Lifting Wavelet Based Cognitive Vision System

This paper presents a cognitive vision system based on the learning of lifting wavelets. The learning process consists of four steps: 1. Extract training and query object images automatically from adjacent video frames using our proposed cosine-maximization method; 2. Compute autocorrelation vectors from the extracted training images, and their discriminant vectors by linear discriminant analysis; 3. Map the autocorrelation vectors onto the discriminant vector space to obtain feature vectors; 4. Learn lifting parameters in the feature vectors using the idea of discriminant analysis. The recognition of a query object is performed by measuring cosine distance between its feature vector and the feature vectors for training object images. Our experimental results on vehicle types recognition show that the proposed system performs better than the discriminant analysis of original images

Lifting Wavelet Based Cognitive Vision System

This paper presents a cognitive vision system based on the learning of lifting wavelets. The learning process consists of four steps: 1. Extract training and query object images automatically from adjacent video frames using our proposed cosine-maximization method; 2. Compute autocorrelation vectors from the extracted training images, and their discriminant vectors by linear discriminant analysis; 3. Map the autocorrelation vectors onto the discriminant vector space to obtain feature vectors; 4. Learn lifting parameters in the feature vectors using the idea of discriminant analysis. The recognition of a query object is performed by measuring cosine distance between its feature vector and the feature vectors for training object images. Our experimental results on vehicle types recognition show that the proposed system performs better than the discriminant analysis of original images

断続的なREM断眠ストレス負荷誘発性マウス腸管輸送能の亢進

Clinical reports have shown that irritable bowel syndrome (IBS) is comorbid with anxiety and stress-related events. The treatment of rapid eye movement (REM) sleep deprivation (REMSD) is a potent stressor in animals. Stress in humans commonly results in gastrointestinal dysfunction, which is characterized by its symptomatology because the etiology is completely unknown. In the present study, we found that intermittent REMSD treatment (20 h/day) for 3 days markedly increased gastrointestinal transit of charcoal meal in mice. The accelerated gastrointestinal transit was significantly inhibited by both diazepam (1 and 2 mg/kg, p.o.) and by a peripheral , μ-opioid receptor agonist loperamide (3 and 10 mg/kg, p.o.), which are clinically used to treat diarrhea-predominant IBS. The ID_ values of loperamide in cage-control (CC) mice and intermittent REM sleep deprived mice were 18.14mg/kg and 6.78mg/kg, respectively. These results indicate that intermittent REMSD treatment increases gastrointestinal transit; this model may be useful for evaluating the effects of drugs on diarrhea-predominant IBS, and the supersensitivity of peripheral , μ-opioid receptor may be involved in the accelerated gastrointestinal transit

Robust person authentication using dyadic wavelet filters learned by cosine-maximization

This paper produces a person authentication system using learned lifting dyadic wavelet filters. Our system has the capability of identifying persons whose facial image is changed the size and angle of an original image. The learning algorithm is done by training free parameters in the lifting filters so as to maximize the cosine between a vector whose components are the lifting filters and a vector of facial parts. This problem can be solved fast using Newton’s method. Applying the learned filters to a test image, facial parts in the image can be detected. Our detection of facial parts is robust for expansion, shrink and rotation of the image. A person is identified by checking the number of faces detected from video frames. Simulation results show that our person authentication is accurate and fast.