Rotation invariant texture descriptor plays an important role in texture-based object classification. However the classification accuracy may decrease due to the inconsistent performance of texture descriptor with respect to various rotated angles. In this paper we propose a consistent rotation invariant texture descriptor named Sorted Neighborhood Differences (SND). SND is derived from the integration of sorted neigh- borhood and binary patterns. Experimental results show that overall texture classification accuracy of SND with respect to different rotations using OUTEX TC 0010 texture database is 91.81% whereas those of LBPriu and LBP-HF are 86.42% and 88.28%, respectively. The texture and coin classification accuracies of SND are also consistent in various rotation angles and illumination levels.
This letter presents lower limb human motion detection using a surface electromyogram (sEMG) with a top and slope (TAS) feature extraction algorithm. Lower limb human motion detection using sEMG signal is generally divided into gait subphase detection, locomotion mode recognition, and mode change detection. Existing feature extraction algorithms using sEMG signal have several innate problems in recognizing lower limb human motion detection. With respect to time-domain features, their values may be analogous because two different gait subphases and locomotion mode may have similar muscle activity pattern. Therefore, it is not easy to select the proper feature set of sEMG signals. The TAS feature extraction algorithm reflects better timing characteristics of sEMG signals than the existing time-domain feature extraction algorithm. Therefore, it can provide high accuracy in lower limb human motion detection. Experimental results show that the average detection accuracy values of the proposed method in terms of the gait subphase detection, locomotion mode recognition, and mode change detection are increased by 8%, 5%, and 4% better than those of feature values of the Willison amplitude, respectively.
We present a relevance feedback approach based on multi-class support vector machine (SVM) learning and cluster-merging which can significantly improve the retrieval performance in region-based image retrieval. Semantically relevant images may exhibit various visual characteristics and may be scattered in several classes in the feature space due to the semantic gap between low-level features and high-level semantics in the user's mind. To find the semantic classes through relevance feedback, the proposed method reduces the burden of completely re-clustering the classes at iterations and classifies multiple classes. Experimental results show that the proposed method is more effective and efficient than the two-class SVM and multi-class relevance feedback methods.
According to rapid development of communication and video compression technology, the video contents are easily disseminated into various areas. In order to prevent illegal distribution of video contents, the necessity of video contents protection technology is increased. In this paper, we propose the video encryption and decryption algorithm based on logistic map and design the prototype system in real-time embedded environment. The sender system with encryption capability is implemented on DM642 EVM target board and the receiver system with decryption capability is implemented in satellite D-STB. Experimental results show that the time change ratio of encoding process on target board are less than 0.97% and the time change ratio of the decoding process on D-STB is less than 1.75%. So, we verify that the proposed encryption/decryption system can be used in real time application.
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