We propose the use of a reliable confidence map for multi-layer stixel segmentation; our confidence map uses a calibrated collinear trinocular vision model. It is generated from three conjugate synchronized stereo images for evaluating the consistency of disparity values. The evaluation measure is referred to as transitivity error in disparity space. Multi-layer stixels are commonly generated from a single disparity map which make them merely dependent on the applied stereo matcher. A multi-map fusion is proposed to achieve more reliable stixel segmentation for disparity values. Moreover, another advantage of our work is to provide a new and effective ground-detection technique (ground-manifold detection) which benefits from the confidence map. Experimental results show a significant improvement on average of 12.6% using our method compared with conventional stixels detected by binocular vision only.

The objective of this research is to design a new JPEG-based compression scheme which simultaneously considers the security issue. Our method starts from dividing image into non-overlapping blocks with size 8×8. Among these blocks, some are used as reference blocks and the rest are used as query blocks. A query block is the combination of the residual and the resultant of a filtered reference block. We put our emphasis on how to estimate an appropriate filter and then use it as part of a secret key. With both reference blocks and the residuals of query blocks, one is able to encode secured images using a correct secret key. The experiment results will demonstrate that how different secret keys can control the quality of restored image based on the priority of authority.

Panoramic images and panoramic cameras (or sensors) are of increasing importance for various applications in computer vision, computer graphics, visualization, and robotics. Various panoramic image capturing sensors have been developed for different purposes. But many of the sensing devices do not support stereo visualization. This paper reviews a methodology for stereo panorama acquisition using a widely available digital matrix camera. Two sensor parameters, off-axis distance and principle angle, are critical to achieving high quality stereo visualization. In this paper, a new camera calibration method will be presented, which supports accurate estimations of these two essential parameters. To our best knowledge, there is no other camera calibration method published so far for such a purpose.

In this paper, we propose a data-driven foreground object detection technique which can detect foreground objects from a moving camera. We propose to build a data-driven consensus foreground object template (CFOT) and then detect the foreground object region in each frame. The proposed foreground object detection technique is equipped with the following functions: (1) the ability to detect the foreground object captured by a fast moving camera ; (2) the ability to detect a low contrast (spatially/temporally) foreground object; and (3) the ability to detect a foreground object from a dynamic background. There are three contributions of our method: (1) a newly proposed data-driven foreground region decision process for generating the CFOT has been shown robust and efficient; (2) a foreground object probability is proposed for properly dealing with the imperfect initial foreground region estimations; and (3) a CFOT is generated for precise foreground object detection.