To obtain text information included in a scene image, we first need to extract text regions from the image before recognizing the text. In this paper, we examine human vision and propose a novel method to extract text regions by evaluating textural variation. Human beings are often attracted by textural variation in scenes, which causes foveation. We frame a hypothesis that texts also have similar property that distinguishes them from the natural background. In our method, we calculate spatial variation of texture to obtain the distribution of the degree of likelihood of text region. Here we evaluate the changes in local spatial spectrum as the textural variation. We investigate two options to evaluate the spectrum, that is, those based on one- and two-dimensional Fourier transforms. In particular, in this paper, we put emphasis on the one-dimensional transform, which functions like the Gabor filter. The proposal can be applied to a wide range of characters mainly because it employs neither templates nor heuristics concerning character size, aspect ratio, specific direction, alignment, and so on. We demonstrate that the method effectively extracts text regions contained in various general scene images. We present quantitative evaluation of the method by using databases open to the public.

Based on the observation that foveation analysis can be used to find most critical content in terms of human visual perception in video and image, one effective error resilience method is proposed for robust transmission of H.264 intra-coded frame in wireless channel. It firstly exploits the results of foveation analysis to find foveated area in picture, and then considers the results of pre-error concealment effect analysis to search for the center of foveation macro-blocks (MB) in foveated area, finally new error resilient alignment order of MB and new coding order of MB are proposed that are used in video encoder. Extensive experimental results on different portrait video sequences over random bit error wireless channel demonstrate that this proposed method can achieve better subjective and objective effect than original JM 8.2 H.264 video codec with little effect on coding rate and image quality.