In this paper, we propose a precise indoor localization method using visible light communication (VLC) with dual-facing cameras on a smart device (mobile phone, smartphone, or tablet device). This approach can assist the visually impaired with navigation, or provide mobile-robot control. The proposed method is different from conventional techniques in that dual-facing cameras are used to expand the localization area. The smart device is used as the receiver, and light-emitting diodes on the ceiling are used as localization landmarks. These are identified by VLC using a rolling shutter effect of complementary metal-oxide semiconductor image sensors. The front-facing camera captures the direct incident light of the landmarks, while the rear-facing camera captures mirror images of landmarks reflected from the floor face. We formulated the relationship between the poses (position and attitude) of the two cameras and the arrangement of landmarks using tilt detection by the smart device accelerometer. The equations can be analytically solved with a constant processing time, unlike conventional numerical methods, such as least-squares. We conducted a simulation and confirmed that the localization area was 75.6% using the dual-facing cameras, which was 3.8 times larger than that using only the front-facing camera. As a result of the experiment using two landmarks and a tablet device, the localization error in the horizontal direction was less than 98 mm at 90% of the measurement points. Moreover, the error estimation index can be used for appropriate route selection for pedestrians.

Region of interest (ROI) coding is a useful function for many applications. JPEG2000 supports ROI coding and can decode ROIs preferentially regardless of the shape and number of the regions. However, if the number of regions is quite large, the ROI coding performance of JPEG2000 declines because the code-stream includes many useless non-ROI codes. This paper proposes a wavelet-based ROI coding method suited for multiple ROIs. The proposed wavelet transform does not access any non-ROIs when transforming the ROIs. Additionally, the proposed method eliminates the need for unnecessary coding of the bits in the higher bit planes of non-ROI regions by adding an ROI map to the code-stream. The experimental results show that the proposed method achieves a higher peak signal-to-noise ratio than the ROI coding of JPEG2000. The proposed method can be applied to both max-shift and scaling-based ROI coding.