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This paper describes a rose analysis and recognition system and presents the main principles used to realize the recognition system. The major principles presented in this paper are the mathematical description methods for rose features such as shape, size and color of the flower, petal, leaf, etc, and the object-oriented pattern recognition (OOPR) approach which mathematically deals with how to comprehensively use all different rose features rationally in the recognition scheme. The recognition system is described and some of its experimental results are given which demonstrate the efficiency of our methods.

This paper presents a novel pattern recognition application, the application in agronomy and proposes a roundness measurement concept to analyze the roundness of rose flower shapes for the new application. The rationality of this concept is theoretically analyzed and its mathematical definition is given. Experiment results show that this measurement is an efficient parameter for image shape analysis.

Image composition is a attractive domain and there are some classical composition technologies. Possion composition and gradient domain are some typical and traditional composition technologies. Their main function is to create plausible composited image. We can use the traditional methods to get a realistic visual results. However, after the composition we found that the details is not preserved, especially for the boundary of the composited image. We use some innovative methods to deal with the problem and there are two main stages in the paper. The first stage is that we introduce the model of our filter and compare it to the other filter. The second stage is that we use the filter and image pyramid to preserve the composited image's details after the image composition, especially for the boundary.

It is shown that images can be subject to lossy compression in such a way that introduced distortions are not visible. For this purpose, two modern visual quality metrics, MSSIM and PSNR-HVS-M, can be used. Their values are to be provided not less than 0.99 and 40 dB, respectively, and the corresponding lossy compression is to be carried out. Attained compression ratio (CR) depends upon image properties and a coder used. The proposed methodology of lossy compression can be successfully exploited in remote sensing and medical imaging with producing CR by several times larger than the best lossless image compression techniques.

Human motion capture typically requires synchronization hardware and specialized cameras, which makes the system expensive and technically complex. Instead, in this paper, we propose a novel approach for markerless motion capture with multiple unsynchronized cameras. Cameras synchronization is achieved using sharp illumination changes produced while recording the videos. To estimate poses with the presence of sharp illumination changes, we also propose a pose estimation method based on a bilayer Gaussian Mixture Model (GMM) and graph-cut, which can handle sharp illumination changes. And we demonstrate our video synchronization and markerless motion capture methods by the experiments on real human motion videos.

In this paper, we present an approach for markerless model-based full human-body motion capture using multi-view images as input. We extract volume data (voxels) representation from the silhouettes extracted from multiple-view video images by the method of shape from Silhouettes (SFS), and match our predefined human body model to the volume data. We construct an energy field in the volume of interest based on the volume data and human body model with pose parameters, and transform the matching to an energy minimizing problem. By dynamic graph cut, we get the minimum energy of certain pose parameters, and at last we optimize the pose parameters using Powell algorithm with a novel approach that uses the linear prediction guiding the optimization process and get the pose recovered. Through the test results on several video sequences of human body movements in an unaugmented office environment, we demonstrate the effectiveness and robustness of our approach.

This paper describes a rose variety recognition project and proposes the modified Fourier descriptor. Based on the modified descriptor, a new measurement, angle measurement, is proposed for image shape analysis. We use it for rose flower shape analysis and the experimental results show that this measurement is quite efficient for image shape analysis.

Presents an extended loop neural network approach to handwritten character recognition. Experiments show that this method is very effective. The recognition rate by this method is higher than that by a backpropagation network. >