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Ultrasonogram plays an important role for medical diagnosis because its convenience, easy to use, low cost in compared with other medical modalities. Ultrasound image analysis is mainly used in real-time but sometimes offline data is used for future diagnosis. In this paper, we analyze the artery pulsation caused by blood flow in neonatal brain using discrete wavelet transform. We quantitatively analyze...
Cranial ultrasound scans are very essential part of the routine investigation of neonatal intensive care. The scan ultrasound image sequence are not only used for real-time diagnosis but also recorded for future diagnosis. In this paper, we analyze and visualize the pulsation amplitude caused by artery pulsation of blood flows from a video stream of ultrasound image sequence by using 1D fast Fourier...
In this paper, we propose spatio-temporal silhouette representations, called silhouette energy image (SEI) and silhouette history image (SHI) to characterize motion and shape properties for recognition of human movements such as human actions, activities in daily life. The SEI and SHI are constructed by using the silhouette image sequence of an action. The span or difference of the end time and start...
In this paper, a method for the detection and segmentation of human motion in moving image sequences is presented. For detecting motion, the intensity of each pixel is convolved with the second derivative of temporal Gaussian smoothing function or temporal Laplacian of Gaussian (LoG) filter. The zero-crossing in a single frame of the resulting function indicates the positions of moving edges. Intensity...
Tissue-motions can represent the strength of artery pulsation of a newborn baby for pediatrics diagnosis. In this paper, we propose a novel method to estimate the tissue-motion quantitatively by combining local and global motion estimation methods, namely combined local-global (CLG) optical flow technique in cranial ultrasonogram of newborn babies. Tissue motions are estimated in different coronal...
In this paper, we propose a spatio-temporal silhouette representation, called silhouette energy image (SEI), and variability models, to characterize motion and shape properties for automatic recognition of human actions in daily life. To address the variability in the recognition of human actions, several parameters, such as anthropometry of the person, speed of the action, phase (starting and ending...
In this paper, we present a novel method for human action recognition from any arbitrary view image sequence that uses the Cartesian component of optical flow velocity and human body silhouette feature vector information. We use principal component analysis (PCA) to reduce the higher dimensional silhouette feature space into lower dimensional feature space. The action region in an image frame represents...
Recognizing human action from image sequences is an active area of research in computer vision. In this paper, we present a novel method for human action recognition from image sequences in different viewing angles that uses the Cartesian component of optical flow velocity and human body shape feature vector information. We use principal component analysis to reduce the higher dimensional shape feature...
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