixels than on the history of any given pixel or local spatial correlations. Furthermore, we clarify how to reduce the GAP modeling time and present experimental results comparing GAP with existing object detection methods, demonstrating that superior object detection with higher precision and recall rates is achieved by GAP.

Statistical reach feature method and its application to robust image registration

In this paper, a novel method for image registration method which affords robust results for various disturbances in the real world, including local and/or global variations of illumination, occlusions, and noises, is proposed. The registration process is based on a set of selected point-pairs with binary coded signs of differences, which is constructed from the given template image. The selection of the point-pairs is defined on the basis of statistical view point. The authors developed the mathematical model of the inverting-ratio which is the quantity strongly related to the similarity index, for the Gaussian-disturbances. The authors also verified the model by numerical experiments. The mathematical model, enforced by the numerical results, gives the theoretical backbone for the robustness of the proposed method.

Robust adapted object detection under complex environment

In this paper, we present a novel robust technique for background subtraction in different complex conditions (e.g. sudden illumination changes, swaying leaves, and camera vibrations). Unlike the previous works, the proposed method utilizes multiple point pairs that exhibit a stable statistical intensity relationship as a background model. The intensity difference between pixels of the pair is much more stable than the intensity of a single pixel, especially in varying environments. Furthermore, our proposed method focuses more on the history of global spatial correlations between pixels than on the history of any given pixel or local spatial correlations. we also adopt an adapted judgement criterion to ensure our method displays well in real-time detection. The approach has been compared with the state of the art on videos from several challenging datasets (PETS, Wallflower, and i-Lids), demonstrating that superior object detection is achieved.