Various haze removal methods based on the atmospheric scattering model have been presented in recent years. Most methods have targeted strong haze images where light is scattered equally in all color channels. This paper presents a haze removal method using near-infrared (NIR) images for relatively weak haze images. In order to recover the lost edges, the presented method first extracts edges from an appropriately weighted NIR image and fuses it with the color image. By introducing a wavelength-dependent scattering model, our method then estimates the transmission map for each color channel and recovers the color more naturally from the edge-recovered image. Finally, the edge-recovered and the color-recovered images are blended. In this blending process, the regions with high lightness, such as sky and clouds, where unnatural color shifts are likely to occur, are effectively estimated, and the optimal weighting map is obtained. Our qualitative and quantitative evaluations using 59 pairs of color and NIR images demonstrated that our method can recover edges and colors more naturally in weak haze images than conventional methods.

We propose an iterative single-image haze-removal method that first divides images with haze into regions in which haze-removal processing is difficult and then estimates the ambient light. The existing method has a problem wherein it often overestimates the amount of haze in regions where there is a large distance between the location the photograph was taken and the subject of the photograph; this problem prevents the ambient light from being estimated accurately. In particular, it is often difficult to accurately estimate the ambient light of images containing white and sky regions. Processing those regions in the same way as other regions has detrimental results, such as darkness or unnecessary color change. The proposed method divides such regions in advance into multiple small regions, and then, the ambient light is estimated from the small regions in which haze removal is easy to process. We evaluated the proposed method through some simulations, and found that the method achieves better haze reduction accuracy even than the state-of-the art methods based on deep learning.

A new exemplar-based inpainting method which effectively preserves global structures and textures in the restored region driven by feature vectors is presented. Exemplars that belong to the source region are segmented based on their features. To express characteristics of exemplars such as shapes of structures and smoothness of textures, the Harris corner response and the variance of pixel values are employed as a feature vector. Enhancements on restoration plausibility and processing speedup are achieved as shown in the experiments.

Finite difference time domain (FDTD) method has been accelerated on the Cell Broadband Engine (Cell B.E.). However the problem has arisen that speedup is limited by the bandwidth of the main memory on large-scale analysis. As described in this paper, we propose a novel algorithm and implement FDTD using it. We compared the novel algorithm with results obtained using region segmentation, thereby demonstrating that the proposed algorithm has shorter calculation time than that provided by region segmentation.

In the present paper, we propose an object tracking method called scenario-type hypothesis object tracking. In the proposed method, an indoor monitoring region is divided into multiple closed micro-cells using sensor nodes that can detect objects and their moving directions. Sensor information is accumulated in a tracking server through wireless multihop networks, and object tracking is performed at the tracking server. In order to estimate the trajectory of objects from sensor information, we introduce a novel concept of the virtual world, which consists of virtual micro-cells and virtual objects. Virtual objects are generated, transferred, and deleted in virtual micro-cells according to sensor information. In order to handle specific movements of objects in micro-cells, such as slowdown of passing objects in a narrow passageway, we also consider the generation of virtual objects according to interactions among virtual objects. In addition, virtual objects are generated when the tracking server estimates loss of sensor information in order to decrease the number of object tracking failures. Through simulations, we confirm that the ratio of successful tracking is improved by up to 29% by considering interactions among virtual objects. Furthermore, the tracking performance is improved up to 6% by considering loss of sensor information.

Tracking a non-rigid object in a video in the presence of background clutter and partial occlusion is challenging. We propose a non-rigid object-tracking paradigm by repeatedly detecting and associating saliency regions. Saliency region segmentation is operated in each frame. The segmentation results provide rich spatial support for tracking and make the reliable tracking of non-rigid object without drifting possible. The precise object region is obtained simultaneously by associating the saliency region using two independent observers. Our formulation is quite general and other salient-region segmentation algorithms also can be used. Experimental results have shown that such a paradigm can effectively handle tracking problems of objects with rapid movement, rotation and partial occlusion.

A novel technique for automatic segmentation of a brain region in single channel MR images for visualization and analysis of a human brain is presented. The method generates a volume of brain masks by automatic thresholding using a dual curve fitting technique and by 3D morphological operations. The dual curve fitting can reduce an error in curve fitting to the histogram of MR images. The 3D morphological operations, including erosion, labeling of connected-components, max-feature operation, and dilation, are applied to the cubic volume of masks reconstructed from the thresholded brain masks. This method can automatically segment a brain region in any displayed type of sequences, including extreme slices, of SPGR, T1-, T2-, and PD-weighted MR image data sets which are not required to contain the entire brain. In the experiments, the algorithm was applied to 20 sets of MR images and showed over 0.97 of similarity index in comparison with manual drawing.

This paper presents a new scheme to estimate depth in a natural three-dimensional scene using a multi-viewpoint image set. In the conventional Multiple-Baseline Stereo (MBS) scheme for the image set, although errors of stereo matching are somewhat reduced by using multiple stereo pairs, the use of square blocks of fixed size sometimes causes false matching, especially, in that image area where occlusion occurs and that image area of small variance of brightness levels. In the proposed scheme, the reference image is segmented into regions which are capable of being arbitrarily shaped, and a depth value is estimated for each region. Also, by comparing the image generated by projection with the original image, depth values are newly estimated in a top-down manner. Then, the error of the previous depth value is detected, and it is corrected. The results of experiments show advantages of the proposed scheme over the MBS scheme.

This paper presents an overview of research activities in Japan in the field of very low bit-rate video coding. Related research based on the concept of "intelligent image coding" started in the mid-1980's. Although this concept originated from the consideration of a new type of image coding, it can also be applied to other interesting applications such as human interface and psychology. On the other hand, since the beginning of the 1990's, research on the improvement of waveform coding has been actively performed to realize very low bit-rate video coding. Key techniques employed here are improvement of motion compensation and adoption of region segmentation. In addition to the above, we propose new concepts of image coding, which have the potential to open up new aspects of image coding, e.g., ideas of interactive image coding, integrated 3-D visual communication and coding of multimedia information considering mutual relationship amongst various media.

Correspondence based on regions rather than lines seems to be effective, as regions are usually fewer than other image features and provide global information such as size, color, adjacency, etc. In this paper, we present a region matching approach for solving the correspondence problem. Images are segmented into regions and are individually described by classification tables using region adjacencies. From the structural description of the two images, the region matching process based on color and structural similarity is carried out. First, a small number of significant regions are selected and matched by using color, and then they are used as handles for constraint propagation to match the remaining regions by using structures. Our technique was implemented by using an efficient selection and propagation algorithm and was tested with a variety of scenes.