The checkerboard is a frequently used pattern in camera calibration, an essential process to get intrinsic parameters for more accurate information from images. An automatic checkerboard detection method that can detect multiple checkerboards in a single image is proposed. It contains a corner extraction approach using self-correlation and a structure recovery solution using constraints related to adjacent corners and checkerboard block edges. The method utilizes the central symmetric feature of the checkerboard crossings as well as the spatial relationship of neighboring checkerboard corners and the grayscale distribution of their neighboring pixels. Five public datasets are used in the experiments to evaluate the method. Results show high detection rates and a short average runtime of the proposed method. In addition, the camera calibration accuracy also presents the effectiveness of the proposed detection method with reprojected pixel errors smaller than 0.5 pixels.
An approach for carrying out depth profile measurement of an object with the plenoptic camera is proposed. A single plenoptic image consists of multiple lenslet images. To begin with, these images are processed directly with a refocusing technique to obtain the depth map, which does not need to align and decode the plenoptic image. Then, a linear depth calibration is applied based on the optical structure of the plenoptic camera for depth profile reconstruction. One significant improvement of the proposed method concerns the resolution of the depth map. Unlike the traditional method, our resolution is not limited by the number of microlenses inside the camera, and the depth map can be globally optimized. We validated the method with experiments on depth map reconstruction, depth calibration, and depth profile measurement, with the results indicating that the proposed approach is both efficient and accurate.
A reflection TIE system consisting of a reflecting microscope and a 4f relay system is presented in this paper, with which the transport of intensity equation (TIE) is applied to reconstruct the three-dimensional (3D) profile of opaque micro objects like wafer structures for 3D inspection. As the shape of an object can affect the phases of waves, the 3D information of the object can be easily acquired with the multiple phases at different refocusing planes. By electronically controlled refocusing, multi-focal images can be captured and used in solving TIE to obtain the phase and depth of the object. In order to validate the accuracy and efficiency of the proposed system, the phase and depth values of several samples are calculated, and the experimental results is presented to demonstrate the performance of the system.
A method is proposed for depth extraction of low-texture region with a light field (LF) camera, which expands the application. Based on the analysis of LF data, it is proven that the depth information can be estimated from a single LF image. Furthermore, as the lenslet LF data can be decoded into a subimages array, and the relationship among subimages is proven to be affine transformation, we used the geometry relationship, which is represented by partition ratio of triangle grids area, to replace the unreliable gray value of low-texture region for stereo matching. In addition, to obtain accurate ratio values, preset points are projected to enrich the texture with a projector, which is convenient and reliable. The proposed method improves the accuracy of the depth extraction obviously at low-texture region compared with the traditional state-of-the-art LF method, and results are validated by experiments.
Videos from a small Unmanned Aerial Vehicle (UAV) are always unstable because of the wobble of the vehicle and the impact of surroundings, especially when the motion has a large drifting. Electronic image stabilization aims at removing the unwanted wobble and obtaining the stable video. Then estimation of intended motion, which represents the tendency of global motion, becomes the key to image stabilization. It is usually impossible for general methods of intended motion estimation to obtain stable intended motion remaining as much information of video images and getting a path as much close to the real flying path at the same time. This paper proposed a fuzzy Kalman filtering method to estimate the intended motion to solve these problems. Comparing with traditional methods, the fuzzy Kalman filtering method can achieve better effect to estimate the intended motion.
The airborne video streams of small-UAVs are commonly plagued with distractive jittery and shaking motions, disorienting rotations, noisy and distorted images and other unwanted movements. These problems collectively make it very difficult for observers to obtain useful information from the video. Due to the small payload of small-UAVs, it is a priority to improve the image quality by means of electronic image stabilization. But when small-UAV makes a turn, affected by the flight characteristics of it, the video is easy to become oblique. This brings a lot of difficulties to electronic image stabilization technology. Homography model performed well in the oblique image motion estimation, while bringing great challenges to intentional motion estimation. Therefore, in this paper, we focus on solve the problem of the video stabilized when small-UAVs banking and turning. We attend to the small-UAVs fly along with an arc of a fixed turning radius. For this reason, after a series of experimental analysis on the flight characteristics and the path how small-UAVs turned, we presented a new method to estimate the intentional motion in which the path of the frame center was used to fit the video moving track. Meanwhile, the image sequences dynamic mosaic was done to make up for the limited field of view. At last, the proposed algorithm was carried out and validated by actual airborne videos. The results show that the proposed method is effective to stabilize the oblique video of small-UAVs.
The small unmanned Aerial Vehicles (UAVs) are more popular because of their lower flight height, shorter flight period and continuous affordability. However, the small UAVs are sensitive to wind and airstream during the flight. The videos are often characterized by jitter, so the effective image electronic stabilization is important. In this paper, firstly, the flight characteristics of small UAVs were summarized and analyzed. Secondly, we analyzed the following problems: 1) under condition of drifts, the intentional motion estimation is not easy and much information will lost if motion compensation is not conducted properly; 2) on the situation of large tilt angle, the motions of images are complicate, simple motion models are not suitable. In order to cope with these problems, corresponding algorithms were proposed. Finally, we conducted some experiments; the results indicated that our methods were effective.
As a new type of aviation remote sensing earth observation system, the UAVRSS (Unmanned Aerial Vehicle remote sensing system) is used in civil remote sensing field more and more. In order to improve the efficiency of the remote sensing image processing and making the Orthophoto of the UAVRSS, in this paper one method is presented to improve the precision of the Orthophoto without the Ground Control Point(GCP) and the high precision sensors, such as the POS and the IMU. Through some real flying experiments of the UAVRSS, the data and the images were obtained. These data and the images were analyzed by the method. The result shows that the precision of the Orthophoto.
Due to the affection of large moving object in the video image, the ordinary image stabilization algorithms can't get
precise motion vector of the image. In this paper, a new image stabilization method that explicitly deals with video
images containing large moving object is given out. It detects a rough area containing moving object first. Then this area
will be got rid of from source image. Finally, a feature area taken from the rest part is used to calculate the image motion.
For the affection of moving object has been eliminated, motion vector's precision of the image is improved greatly.
A new image corner detection algorithm is proposed in this paper based on the SUSAN algorithm for the electronic
image stabilization of the UAV video image. Through analyzing the gray characteristics of the image of the UAV, the
new algorithm changed the judge criteria of the SUSAN corner detection algorithm to increase the accuracy and velocity
of the image processing. The basic steps of the algorithmic show as follow: First, the correct threshold is decided using
the gray characteristic. Second, comparing the one pixel with the eight neighborhood pixels, the elementary direction of
corner is acquired. Last, a corner is acquired through calculating the number of the congener pixels based on these new
directions.
Using this new algorithm the corners should be detected fast and efficiently. Experimental results of the UAV video
image processing show that the new method can highly increase computational velocity. Consequently the proposed
algorithm meets the need for real-time image processing.
With maturation of UAV (Unmanned Aerial Vehicle) key techniques, the UAV aviation is more stable than before. That
shows us the possibility of reconnaissance in atrocious environment. Here the structure of an UAV remote sensing
platform is given out first. Then the control system modules of aerial remote sensing and their functions with each
realization are discussed in detail. The experiments show that the system can satisfy the needs for aerial remote sensing
task.
An approach of fast image mosaic is presented, which involves image matching and image intensity smoothing. Image
matching includes two procedures, i.e. rough matching and fine matching. In rough matching, the overlapped regions of
two adjacent images to be mosaicked are segmented to binary image at first. Then the binary images are filtered by open
operation of mathematic morphologic method. In the binary image region of the reference image, feature template is
searched and extracted on a given rule. Via XOR operation of the feature template and search region, some possible
matching positions in the overlapped region of the other image are got. In the fine matching, the sequential similarity
detection algorithm (SSDA) is adopted to perform matching computation in the small regions near the positions got in
the rough matching, and then the relative position offsets in X-orientation and Y-orientation between the two adjacent
images are got. Based on the result of the image matching, the two images are stitched. An approach of seam-line
smoothing is adopted to adjust the intensity of the overlapped area. Simulation experimental results show that the
approach greatly improves the operation speed, while the precision remains fine, so it can be applied in real-time
mosaicking.
As a new type of aviation remote sensing earth observation system, the UAVRSS (Unmanned Aerial Vehicle Remote Sensing System) has some special characteristics for getting the digital remote sensing images. Through some real flying experiments of the UAVRSS, the data and the images were obtained. These data and the images were analyzed by three methods from three aspects in this paper. The result shows that it is viable that using the UAV with remote sensing system to achieve the remote sensing works.
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