This second part of the special issue on patternrecognition reports the advances in patternrecognition for visual data. The selected articles address visual categorization by cross-domain dictionary learning, facial...
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This second part of the special issue on patternrecognition reports the advances in patternrecognition for visual data. The selected articles address visual categorization by cross-domain dictionary learning, facial expression recognition, face sketch-photo matching, heartbeat rate measurement from facial video, driver gaze estimation, nighttime vehicle detection, and overlaid arrow detection in biomedical images, respectively.
To reconstruct a 3D scene around the lunar rover from stereo image-pairs captured by the panoramic cameras, based on which an intuitive platform can be put up for scientists to plan exploration commands, we set about ...
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ISBN:
(纸本)9780819469526
To reconstruct a 3D scene around the lunar rover from stereo image-pairs captured by the panoramic cameras, based on which an intuitive platform can be put up for scientists to plan exploration commands, we set about to study the 3D scene reconstruction. This paper mainly presents a scheme of registering local scene models to reconstruction a large scene. When a few of local 3D scene models have been reconstructed respectively, we firstly find the common 3D point sets between every two adjacent local models based on edge detection and image matching, secondly fit the matrix of coordinate transformation employing the technique of separating rotation matrix and translate vector, and lastly register these local 3D models into a uniform coordinate system. In this scheme, we don't need to set a few of control points in a scene beforehand;and we determine the rotation matrix by a system of linear equations based on Cayley transformation. Experimental results of reconstructing indoor and outdoor scenes show that our scene registration method is feasible.
In the front-view monocular vision system, the accuracy of solving the depth field is related to the length of the inter-frame baseline and the accuracy of image matching result. In general, a longer length of the bas...
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ISBN:
(纸本)9781510617223;9781510617216
In the front-view monocular vision system, the accuracy of solving the depth field is related to the length of the inter-frame baseline and the accuracy of image matching result. In general, a longer length of the baseline can lead to a higher precision of solving the depth field. However, at the same time,the difference between the inter-frame images increases, which increases the difficulty in image matching and the decreases matching accuracy and at last may leads to the failure of solving the depth field. One of the usual practices is to use the tracking and matching method to improve the matching accuracy between images, but this algorithm is easy to cause matching drift between images with large interval, resulting in cumulative error in image matching, and finally the accuracy of solving the depth field is still very low. In this paper, we propose a depth field fusion algorithm based on the optimal length of the ***,we analyze the quantitative relationship between the accuracy of the depth field calculation and the length of the baseline between frames, and find the optimal length of the baseline by doing lots of experiments;secondly, we introduce the inverse depth filtering technique for sparse SLAM, and solve the depth field under the constraint of the optimal length of the *** doing a large number of experiments, the results show that our algorithm can effectively eliminate the mismatch caused by image changes, and can still solve the depth field correctly in the large baseline scene. Our algorithm is superior to the traditional SFM algorithm in time and space complexity. The optimal baseline obtained by a large number of experiments plays a guiding role in the calculation of the depth field in front-view monocular.
This paper presents the hardware implementation of a stereo vision core algorithm, that runs in real-time and is targeted at automotive applications. The algorithm is based on the Sum of Absolute Differences (SAD) and...
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ISBN:
(纸本)9781424411795
This paper presents the hardware implementation of a stereo vision core algorithm, that runs in real-time and is targeted at automotive applications. The algorithm is based on the Sum of Absolute Differences (SAD) and computes the disparity map using 320 X 240 input images with a maximum disparity of 100 pixels. The hardware operates at a frequency of 65 MHz and achieves a frame rate of 425 fps by calculating the data highly parallel and pipelined. Thus an implemented and basically optimized software solution, running on an Intel Pentium 4 with 3 GHz clock frequency is 166 times outperformed.
This paper proposes a new methodology to extract biometric features of plant leaf structures. Combining computervision techniques and plant taxonomy protocols, these methods are capable of identifying plant species. ...
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This paper proposes a new methodology to extract biometric features of plant leaf structures. Combining computervision techniques and plant taxonomy protocols, these methods are capable of identifying plant species. The biometric measurements are concentrated in leaf internal forms, specifically in the veination system. The methodology was validated with real cases of plant taxonomy, and eleven species of passion fruit of the genus Passiflora were used. The features extracted from the leaves were applied to the neural network system to perform the classification of species. The results showed to be very accurate in correctly differentiating among species with 97% of success. The computervision methods developed can be used to assist taxonomists to perform biometric measurements in plant leaf structures.
The goal of single-frame Super-Resolution is to improve the spatial resolution of a given low-resolution image. However, it is ill-posed. Regularization which can be interpreted as the way of finding the prior distrib...
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Recently, we developed NIR based face recognition for highly accurate face recognition under illumination variations [10]. In this paper, we present a part-based method for improving its robustness with respect to pos...
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ISBN:
(纸本)9781424411795
Recently, we developed NIR based face recognition for highly accurate face recognition under illumination variations [10]. In this paper, we present a part-based method for improving its robustness with respect to pose variations. An NIR face is decomposed into parts. A part classifier is built for each part, using the most discriminative LBP histogram features selected by AdaBoost learning. The outputs of part classifiers are fused to give the final score. Experiments show that the present method outperforms the whole face-based method [10] by 4.53%.
A 1,000-fps camera-projector system in which projected patterns are adaptively controlled according to image processing results is described. Adaptive structured light projection enables fast and efficient 3-D imforma...
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ISBN:
(纸本)9781424411795
A 1,000-fps camera-projector system in which projected patterns are adaptively controlled according to image processing results is described. Adaptive structured light projection enables fast and efficient 3-D imformation acquisition. The prototype system is applied to the tracking of the nearest point of an object, and experimental results show that the system successfully tracked an apex of a fastmoving target object.
We propose using stereo matching for 2-D face recognition across pose. We match one 2-D query image to one 2-D gallery image without performing 3-D reconstruction. Then the cost of this matching is used to evaluate th...
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ISBN:
(纸本)9781424411795
We propose using stereo matching for 2-D face recognition across pose. We match one 2-D query image to one 2-D gallery image without performing 3-D reconstruction. Then the cost of this matching is used to evaluate the similarity of the two images. We show that this cost is robust to pose variations. To illustrate this idea we built a face recognition system on top of a dynamic programming stereo matching algorithm. The method works well even when the epipolar lines we use do not exactly fit the viewpoints. We have tested our approach on the PIE dataset. In all the experiments, our method demonstrates effective performance compared with other algorithms.
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