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This paper proposes a key motion spotting method which is designed to locate the motion of interest (or key motion) from a database of motion sequences. As a key motion could be just a subsequence of the stored motion sequence, the proposed method differs much from the general methods of content-based retrieval of motion sequences, which searches from the database the individual sequences similar...
In biomedical image analysis, object description and classification tasks are very common. Our work relates to the problem of classification of Human Epithelial (HEp-2) cells. Since the crucial part of each classification process is the feature extraction and selection, much attention should be concentrated to the development of proper image descriptors. In this article, we introduce a new efficient...
In recent years, pedestrian detection for Automobile Driver Assistance System (ADAS) is a primordial task in the smart vehicle. Histogram of oriented gradients (HoG) is one of the most effective pedestrian feature extraction approaches to the study. In this paper, an optimization of pedestrian detection based on HOG method is presented and investigated to achieve an accurate human detection system...
This paper affords a method for the automatic multi-agent event recognition (M-AER) of snatch theft event. M-AER is known to be difficult since parameters such as motion, background, appearance, illumination etc. are constantly changing. In this work, motion vector flow (MVF) and directional motion histogram (DMH) that capture the interaction between two persons in a video sequence are being proposed...
Effective object and scene classification and indexing depend on extraction of informative image features. This paper shows how large families of complex image features in the form of subgraphs can be built out of simpler ones through construction of a graph lattice--a hierarchy of related subgraphs linked in a lattice. Robustness is achieved by matching many overlapping and redundant subgraphs, which...
The classification of diffuse lung opacities in high resolution computed tomography(HRCT) images is an important step for developing a computer-aided diagnosis(CAD) system. In designing the CAD system for classifying diffuse lung opacities in HRCT images, a histogram feature vector approach has been shown to be effective. In order to improve further the classification performance of the CAD system,...
This paper reports a study on methods for real-time speaker identification using the output from an event-based silicon cochlea. These methods are evaluated based on the amount of computation that needs to be performed and the classification performance in a speaker identification task. It uses the binaural AEREAR2 silicon cochlea, with 64 frequency channels and 512 output neurons. Auditory features...
The selection of suitable features and their parameters for the classification of three-dimensional laser range data is a crucial issue for high-quality results. In this paper we compare the performance of different histogram descriptors and their parameters on three urban datasets recorded with various sensors—sweeping SICK lasers, tilting SICK lasers and a Velodyne 3D laser range scanner. These...
This paper addresses the problem of recognizing rotated characters in scene and estimating the rotation angle using Weighted Direction Code Histogram (WDCH) and Modified Quadratic Discriminant Function (MQDF). In our previous paper [1], we proposed a rotation-free character recognition method and confirmed the feasibility of real world application. We also applied our method to scene analysis to detect...
In this paper, we develop an effective framework to predict in-hospital mortality (IHM) during an intensive care unit (ICU) stay, on the basis of specific medical variables. This work involves both binary mortality predictions and mortality risk estimates, corresponding to Event-1 and Event-2 of the Computing in Cardiology (CinC) Challenge 2012. Our proposed framework contains 1) feature extraction...
In this study, a new feature-based steganalytic method is presented and four classification methods: Fisher linear discriminant, Gaussian naive Bayes, multilayer perceptron, and k nearest neighbor, are compared for steganalysis of suspicious images. The method exploits statistics of the histogram, wavelet statistics, amplitudes of local extrema from the ID and 2D adjacency histograms, center of mass...
This paper presents a new bag-of-words based algorithm for object recognition. Our algorithm also includes five steps: feature detection and representation, codebook generation, learning and recognition. All features are extracted as dense grids of images instead of interest point for computationally efficiency and effectiveness. While features are described by histograms of oriented gradients (HOG)...
LPP (locality preserving projection), as a linear version of manifold learning algorithm, has attracted considerable interests in recent years. For real time applications, the response time is required to be as short as possible. In this paper, a new local image descriptor-LPP-HOG (histograms of oriented gradients) for fast human detection is presented. We employ HOG features extracted from all locations...
Vector fields may come from video data (via optical flow and tracking), from weather phenomena (e.g., wind speed and direction), and from medical imaging. An important component in analyzing this data is to be able to quantitatively compare different points within a vector field or across different fields of the same type. We present a novel local descriptor to compare individual points in a vector...
Straightforward classification using kernelized SVMs requires evaluating the kernel for a test vector and each of the support vectors. For a class of kernels we show that one can do this much more efficiently. In particular we show that one can build histogram intersection kernel SVMs (IKSVMs) with runtime complexity of the classifier logarithmic in the number of support vectors as opposed to linear...
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