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In order to extract the gait sequence features better, this paper presents a method of feature extraction algorithm based on Fisher Discriminance. First, extract the profile silhouette by background cut off method, and then get the gait cycle by the change of their center of mass, at last convert the feature space. Then weight the gait characteristic matrix by Fisher Discriminance to make characteristics...
Autonomous operation of blast hole drill rigs requires monitoring of drilling parameters known as “Measurement While Drilling” (MWD) data. From these data, rock properties can be inferred. A supervised classification scheme is usually used to map MWD data inputs to rock type outputs given some labeled training data. However, the geology has no definite ground truth that can allow a reliable labeling...
A crucial component of an autonomous mine is the ability to infer rock types from mechanical measurements of a drill rig. The major difficulty lies in that there is not a clear one to one correspondence between the mechanical measurements and the rock type due to the mechanical noise as well as the variety of the rock geology. This paper proposes a novel wavelet feature space projection approach to...
Human action analysis has recently received growing interest from vision researchers. In this paper, we present a simple but effective approach for action recognition by combining multiple complementary features with Gaussian process classification. Since it is often insufficient for a single type of feature derived from action videos to characterize variations among different motions, we propose...
We improve Gaussian processes (GP) classification by reorganizing the (non-stationary and anisotropic) data to better fit to the isotropic GP kernel. First, the data is partitioned into two parts: along the feature with the highest frequency bandwidth. Secondly, for each part of the data, only the spectrally homogeneous features are chosen and used (the rest discarded) for GP classification. In this...
This paper investigates the applicability of Gaussian processes (GP) classification for recognition of articulated and deformable human motions from image sequences. Using tensor subspace analysis (TSA), space-time human silhouettes (extracted from motion videos) are transformed to low-dimensional multivariate time series, based on which structure-based statistical features are calculated to summarize...
We use spectral analysis to facilitate Gaussian processes (GP) classification. Our solution provides two improvements: scaling of the data to achieve a more isotropic nature, as well as a method to choose the kernel to match certain data characteristics. Given the dataset, from the Fourier transform of the training data we compare the frequency domain features of each dimension to estimate a rescaling...
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