In this paper, we present a novel approach that addresses the blind reconstruction problem in scanning electron microscope (SEM) photometric stereo. Using only two observed images that suffer from shadowing effects, our method automatically calibrates the parameter and resolves shadowing errors for estimating an accurate three-dimensional (3D) shape and underlying shadowless images. We introduce a novel shadowing compensation model using image intensities for both cases of presence and absence of shadowing. With this model, the proposed de-shadowing algorithm iteratively compensates for image intensities and modifies the corresponding 3D surface. Besides de-shadowing, we introduce a practically useful self-calibration criterion by enforcing a good reconstruction. We show that incorrect parameters will engender significant distortions of 3D reconstructions in shadowed regions during the de-shadowing procedure. This motivated us to design the self-calibration criterion by utilizing shadowing to pursue the proper parameter that produces the best reconstruction with least distortions. As a result, we develop a bootstrapping approach for simultaneous de-shadowing and self-calibration in SEM photometric stereo. Extensive experiments on real image data demonstrate the effectiveness of our method.

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270002/02.jpg"" width=""400"" /> CP3 histogram</div> An ideal similarity measure for matching image should be discriminative, producing a conspicuous correlation peak and suppressing false local maxima. Image matching tasks in practice, however, often involves complex conditions, such as blurring and fluctuating illumination. These may cause the similarity measure to not be discriminative enough. We utilized a robust scene modeling method to model the appearance of an image and propose an associated similarity measure for image matching. The proposed method utilizes a spatio-temporal learning stage to select a group of supporting pixels for each target pixel, then builds a differential statistic model of them to describe the uniqueness of the spatial structure and to provide illumination invariance for robust matching. We utilized this method for image matching in several challenging environments. Experimental results show that the proposed similarity measure produces explicit correlation peaks to achieve robust image matching. </span>