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A reconstruction-based image processing algorithm is developed to automatically extract feature points of digitalized 2D objects. This algorithm, which is introduced using a bumblebee flight case, is made up of two parts: a four-connected dot chasing rearrangement scheme and an extreme point extraction on a polarized contour. It is then applied to a dune evolution case that is simulated with a cellular automation model. The results show that the proposed algorithm is effective in characterizing individual moving objects. An additional algorithm is developed to categorize the extracted feature points of a bumblebee with translucent wings.

In this paper, an edge point, line point, or curve point is called a feature point. A new approach to extract junction points and to describe feature points is proposed here. It accepts as input data a binary image resulting from a feature detector without thinning. In the binary image, each black point is first classified based on the number of lines passing through it and on a local property that the classes of its neighboring points are almost the same. Next, an aggregation method is presented to group those classified points into several segments. The orientation of each segment is kept either clockwise or counterclockwise. Conic curves are then used to describe these segments. Finally, junction points including corner points, cross points, branch points, and inflection points are located. It is worth mentioning that the proposed method does not use any thinning process and curvature information. The effectiveness of the approach is also verified by one illustrative example and two experimental results.

Coping with geometrical attacks in transform domain is crucial when we design a robust image watermarking scheme. In this paper, a novel contourlet-domain image watermarking scheme, which is robust to common signal processing and geometrical attacks, is proposed. First, the region with maximum energy in the directional subbands is considered for watermarking, i.e. the watermark can be embedded into the significant region as well as highly textured region of the host image. Then, for each coefficient of the selected subband, the strength factor was adaptively adjusted in terms of the energy of its parent and neighbor coefficients. Consequently, the tradeoff between the transparency and robustness of watermark can be achieved. Furthermore, the robust feature points, which can survive various signal processing and affine transformation, are extracted by using the Harris–Laplace detector. In watermark detection, the geometrical distortion of image is identified by using the feature template constructed during embedding phase, i.e. watermark resynchronization is performed. Experimental results show that the proposed watermark scheme is invisible and robust against common signal processing such as median filtering, sharpening, noise adding and JPEG compression, etc and geometrical attacks such as rotation, translation, scaling, row or column removal, shearing and local random bend, etc.

Motion estimation is the crucial step for video stabilization algorithm. There are many literatures focus on it and various methods have been proposed. The solution of this problem is now sophisticated when only replacement is taken into account. But if the rotation between two successive frames can't be ignored, conventional methods may lose their valid. In this paper, we aim to estimate motion parameters when replacement and rotation both exist in the video sequence. A framework based on feature points is employed. In order to find corresponding feature points in successive frames to estimate global motion parameters, a novel feature point match algorithm based on parameter space is proposed. It can endure great changes of the distribution of feature points and the processing speed is rather fast.