The traditional method for dealing with the problem of epipolar rectification in the semi-calibrated
case is to use RANdom SAmple Consensus(RANSAC), which could not get a correct parameter when
exist serious mismatch points. So the weighted least square method is proposed to solve this problem.
First, extracting Scale Invariant Feature Transform(SIFT) and conducting initial feature matching for
image pairs. Next, according to the internal geometric relations of corresponding points, transforming the
problem into a maximum likelihood estimate problem. And then, each pair of corresponding points is
given weight, and the weight is regarded as a latent variable to stand for the precision of correct matching.
Finally, weighted least square method and Expectation Maximization(EM) algorithm are used to
estimate the latent variable and uncalibrated parameters. Experimental results show that propo- sed
method could not only keep rectified precision high, but also has slighter image morphing and faster
rectified velocity than state-of-the-art algorithms.
The Microsoft Kinect sensor has been widely used in many applications, but it suffers from the drawback of low registration precision between color image and depth image. In this paper, we present a robust method to improve the registration precision by a mixture model that can handle multiply images with the nonparametric model. We impose non-parametric geometrical constraints on the correspondence, as a prior distribution, in a reproducing kernel Hilbert space (RKHS).The estimation is performed by the EM algorithm which by also estimating the variance of the prior model is able to obtain good estimates. We illustrate the proposed method on the public available dataset. The experimental results show that our approach outperforms the baseline methods.
Proc. SPIE. 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
KEYWORDS: Principal component analysis, Detection and tracking algorithms, Cameras, Image segmentation, Digital cameras, Feature extraction, Ear, Manufacturing equipment, Space operations, RGB color model
In this paper, we proposed an image-based approach for automatic recognizing the flowering stage of maize. A modified HOG/SVM detection framework is first adopted to detect the ears of maize. Then, we use low-rank matrix recovery technology to precisely extract the ears at pixel level. At last, a new feature called color gradient histogram, as an indicator, is proposed to determine the flowering stage. Comparing experiment has been carried out to testify the validity of our method and the results indicate that our method can meet the demand for practical observation.
With the emergence of very high-resolution airborne synthetic aperture radar systems, it is necessary to reinvestigate these proposed methods with respect to their despeckling performances. As for the very high resolution polarimetric synthetic aperture radar (PolSAR) data, the presumption that the resolution cell is much larger than the radar wavelength becomes ineffective. Therefore, some classic and new filters are thoroughly reviewed. For the evaluation of speckle filters, both indicators for polarimetric information and spatial information are listed. The absolute relative bias is introduced, with the purpose of measuring the filtering performance concerning the indicators for polarimetric information. Moreover, the ratio of half power point width is employed to quantitatively assess the degree of point target preservation. A series of experiments are carried out based on the real PolSAR imagery which is obtained from an uninhabited aerial vehicle synthetic aperture radar system. It can be concluded that existing filters can only attain good performance with reference to part of the indicators. As regards very high-resolution PolSAR imagery, it is necessary to conceive more apposite new filters or make improved versions of the existing filters.
In this paper, we proposed an illumination-invariant crop extraction method based on specularity learning. Several useful contextual cues including object appearance and location inspired by recognition mechanism of human beings were introduced and integrated to machine learning architecture, generating a well-trained highlight region classifier. Combing with the Hue-intensity Look-up table and super-pixel techniques, the classifier gives the final extraction result. Comparing experiment confirmed the validity and feasibility of our method.
For image classification tasks, the region containing object which plays a decisive role is indefinite in both position and scale. In this case, it does not seem quite appropriate to use the spatial pyramid matching (SPM) approach directly. In this paper, we describe an approach for handling this problem based on region of interest (ROI) detection. It verifies the feasibility of using a state-of-the-art object detection algorithm to separate foreground and background for image classification. It first makes use of an object detection algorithm to separate an image into object and scene regions, and then constructs spatial histogram features for them separately based on SPM. Moreover, the detection score is used to rescore. Our contributions include: i) verify the feasibility of using a state-of-the-art object detection algorithm to separate foreground and background used for image classification; ii) a simple method, called <i>coarse object alignment matching</i>, is proposed for constructing histogram using the foreground and background provided by object localization. Experimental results demonstrate an obvious superiority of our approach over the standard SPM method, and it also outperforms many state-of-the-art methods for several categories.
Keywords: Unmanned Aerial Vehicles (UAVs) are been increasingly used in civilian and military domains. Vision-aided
inertial navigation system in UAV is studied by more and more researchers for it's non-contact, high accuracy and
stability. In this paper, an L1-Graph-based image matching approach, which constructs neighbouring system based on
sparse representation, is proposed for monocular motion vision measurement. Then, a scheme for amending the outputs
of inertial sensor for the velocity measurement is designed, which fuse the outputs from the downward-looking velocity
measurement and inertial sensor by Kalman filter. Experiments show this design form an accurate navigation solution.