Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures. This dataset is dubbed as the corpus of social touch, where touch was performed on a mannequin arm. A leave-one-subject-out cross-validation method is used to evaluate system performance. The proposed method can recognize gestures in nearly real time after acquiring a minimum number of frames (the average range of frame length was from 0.2% to 4.19% from the original frame lengths) with a classification accuracy of 63.7%. The achieved classification accuracy is competitive in terms of the performance of existing algorithms. Furthermore, the proposed system outperforms other classification algorithms in terms of classification ratio and touch recognition time without data preprocessing for the same dataset.
Problem statement: Low complexity image compression algorithms are necessary for modern portable devices such as mobile phones, wireless sensor networks and high constraint power consumption devices. In such applications low bit rate along with an acceptable image quality are an essential requirements. Approach: This study proposes low and moderate complexity algorithms for colour image compression. Two algorithms will be presented; the first one is intensity based adaptive quantization coding, while the second is a combination of discrete wavelet transforms and the intensity based adaptive quantization coding algorithm. Adaptive quantization coding produces a good Peak Signal to Noise Ratio (PSNR), but with high bit rates compared with other low complex algorithms. The presented algorithms produce low bit rate whilst preserving the PSNR and image quality at an acceptable range. Results: Experiments were performed using different kinds of standard colour images, a multi level quantizer, different thresholds, different block sizes and different wavelet filters. Both algorithms considered the intensity variation of each colour plane. At high compression ratios the proposed algorithms produced 1-3 bpp bit rate reduction against the stand alone adaptive quantization coding for the same image quality. This reduction was achieved due to dropping of some blocks that claimed to be low intensity variation according to a comparison with predefined thresholds for each colour plane. The results show that the bit rate can be reduced by 72-88% for each low variation image block from the original bit rate. Conclusion: The results obtained show a good reduction in bit rate with the same PSNR, or a slightly less than PSNR of a standalone adaptive quantization coding algorithm. Further bit rate reduction has been achieved by decomposing the input image using different wavelet filters and intensity based adaptive quantization coding. The proposed algorithm comprises a number of parameters to control the performance of the compressed images.
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