Holoscopic 3D imaging is a true 3D imaging system mimics fly’s eye technique to acquire a true 3D optical model of a real scene. To reconstruct the 3D image computationally, an efficient implementation of an Auto-Fea...Holoscopic 3D imaging is a true 3D imaging system mimics fly’s eye technique to acquire a true 3D optical model of a real scene. To reconstruct the 3D image computationally, an efficient implementation of an Auto-Feature-Edge (AFE) descriptor algorithm is required that provides an individual feature detector for integration of 3D information to locate objects in the scene. The AFE descriptor plays a key role in simplifying the detection of both edge-based and region-based objects. The detector is based on a Multi-Quantize Adaptive Local Histogram Analysis (MQALHA) algorithm. This is distinctive for each Feature-Edge (FE) block i.e. the large contrast changes (gradients) in FE are easier to localise. The novelty of this work lies in generating a free-noise 3D-Map (3DM) according to a correlation analysis of region contours. This automatically combines the exploitation of the available depth estimation technique with edge-based feature shape recognition technique. The application area consists of two varied domains, which prove the efficiency and robustness of the approach: a) extracting a set of setting feature-edges, for both tracking and mapping process for 3D depthmap estimation, and b) separation and recognition of focus objects in the scene. Experimental results show that the proposed 3DM technique is performed efficiently compared to the state-of-the-art algorithms.展开更多
This study examines perceptions of music depth by exploring its relationships to different music features.First,a correlation analysis shows that the perceived depth of music is negatively correlated with valence and ...This study examines perceptions of music depth by exploring its relationships to different music features.First,a correlation analysis shows that the perceived depth of music is negatively correlated with valence and arousal and is also related to different music features,including tempo,Mel-frequency cepstrum coefficients,chromagrams,spectral centroids,spectral bandwidth,spectral contrast,spectral flatness,spectral roll-off,and tonal centroid features.Applying machine learning methods,we find that selected music features can predict perceptions of music depth,and a random forest regression(RFR)is found to perform best in this study.Finally,a feature importance analysis shows that the principal component of spectral contrast dominates the RFR-based music depth recognition model,showing that deep music usually has clear and narrow-band audio signals.展开更多
文摘Holoscopic 3D imaging is a true 3D imaging system mimics fly’s eye technique to acquire a true 3D optical model of a real scene. To reconstruct the 3D image computationally, an efficient implementation of an Auto-Feature-Edge (AFE) descriptor algorithm is required that provides an individual feature detector for integration of 3D information to locate objects in the scene. The AFE descriptor plays a key role in simplifying the detection of both edge-based and region-based objects. The detector is based on a Multi-Quantize Adaptive Local Histogram Analysis (MQALHA) algorithm. This is distinctive for each Feature-Edge (FE) block i.e. the large contrast changes (gradients) in FE are easier to localise. The novelty of this work lies in generating a free-noise 3D-Map (3DM) according to a correlation analysis of region contours. This automatically combines the exploitation of the available depth estimation technique with edge-based feature shape recognition technique. The application area consists of two varied domains, which prove the efficiency and robustness of the approach: a) extracting a set of setting feature-edges, for both tracking and mapping process for 3D depthmap estimation, and b) separation and recognition of focus objects in the scene. Experimental results show that the proposed 3DM technique is performed efficiently compared to the state-of-the-art algorithms.
基金Zhejiang University of Technology’s Social Science Research Funding,Grant/Award Number:GZ20511080007。
文摘This study examines perceptions of music depth by exploring its relationships to different music features.First,a correlation analysis shows that the perceived depth of music is negatively correlated with valence and arousal and is also related to different music features,including tempo,Mel-frequency cepstrum coefficients,chromagrams,spectral centroids,spectral bandwidth,spectral contrast,spectral flatness,spectral roll-off,and tonal centroid features.Applying machine learning methods,we find that selected music features can predict perceptions of music depth,and a random forest regression(RFR)is found to perform best in this study.Finally,a feature importance analysis shows that the principal component of spectral contrast dominates the RFR-based music depth recognition model,showing that deep music usually has clear and narrow-band audio signals.
