Owing to the shortages of inconvenience, expensive and high professional requirements etc. for conventional recognition devices of wheat leaf diseases, it does not satisfy the requirements of uploading and releasing timely investigation data in the large-scale field, which may influence the effectiveness of prevention and control for wheat diseases. In this study, a fast, accurate, and robust diagnose system of wheat leaf diseases based on android smartphone was developed, which comprises of two parts—the client and the server. The functions of the client include image acquisition, GPS positioning, corresponding, and knowledge base of disease prevention and control. The server includes image processing, feature extraction, and selection, and classifier establishing. The recognition process of the system goes as follow: when disease images were collected in fields and sent to the server by android smartphone, and then image processing of disease spots was carried out by the server. Eighteen larger weight features were selected by algorithm relief-F and as the input of Relevance Vector Machine (RVM), and the automatic identification of wheat stripe rust and powdery mildew was realized. The experimental results showed that the average recognition rate and predicted speed of RVM model were 5.56% and 7.41 times higher than that of Support Vector Machine (SVM). And application discovered that it needs about 1 minute to get the identification result. Therefore, it can be concluded that the system could be used to recognize wheat diseases and real-time investigate in fields.
This paper presents a monotonic invariant intensity descriptor (MIID) via spectral embedding and nonsubsampled contourlet transform (NSCT). To make the proposed descriptor discriminative, NSCT is used for the construction of multiple support regions. Specifically, the directed graph and the spectral feature vectors of the signless Laplacian matrix are exploited to construct the MIID. We theoretically demonstrate that the proposed descriptor is able to tackle monotonic illumination changes and many other geometric and photometric transformations. We conduct extensive experiments on the standard Oxford dataset and the complex illumination dataset to demonstrate the superiority of proposed descriptor over the existing state-of-the-art descriptors in dealing with image blur, viewpoint changes, illumination changes, and JPEG compression.
Camera calibration is a necessary step in 3D computer vision in order to extract metric information from 2D images. In this paper, using the homography between a plane in the scene and the plane of an image, we propose an alternative method on camera calibration with one-dimensional objects, and give both a linear and nonlinear solutions for the camera intrinsic parameters. Experimental results show that our approach also has a high accuracy.
A new algorithm of camera self-calibration is proposed in this paper according to the situation that the intrinsic camera parameters remain unchanged during the image shooting. The advantage of this algorithm is that it does not need to make any prior assumption for the intrinsic camera parameters. We use conjugate gradient method to estimate the unknown scale factors in Kruppa equations; and then solve Kruppa equations linearly by the estimated scale factors and calibrate the intrinsic camera parameters. The validity of the proposed algorithm has been confirmed by experiments.
In this paper, we propose a flexible new technique to calibrate a camera easily. It is well suited for use without specialized knowledge of 3D geometry or computer vision. The technique only require the camera to observe a planar pattern shown at a few different orientations. Either the camera or the planar can be freely moved. And the motion need not be known. The whole procedure can be done automatically. Both computer simulation and real data have been used to test the proposed technique, and prove that the method has some effect.
In the corse of completing the hierarchical reconstruction, recovering projective depth is the key step. The existence methods are very efficient for simulation data, but they are not perfect for real image. In this paper, the estimation of projective depths based on genetic algorithm is proposed, by which the projective depths are iteratively estimated so that the measurement matrix is made to be as close as possible to rank 4. The validity of the proposed algorithm is confirmed by experiments.
The POCS method was original developed in 1960's. It is applied in many fields such as: image processing, signal recovery and optics. The POCS method allows us to incorporate into iteration scheme available information about the experimental data and the measurement error as well as priori constraints based on physical reasoning. It is important to note that the POCS-method doesn't lead to a unique `optimum' solution. The next step to projection is to find a optimal method within a `solution space'. Based on synergetic theory founded by Haken in 1970's, this optimal problem can be resolved by synergetic pattern recognition procedure. In our paper, we propose a synergetic pattern recognition approach to accomplish the optimal processing.
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