Video-based gait recognition is a challenging problem in computervision. In this paper, fractal scale wavelet analysis is applied to describe and automatically recognize gait. Fractal scale based on wavelet analysis ...
详细信息
ISBN:
(纸本)0769525210
Video-based gait recognition is a challenging problem in computervision. In this paper, fractal scale wavelet analysis is applied to describe and automatically recognize gait. Fractal scale based on wavelet analysis represents the self-similarity of signals, and improves the flexibility of wavelet moments. Optimal wavelets based on generalized multi-resolution analysis are used to improve the recognition rate. Descriptors of fractal scale are translation, scale and rotation invariant. Moreover, a combination of fractal scale and wavelet moments improves the recognition rate. Experiments show that the proposed descriptor is efficient for gait recognition.
We consider the problem of depth-based robust 3D facial pose tracking under unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Unlike the previous depth-based discriminative or d...
详细信息
ISBN:
(纸本)9781538604571
We consider the problem of depth-based robust 3D facial pose tracking under unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Unlike the previous depth-based discriminative or data-driven methods that require sophisticated training or manual intervention, we propose a generative framework that unifies pose tracking and face model adaptation on-the-fly. Particularly, we propose a statistical 3D face model that owns the flexibility to generate and predict the distribution and uncertainty underlying the face model. Moreover, unlike prior arts employing the ICP-based facial pose estimation, we propose a ray visibility constraint that regularizes the pose based on the face model's visibility against the input point cloud, which augments the robustness against the occlusions. the experimental results on Biwi and ICT-3DHP datasets reveal that the proposed framework is effective and outperforms the state-of-the-art depth-based methods.
Person re-identification (ReID) is an important task in video surveillance and has various applications. It is non-trivial due to complex background clutters, varying illumination conditions, and uncontrollable camera...
详细信息
ISBN:
(纸本)9781538604571
Person re-identification (ReID) is an important task in video surveillance and has various applications. It is non-trivial due to complex background clutters, varying illumination conditions, and uncontrollable camera settings. Moreover, the person body misalignment caused by detectors or pose variations is sometimes too severe for feature matching across images. In this study, we propose a novel Convolutional Neural Network (CNN), called Spindle Net, based on human body region guided multi-stage feature decomposition and tree-structured competitive feature fusion. It is the first time human body structure information is considered in a CNN framework to facilitate feature learning. the proposed Spindle Net brings unique advantages: 1) it separately captures semantic features from different body regions thus the macro-and micro-body features can be well aligned across images, 2) the learned region features from different semantic regions are merged with a competitive scheme and discriminative features can be well preserved. State of the art performance can be achieved on multiple datasets by large margins. We further demonstrate the robustness and effectiveness of the proposed Spindle Net on our proposed dataset SenseReID without fine-tuning. (1)
this paper accounts the testing protocols used at the author's university. It examines the experiences of one particular study in dynamic signature verification. the paper also outlines some additions to the curre...
详细信息
ISBN:
(纸本)9810474806
this paper accounts the testing protocols used at the author's university. It examines the experiences of one particular study in dynamic signature verification. the paper also outlines some additions to the current testing methodologies.
this paper deals with knowledge extraction from visual data for content-based image retrieval of natural scenes. Images are analysed using a ridgelet transform that enhances information at different scales, orientatio...
详细信息
ISBN:
(纸本)354029032X
this paper deals with knowledge extraction from visual data for content-based image retrieval of natural scenes. Images are analysed using a ridgelet transform that enhances information at different scales, orientations and spatial localizations. the main contribution of this work is to propose a method that reduces the size and the redundancy of this ridgelet representation, by defining both global and local signatures that are specifically designed for semantic classification and content-based retrieval. An effective recognition system can be built when these descriptors are used in conjunction with a support vector machine (SVM). Classification and retrieval experiments are conducted on natural scenes, to demonstrate the effectiveness of the approach.
this paper presents a method for the automatic evaluation of the appearance of seam puckers (SP) on suits. Presently, evaluations are done by inspectors who compare standard photographs to test samples. We regard the ...
详细信息
this paper presents a method for the automatic evaluation of the appearance of seam puckers (SP) on suits. Presently, evaluations are done by inspectors who compare standard photographs to test samples. We regard the evaluation as patternrecognition, using the fractal dimensions as the features of SP. the first difficult point of automatic evaluation is that the gray levels of SP are often confused withthe gray levels of material's texture. We solved the problem by distinguishing the SP from the texture using the concept of variance. For images containing SP we apply the contrast transform. By these processes, confusion is avoided. the second point is the calculation of fractal dimensions. In order to make it easy to calculate fractal dimensions, we make a curve representing the property of SP. From the curve fractal dimension is calculated. Twenty suits were used as test patterns for the evaluation experiment, and a good result was obtained. We also made an evaluation system using Daubechies' wavelet and compared it withthe present system. the evaluation results obtained by the system using the fractal dimensions showed a better result than that of the wavelet feature.
Measures of scatter are used in statistical patternrecognition to identify and select important features, computed as linear combinations of the given features. Examples include principal components and linear discri...
详细信息
Measures of scatter are used in statistical patternrecognition to identify and select important features, computed as linear combinations of the given features. Examples include principal components and linear discriminants. the classic computational procedures require eigenvector decomposition of large matrices, and in the case of images they are only practical for identifying a low dimensional feature sub-space. We investigate the case in which the selected features are required to be a subset of the given features. It is shown that the same scatter measures used in the general case can also be used in this discrete selection case, but the computational procedure no longer involves matrix eigenvector decomposition. Instead, the selection of pixels that optimize scatter measures can be accomplished by a very simple and efficient discrete optimization technique that runs in linear time regardless of the subspace size. Applications to clustering and content based indexing are discussed.
the pointing problem of visual question answering (VQA) is that given an image and a question which asks for the location of the interested object, find a region that answers the question. It is an important research ...
详细信息
ISBN:
(纸本)9781538637883
the pointing problem of visual question answering (VQA) is that given an image and a question which asks for the location of the interested object, find a region that answers the question. It is an important research problem in VQA tasks and has many potential applications in our daily life. Most of the existing work on this task can only solve it in the form of multiple choices, i.e., given candidate answers in advance, and then selecting a correct one. In this paper, we propose a retrieval model, which can not only deal withthe multiple-choices task, but also provide a feasible solution for the no-candidate-answer task. the principle of our method is to pull the question and correct answer close, and push the question and incorrect answer away in a common feature space. To our best knowledge, we are the first to use retrieval method to solve the unconstrained (no-candidate-answer) pointing problem of VQA. Furthermore, our proposed method outperforms the state-of-the-art methods on the Visual7W [1] dataset in terms of the pointing problem of VQA.
Gender recognition from face images is an important application and it is still an open computervision problem, even though it is something trivial from the human visual system. Variations in pose, lighting, and expr...
详细信息
ISBN:
(纸本)9781538637883
Gender recognition from face images is an important application and it is still an open computervision problem, even though it is something trivial from the human visual system. Variations in pose, lighting, and expression are few of the problems that make such an application challenging for a computer system. Neurophysiological studies demonstrate that the human brain is able to distinguish men and women also in absence of external cues, by analyzing the shape of specific parts of the face. In this paper, we describe an automatic procedure that combines trainable shape and color features for gender classification. In particular the proposed method fuses edge-based and color-blob-based features by means of trainable COSFIRE filters. the former types of feature are able to extract information about the shape of a face whereas the latter extract information about shades of colors in different parts of the face. We use these two sets of features to create a stacked classification SVM model and demonstrate its effectiveness on the GENDER-COLOR-FERET dataset, where we achieve an accuracy of 96.4%.
暂无评论