A method of binary trademark retrieval based on sub-block images features is proposed in the paper. First, in order to
avoid the rotation effect, the trademark image's principal orientation is computed and the image is rotated accordingly.
Second, by way of locating the image's object region, the translation effect is removed as well. Then, the image's object
region is hierarchically partitioned into a quad tree of multilevel sub-block images. After that, the images' features are
extracted under different level of divisions, so that the multi-level shape descriptors can be gained. The experiments
prove the features based on sub-block images have strong invariability with respect to translation, scaling and rotation.
Besides, the features compromise image's local features and global features at the same time, therefore, the retrieval
results fit human visual perfect well.
Trademarks' retrieval has obtained more and more attention in recent research on content-based management and utilization of image database system. To retrieve the images, the key is to get the shape features. In this paper, a new method for extracting shape features of trademarks is presented. Based on the theory of information, the method uses images' entropy and invariant moments to capture the shape and spatial information of images. The algorithm is easy and the experimental results show its invariability with respect to translation, scale and rotation of objects. What's more, it also have the noise invariance.
In this paper, trademark is regarded as a combination of several geometric regions which have sharp edges. To retrieve such a combination, a method based on its member regions' shape and spatial features is proposed. Since the way considers the shape feature adn spatial relationship at the same time, so it can ensure the consistency in both the local and whole sides. Compared with the method of only using shape feature to retrieve the trademark, the results of experimentshow this way has higher precision and the output accords with people's visual feeling better.
In this paper, we describe high accurate non-contact measuring equipment together with its principle, hardware structure and idea for software design. Its principle is using optical modulation to modulate and amplify the figure of fibers' width, then convert the optical signa into electrical signal. The electrical signal is processed by an automatic processor which comprises MCS-51 single-chip microprocessor and other interface circuits. Finally the processed data is displayed or printed. This equipment can exclude the system error with calibration. Multivariate linear regression algorithm is used in calibration software design. It can discriminate the overlapping and across of fibers and eliminate the system errors. All proceeding is automatically completed without the interference of people. It excludes the personal influence and ensures the reliability of measurement.
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