A new method to reconstruct 3D scene points from nonparallel stereo is proposed. From a pair of conjugate images in an arbitrarily configured stereo system that has been calibrated, coordinates of 3D scene points can ...
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A new method to reconstruct 3D scene points from nonparallel stereo is proposed. From a pair of conjugate images in an arbitrarily configured stereo system that has been calibrated, coordinates of 3D scene points can be computed directly using the method, bypassing the process of rectifying images or iterative solution involved in existing methods. Experiment results from both simulated data and real images validate the method. Practical application to surgical navigator shows that the method has advantages to improve efficiency and accuracy of 3D reconstruction from nonparallel stereo system in comparison with the conventional method that employs algorithm for standard parallel axes stereo geometry.
The discrete cosine transform plays an important role in rectangularly sampled image coding for its excellent performance in information compaction. Hexagonal sampling is the optimal sampling strategy for two-dimensio...
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The discrete cosine transform plays an important role in rectangularly sampled image coding for its excellent performance in information compaction. Hexagonal sampling is the optimal sampling strategy for two-dimensional signals in the sense that exact reconstruction of the waveform requires a lower sampling density than with the alternative schemes. In this Letter, a hexagonal discrete cosine transform (HDCT) for encoding the hexagonally sampled signals is presented.
A new approach for optical flow (image velocity) fields computation is presented using computational neural networks. The computational procedure consists of three stages: estimation of the parameters of the neural ne...
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A new approach for optical flow (image velocity) fields computation is presented using computational neural networks. The computational procedure consists of three stages: estimation of the parameters of the neural network model, dynamic measurement of the perpendicular velocity components of the contours or region boundaries and computation of the image velocity fields. The parameters are estimated by comparing the energy function of the neural network with a constrained error function. The nonlinear velocity fields computation method is then carried out iteratively by using a dynamic algorithm to minimise the energy function simultaneously with the dynamic measurement of the perpendicular velocity components by a dynamic procedure. Experiments generate velocity fields that are meaningful and consistent with visual perception.
A blind image restoration algorithm for noiseless blurred two-tone images based on the criterion of minimum entropy is presented. Performance of the algorithm is demonstrated by simulation.
A blind image restoration algorithm for noiseless blurred two-tone images based on the criterion of minimum entropy is presented. Performance of the algorithm is demonstrated by simulation.
The concept of occlusion mesh model is introduced. A novel object tracking algorithm based on occlusion mesh model is proposed. A modified occlusion detection method is considered to improve the detection accuracy. Me...
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ISBN:
(纸本)0780375084
The concept of occlusion mesh model is introduced. A novel object tracking algorithm based on occlusion mesh model is proposed. A modified occlusion detection method is considered to improve the detection accuracy. Mesh nodes motion estimation method based on feature window matching is presented to achieve sub-pixel resolution and overcome block artifacts produced by block matching. Experiment results indicate that the proposed algorithm is practical and feasible, it can be used to object tracking effectively. Also it solves 2D motion estimation problem existed in occlusion regions and shows better visual performance.
In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The mod...
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In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The model improves conventional D-S evidence theory in temporal domain, such that it can satisfy the requirement of real-time processing and utilize traffic detection information more efficaciously. The model frame and computation al procedures are given. In addition, a generalized reliability weight matrix of evidence is also presented to increase the accuracy of estimation. After that, a simulation test is presented to explain the advantage of the proposed method in comparison with conventional D-S evidence theory. Besides, the validity of the model is proven by the use of the data of loop detectors and GPS probe vehicles collected from an urban link in Shanghai. Results of the experiment show that the proposed approach can well embody and track traffic state at character level in real-time conditions.
Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment im...
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Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment image correctly. Enlightened from segmentation based on fuzzy theories, soft class-map is constracted to solve that problem. The definitions of values and other related ones are adjusted according to the soft class-map. With more detailed values obtained from soft class map, more color distribution information is preserved. Experiments on a synthetic image and many other color images illustrate that JSEG with soft class-map can solve efficiently the problem that in a region there may exist color gradual variation in a smooth transition. It is a more robust method especially for images which haven＇ t been heavily blurred near boundaries of underlying regions.
An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. inst.ad of color quantization algorithm, an automatic classification method based on adaptive mean shift ...
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An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. inst.ad of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.
Many problems in image representation and classification involve some form of dimensionality reduction. Nonnegative matrix factorization （NMF） is a recently proposed unsupervised procedure for learning spatially loc...
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Many problems in image representation and classification involve some form of dimensionality reduction. Nonnegative matrix factorization （NMF） is a recently proposed unsupervised procedure for learning spatially localized, partsbased subspace representation of objects. An improvement of the classical NMF by combining with Log-Gabor wavelets to enhance its part-based learning ability is presented. The new method with principal component analysis （PCA） and locally linear embedding （LIE） proposed recently in Science are compared. Finally, the new method to several real world datasets and achieve good performance in representation and classification is applied.
This paper investigated the performances of a well-known car-following model with numerical simulations in describing the deceleration process induced by the motion of a leading car. A leading car with a pre-specilied...
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This paper investigated the performances of a well-known car-following model with numerical simulations in describing the deceleration process induced by the motion of a leading car. A leading car with a pre-specilied speed profile was used to test the above model. The results show that this model is to some extent deficient in performing the process aforementioned. Modifications of the model to overcome these deficiencies were demonstrated anda modified car-following model was proposed accordingly. Furthermore, the delay time of car motion of the new model were studied.
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