781 research outputs found
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure
Robust Recognition using L1-Principal Component Analysis
The wide availability of visual data via social media and the internet, coupled with the demands of the security community have led to an increased interest in visual recognition. Recent research has focused on improving the accuracy of recognition techniques in environments where variability is well controlled. However, applications such as identity verification often operate in unconstrained environments. Therefore there is a need for more robust recognition techniques that can operate on data with considerable noise.
Many statistical recognition techniques rely on principal component analysis (PCA). However, PCA suffers from the presence of outliers due to occlusions and noise often encountered in unconstrained settings. In this thesis we address this problem by using L1-PCA to minimize the effect of outliers in data. L1-PCA is applied to several statistical recognition techniques including eigenfaces and Grassmannian learning. Several popular face databases are used to show that L1-Grassmann manifolds not only outperform, but are also more robust to noise and occlusions than traditional L2-Grassmann manifolds for face and facial expression recognition. Additionally a high performance GPU implementation of L1-PCA is developed using CUDA that is several times faster than CPU implementations
SIRU development. Volume 1: System development
A complete description of the development and initial evaluation of the Strapdown Inertial Reference Unit (SIRU) system is reported. System development documents the system mechanization with the analytic formulation for fault detection and isolation processing structure; the hardware redundancy design and the individual modularity features; the computational structure and facilities; and the initial subsystem evaluation results
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