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One-dimensional (1-D) and two-dimensional (2-D) frequency estimation for a single complex sinusoid in white Gaussian noise is a classic signal processing problem with numerous applications. It is revisited here through a new unitary principal-singular-vector utilization modal analysis (PUMA) approach, which is realized in terms of real-valued computations. The 2-D unitary PUMA is first formulated...
The fixed maximum acceleration and maneuvering frequency of current statistical model leads to the divergence of filtering algorithm. In this study, a new model which employs innovation dominated subjection function to adaptively adjust maximum acceleration and maneuvering frequency is proposed based on current statistical model. Although the new model has a better performance, a fluctuant phenomenon...
Semi-supervised learning and active learning are important techniques to build more accurate model while labeled data are scarce. The objective of this paper is combining both to effectively relieve user labor for multi-class annotation. We propose a novel graph-based active semi-supervised learning framework which aim at efficiently learning a multi-class model with minimal human labor. In particular,...
Alzheimer disease (AD) is known as the most common form of dementia, which imposes a considerable burden on society. In this paper, we focus on the automated diagnosis of Alzheimer disease. Based on the researches on neuropathology, we adopt the thickness of cortex regions from the magnetic resonance imaging (MRI) to characterize the pathology of AD. 3D reconstruction technique is utilized to extract...
Semi-supervised learning methods can largely leverage the image annotation problem using both labeled and unlabeled data, especially when the labeled information is quite limited. However, most of them suffer the expensive computation stemming from the batch learning on large training dataset. In this paper we proposed a highly efficient semi-supervised annotation approach with the partial label propagation...
In this paper, we propose a new algorithm for shape initialization and 3D pose alignment in Active Shape Model (ASM). Instead of initializing with average shape in previous works, we build a scatter data interpolation model from key points to obtain the initial shape, which ensures shape initialized around face organs. These key points are chosen from organs of face shape and located with a strong...
Crowd estimation is crucial for crowd monitoring and control. It differs from pedestrian detection or people counting in that no individual pedestrian can be properly segmented in the image. This paper describes a novel and efficient system for crowd density estimation, based on local image texture analysis. A novel indication of local binary pattern feature vector called Advanced LBP is proposed...
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