Digital image processing has been applied to diverse industry disciplines. The wide application of digital image processing can be attributed to the following advantages: accuracy, objectivity, fastness, consistency, ...
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ISBN:
(纸本)9781601321916
Digital image processing has been applied to diverse industry disciplines. The wide application of digital image processing can be attributed to the following advantages: accuracy, objectivity, fastness, consistency, and the mixture of them. Although the RUst Defect Assessment(RUDA) method [1] was efficiently used to differentiate defective images from non-defective images on blue color coat, its effect on other colors or under specific environmental conditions has not yet been explored. A new steel bridge rust defect recognition method which hybrids Fourier Transform and Color Image Processing is proposed in this paper for automatically recognizing rust defects and adapting to various background colors.
In this paper, we focus on discrete expression classification using dynamic 3D sequences (4D data) recording the facial movements. A robust approach for registering 4D data is proposed and a variant of local binary pa...
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ISBN:
(纸本)9781467300636
In this paper, we focus on discrete expression classification using dynamic 3D sequences (4D data) recording the facial movements. A robust approach for registering 4D data is proposed and a variant of local binary patterns on three orthogonal planes is used for feature extraction. We present a fully automatic facial expression recognition pipeline. The system was evaluated on the publicly available facial expression database BU-4DFE and promising results were obtained.
Clothes patternrecognition is a challenging task for blind or visually impaired people. Automatic clothes patternrecognition is also a challenging problem in computervision due to the large pattern variations. In t...
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ISBN:
(纸本)9781450306164
Clothes patternrecognition is a challenging task for blind or visually impaired people. Automatic clothes patternrecognition is also a challenging problem in computervision due to the large pattern variations. In this paper, we present a new method to classify clothes patterns into 4 categories: stripe, lattice, special, and patternless. While existing texture analysis methods mainly focused on textures varying with distinctive pattern changes, they cannot achieve the same level of accuracy for clothes patternrecognition because of the large intra-class variations in each clothes pattern category. To solve this problem, we extract both structural feature and statistical feature from image wavelet subbands. Furthermore, we develop a new feature combination scheme based on the confidence margin of a classifier to combine the two types of features to form a novel local image descriptor in a compact and discriminative format. The recognition experiment is conducted on a database with 627 clothes images of 4 categories of patterns. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art texture analysis methods in the context of clothes patternrecognition. Copyright 2011 ACM.
In this research, we develop and integrate methods for real-time streaming audio classification based on psychoacoustic models of hearing as well as techniques in patternrecognition. Specifically, a framework for aud...
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ISBN:
(纸本)9781457701214;9781457701221
In this research, we develop and integrate methods for real-time streaming audio classification based on psychoacoustic models of hearing as well as techniques in patternrecognition. Specifically, a framework for auditory event detection and signal description by means of computervision approach has been designed to enable real-time processing and classification of audio signals present in home environments. Local binary patterns are employed to describe the extracted sound blobs in the spectrogram. Experimental results show that the proposed approach is quite effective, achieving an overall recognition rate of 80-90% for 8 types of audio input. The performance degrades only slightly in the presence of noise and other interferences.
Variation in viewpoints poses significant challenges to action recognition. One popular way of encoding view-invariant action representation is based on the exploitation of epipolar geometry between different views of...
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ISBN:
(纸本)9781457703935
Variation in viewpoints poses significant challenges to action recognition. One popular way of encoding view-invariant action representation is based on the exploitation of epipolar geometry between different views of the same action. Majority of representative work considers detection of landmark points and their tracking by assuming that motion trajectories for all landmark points on human body are available throughout the course of an action. Unfortunately, due to occlusion and noise, detection and tracking of these landmarks is not always robust. To facilitate it, some of the work assumes that such trajectories are manually marked which is a clear drawback and lacks automation introduced by computervision. In this paper, we address this problem by proposing view invariant action matching score based on epipolar geometry between actor silhouettes, without tracking and explicit point correspondences. In addition, we explore multi-body epipolar constraint which facilitates to work on original action volumes without any pre-processing. We show that multi-body fundamental matrix captures the geometry of dynamic action scenes and helps devising an action matching score across different views without any prior segmentation of actors. Extensive experimentation on challenging view invariant action datasets shows that our approach not only removes long standing assumptions but also achieves significant improvement in recognition accuracy and retrieval.
We analyse the potential of Gibbs Random Fields for shape prior modelling. We show that the expressive power of second order GRFs is already sufficient to express spatial relations between shape parts and simple shape...
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The quantitative evaluation of disparity maps is based on error measures. Among the existing measures, the percentage of Bad Matched Pixels (BMP) is widely adopted. Nevertheless, the BMP does not consider the magnitud...
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ISBN:
(纸本)9783642250842
The quantitative evaluation of disparity maps is based on error measures. Among the existing measures, the percentage of Bad Matched Pixels (BMP) is widely adopted. Nevertheless, the BMP does not consider the magnitude of the errors and the inherent error of stereo systems, in regard to the inverse relation between depth and disparity. Consequently, different disparity maps, with quite similar percentages of BMP, may produce 3D reconstructions of largely different qualities. In this paper, a ground-truth based measure of errors in estimated disparity maps is presented. It offers advantages over the BMP, since it takes into account the magnitude of the errors and the inverse relation between depth and disparity. Experimental validations of the proposed measure are conducted by using two state-of-the-art quantitative evaluation methodologies. Obtained results show that the proposed measure is more suited than BMP to evaluate the depth accuracy of the estimated disparity map.
This paper presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transforms for the first time. We introduce a new di...
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This paper presents a novel compact description of a pattern based on the interference of circular waves. The proposed approach, called "interference description", leads to a representation of the pattern, w...
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ISBN:
(纸本)9783642193088
This paper presents a novel compact description of a pattern based on the interference of circular waves. The proposed approach, called "interference description", leads to a representation of the pattern, where the spatial relations of its constituent parts are intrinsically taken into account. Due to the intrinsic characteristics of the interference phenomenon, this description includes more information than a simple sum of individual parts. Therefore it is suitable for representing the interrelations of different pattern components. We illustrate that the proposed description satisfies some of the key Gestalt properties of human perception such as invariance, emergence and reification, which are also desirable for efficient pattern description. We further present a method for matching the proposed interference descriptions of different patterns. In a series of experiments, we demonstrate the effectiveness of our description for several computervision tasks such as patternrecognition, shape matching. and retrieval.
This paper proposes a novel method for human action recognition. Different from many action recognition methods which consider an action sequence along the time axis, the proposed method views an action sequence along...
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ISBN:
(纸本)9781467300636
This paper proposes a novel method for human action recognition. Different from many action recognition methods which consider an action sequence along the time axis, the proposed method views an action sequence along the space axis. This brings two advantages: the human body structures in all frames are encoded in the feature;the time information is completely used. The process of feature extraction is as follows: first an action sequence is cut into slices parallel to the X-T plane. Every slice, we call X-T slice, is transformed to a mean histogram and a variance histogram along the T axis. Then all mean histograms and all variance histograms are concatenated separately to two vectors, and finally encoded with Mel Frequency Cepstrum Coefficient (MFCC). MFCC, a feature commonly used in speech recognition, can effectively capture changes of 1-D signals over time. The encoded values are sent to classifier for action recognition. Our system achieves very efficient result: it needs only 0.02 second to deal with a frame on average with Matlab.
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