We propose a new approach to stereo matching for obstacle detection in the autonomous navigation framework. An accurate but slow reconstruction of the ID scene is not needed;rather, it is more important to have a fast...
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ISBN:
(纸本)9783540729020
We propose a new approach to stereo matching for obstacle detection in the autonomous navigation framework. An accurate but slow reconstruction of the ID scene is not needed;rather, it is more important to have a fast localization of the obstacles to avoid them. All the methods in the literature, based on a punctual stereo matching, are ineffective in realistic contexts because they are either computationally too expensive, or unable to deal with the presence of uniform patterns, or of perturbations between the left and right images. Our idea is to face the stereo matching problem as a matching between homologous regions. The stereo images are represented as graphs and a graph matching is computed to find homologous regions. Our method is strongly robust in a realistic environment, requires little parameter tuning, and is adequately fast, as experimentally demonstrated in a comparison with the best algorithms in the literature.
This paper presents an automatic sign language translator, which is able to translate Malaysian sign language using pattern-matching algorithm. The sign language translator is a vision-based system where the image of ...
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The mathematical operation of correlation is a very simple concept, yet has a very rich history of application in a variety of engineering fields. It is essentially nothing but a technique to measure if and to what de...
The mathematical operation of correlation is a very simple concept, yet has a very rich history of application in a variety of engineering fields. It is essentially nothing but a technique to measure if and to what degree two signals match each other. Since this is a very basic and universal task in a wide variety of fields such as signal processing, communications, computervision etc., it has been an important tool. The field of patternrecognition often deals with the task of analyzing signals or useful information from signals and classifying them into classes. Very often, these classes are predetermined, and examples (templates) are available for comparison. This task naturally lends itself to the application of correlation as a tool to accomplish this goal. Thus the field of Correlation patternrecognition has developed over the past few decades as an important area of research. From the signal processing point of view, correlation is nothing but a filtering operation. Thus there has been a great deal of work in using concepts from filter theory to develop Correlation Filters for patternrecognition. While considerable work has been to done to develop linear correlation filters over the years, especially in the field of Automatic Target recognition, a lot of attention has recently been paid to the development of Quadratic Correlation Filters (QCF). QCFs offer the advantages of linear filters while optimizing a bank of these simultaneously to offer much improved performance. This dissertation develops efficient QCFs that offer significant savings in storage requirements and computational complexity over existing designs. Firstly, an adaptive algorithm is presented that is able to modify the QCF coefficients as new data is observed. Secondly, a transform domain implementation of the QCF is presented that has the benefits of lower computational complexity and computational requirements while retaining excellent recognition accuracy. Finally, a two dimensional QCF
We present a method for 3D pose estimation of human motion in generative framework. For the generalization of application scenario, the observation information we utilized comes from monocular silhouettes. We distill ...
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ISBN:
(纸本)9783540763857
We present a method for 3D pose estimation of human motion in generative framework. For the generalization of application scenario, the observation information we utilized comes from monocular silhouettes. We distill prior information of human motion by performing conventional PCA on single motion capture data sequence. In doing so, the aims for both reducing dimensionality and extracting the prior knowledge of human motion are achieved simultaneously. We adopt the shape contexts descriptor to construct the matching function, by which the validity and the robustness of the matching between image features and synthesized model features can be ensured. To explore the solution space efficiently, we design the Annealed Genetic Algorithm (AGA) and Hierarchical Annealed Genetic Algorithm (HAGA) that searches the optimal solutions effectively by utilizing the characteristics of state space. Results of pose estimation on different motion sequences demonstrate that the novel generative method can achieves viewpoint invariant 3D pose estimation.
The advances in computer and communication technologies increased both the number of users and the amount of data shared over the Network. Many times the amount of complex and articulated information available makes i...
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ISBN:
(数字)9783540732792
ISBN:
(纸本)9783540732785
The advances in computer and communication technologies increased both the number of users and the amount of data shared over the Network. Many times the amount of complex and articulated information available makes it difficult to retrieve what is really required for a given task. For these reasons, the efficient, easy and trustworthy transfer of data is now of paramount importance in many everyday scenarios, especially concerning environments and situations where security and data protection are mandatory. On the other hand, data protection often implies the adoption of security means which create virtual (and sometimes even physical) barriers to data retrieval. In this paper, advanced identification technologies, based on the processing of biometric data, are presented. These techniques provide a number of tools to facilitate the seamless human interaction with the data, and the security barriers, by enabling the environment to recognize and learn from the user, shaping the data available on the basis of his/her identity. The presented techniques are based on the extraction of invariant features from face and fingerprint images to process static biometric features, also allowing the enhancement of identification accuracy by data fusion.
The 3D scanning of human faces is an important tool to acquire accurate shape and texture information of heads and faces for both animation and recognition purposes. One of the most popular scanning tools is Cyberware...
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Compared with the traditional interaction approaches, such as keyboard, mouse, pen, etc, vision based hand interaction is more natural and efficient. In this paper, we proposed a robust real-time hand gesture recognit...
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The proceedings contain 157 papers. The topics discussed include: known unknowns: novelty detection in condition monitoring;seeing the invisible and predicting the unexpected;vision-based SLAM in real-time;handwritten...
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ISBN:
(纸本)9783540728467
The proceedings contain 157 papers. The topics discussed include: known unknowns: novelty detection in condition monitoring;seeing the invisible and predicting the unexpected;vision-based SLAM in real-time;handwritten symbol recognition by a boosted blurred shape model with error correction;Bayesian hyperspectral image segmentation with discriminative class learning;comparison of unsupervised band selection methods for hyperspectral imaging;learning mixture models for gender classification based on facial surface normals;motion segmentation from feature trajectories with missing data;segmentation of rigid motion from non-rigid 2D trajectories;hierarchical eyelid and face tracking;automatic learning of conceptual knowledge in image sequences for human behavior interpretation;a comparative study of local descriptors for object category recognition:SIFT vs HMAX;and moment-based pattern representation using shape and grayscale features.
As part of the U.S. Department of Transportation's Intelligent Vehicle Initiative (IVI) program, the Federal Highway Administration (FHWA) is conducting R&D in vehicle safety and driver information systems. Th...
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ISBN:
(纸本)9780819469243
As part of the U.S. Department of Transportation's Intelligent Vehicle Initiative (IVI) program, the Federal Highway Administration (FHWA) is conducting R&D in vehicle safety and driver information systems. There is an increasmig number of applications where pedestrian monitoring is of high importance. visionbased pedestrian detection in outdoor scenes is still an open challenge. People dress in very different colors that sometimes blend with the background, wear hats or carry bags, and stand, walk and change directions unpredictably. The background is various, containing buildings, moving or parked cars, bicycles, street signs, signals, etc. Furthermore, existing pedestrian detection systems perform only during daytime, making it impossible to detect pedestrians at night. Under FHWA funding, we are developing a multi-pedestrian detection system using IR LED stereo camera. This system, without using any templates, detects the pedestrians through statistical patternrecognition utilizing 3D features extracted from the disparity map. A new IR LED stereo camera is being developed, which can help detect pedestrians during daytime and might time. Using the image differencing and denoising, we have also developed new methods to estimate the disparity map of pedestrians in near real time. Our system will have a hardware interface with the traffic controller through wireless communication. Once pedestrians are detected, traffic signals at the street intersections will change phases to alert the drivers of approaching vehicles. The initial test results using images collected at a street intersection show that our system can detect pedestrians in near real time.
Action recognition is one of the most active research fields in computervision. In this paper, we propose a novel method for classifying human actions in a series of image sequences containing certain actions. Human ...
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ISBN:
(纸本)9781424414369
Action recognition is one of the most active research fields in computervision. In this paper, we propose a novel method for classifying human actions in a series of image sequences containing certain actions. Human action in image sequences can be recognized by a time-varying contour of human body. We first extract shape context of each contour to form the feature space. Then the dominant sets approach is used for feature clustering and classification to obtain the labeled sequences. Finally, we use a smoothing algorithm upon the labeled sequences to recognize human actions. The proposed dominant sets-based approach has been tested in comparison to three classical methods: K-means, mean shift, and Fuzzy-Cmean. Experimental results demonstrate that the dominant sets-based approach achieves the best recognition performance. Moreover, our method is robust to non-rigid deformations, significant scale changes, high action irregularities, and low quality video.
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