Parallel concatenated spa ce time trellis code modulation, called Turbo STCM, can efficiently increase the coding gains of the space time codes. However, the complexity of the iterat iv e decoding restricts its ap...Parallel concatenated spa ce time trellis code modulation, called Turbo STCM, can efficiently increase the coding gains of the space time codes. However, the complexity of the iterat iv e decoding restricts its application. This paper introduces a lower complex deco ding algorithm based on soft output Viterbi algorithm (SOVA) for Turbo STCM. S imulational results show that the new SOVA algorithm for the Turbo STCM outperf orms the original space time trellis code (STTC) by 4~6 dB. At the same time, compared with the Max Log MAP (maximum a posteriori) algorithm, the new scheme requires a lower complexity and approaches the performance of Turbo STCM decod ing w ith Max Log MAP.展开更多
Cascaded regression has been recently applied to reconstruct 3D faces from single 2D images directly in shape space, and has achieved state-of-the-art performance. We investigate thoroughly such cascaded regression ba...Cascaded regression has been recently applied to reconstruct 3D faces from single 2D images directly in shape space, and has achieved state-of-the-art performance. We investigate thoroughly such cascaded regression based 3D face reconstruction approaches from four perspectives that are not well been studied： （1） the impact of the number of 2D landmarks; （2） the impact of the number of 3D vertices; （3） the way of using standalone automated landmark detection methods; （4） the convergence property. To answer these questions, a simplified cascaded regression based 3D face reconstruction method is devised. This can be integrated with standalone automated landmark detection methods and reconstruct 3D face shapes that have the same pose and expression as the input face images, rather than normalized pose and expression. An effective training method is also proposed by disturbing the automatically detected landmarks. Comprehensive evaluation experiments have been carried out to compare to other 3D face reconstruction methods. The results not only deepen the understanding of cascaded regression based 3D face reconstruction approaches, but also prove the effectiveness of the proposed method.展开更多
文摘Parallel concatenated spa ce time trellis code modulation, called Turbo STCM, can efficiently increase the coding gains of the space time codes. However, the complexity of the iterat iv e decoding restricts its application. This paper introduces a lower complex deco ding algorithm based on soft output Viterbi algorithm (SOVA) for Turbo STCM. S imulational results show that the new SOVA algorithm for the Turbo STCM outperf orms the original space time trellis code (STTC) by 4~6 dB. At the same time, compared with the Max Log MAP (maximum a posteriori) algorithm, the new scheme requires a lower complexity and approaches the performance of Turbo STCM decod ing w ith Max Log MAP.
基金Project supported by the National Key Research and Development Program of China(Nos.2017YFB0802303and 2016YFC0801100)the National Key Scientific Instrument and Equipment Development Projects of China(No.2013YQ49087904)+1 种基金the National Natural Science Foundation of China(No.61773270)the Miaozi Key Project in Science and Technology Innovation Program of Sichuan Province,China(No.2017RZ0016)
文摘Cascaded regression has been recently applied to reconstruct 3D faces from single 2D images directly in shape space, and has achieved state-of-the-art performance. We investigate thoroughly such cascaded regression based 3D face reconstruction approaches from four perspectives that are not well been studied： （1） the impact of the number of 2D landmarks; （2） the impact of the number of 3D vertices; （3） the way of using standalone automated landmark detection methods; （4） the convergence property. To answer these questions, a simplified cascaded regression based 3D face reconstruction method is devised. This can be integrated with standalone automated landmark detection methods and reconstruct 3D face shapes that have the same pose and expression as the input face images, rather than normalized pose and expression. An effective training method is also proposed by disturbing the automatically detected landmarks. Comprehensive evaluation experiments have been carried out to compare to other 3D face reconstruction methods. The results not only deepen the understanding of cascaded regression based 3D face reconstruction approaches, but also prove the effectiveness of the proposed method.
