Recently, a new representation for recognizing instances and categories of scenes called spatial Principal component analysis of Census Transform histograms (PACT) has shown its excellent performance in the scene imag...
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ISBN:
(纸本)9781457720086
Recently, a new representation for recognizing instances and categories of scenes called spatial Principal component analysis of Census Transform histograms (PACT) has shown its excellent performance in the scene image classification task. PACT captures local structures of an image through the Census Transform (CT), meanwhile, large scale structures are captured by the strong correlation between neighboring CT values and the histogram. However, the original spatial PACT only simply concatenates all levels compact histograms together, and discards the difference between various levels. In order to improve this problem, we propose a multi-level kernel machine method, which computes a set of base kernels at each level of pyramid of PACT, and finds optimal weights for best fusing all these base kernels for scene recognition. Experiments on two popular benchmark datasets demonstrate that our proposed multi-level kernel machine method outperforms the spatial PACT on scene recognition. Besides, our method is easy to be implemented comparing with spatial PACT.
Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high...
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Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high fault prone metrical data are much scattered and multi-centers can represent the whole dataset better, we used artificial immune network (aiNet) algorithm to extract and simplify data from the modules that have been tested, then generated multi-centers for each network by Hierarchical Clustering. The proposed framework acquires information along with the testing process timely and adjusts the network generated by aiNet algorithm dynamically. Experimental results show that higher accuracy can be obtained by using the proposed framework.
SIFT (Scale Invariant Feature Transform) is one of most popular approach for feature detection and matching. Many parallelized algorithms have been proposed to accelerate SIFT to apply into real-time systems. This pap...
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image annotation is the process of assigning proper keywords to describe the content of a given image, which can be regarded as a problem of multi-object image classification. In this paper, a general multi-label anno...
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ISBN:
(纸本)9781457720086
image annotation is the process of assigning proper keywords to describe the content of a given image, which can be regarded as a problem of multi-object image classification. In this paper, a general multi-label annotation algorithm is proposed, which is based on sparse representation theory and employs a multi-level decision method to deal with the multi-object classification problem. The experimental results show that the proposed algorithm can provide more promising results compared with the traditional classification based image annotation methods.
Moving objects classification in traffic scene videos is a hot topic in recent years. It has significant meaning to intelligent traffic system by classifying moving traffic objects into pedestrians, motor vehicles, no...
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ISBN:
(纸本)9781457701221
Moving objects classification in traffic scene videos is a hot topic in recent years. It has significant meaning to intelligent traffic system by classifying moving traffic objects into pedestrians, motor vehicles, non-motor vehicles etc.. Traditional machine learning approaches make the assumption that source scene objects and target scene objects share same distributions, which does not hold for most occasions. Under this circumstance, large amount of manual labeling for target scene data is needed, which is time and labor consuming. In this paper, we introduce TrAdaBoost, a transfer learning algorithm, to bridge the gap between source and target scene. During training procedure, TrAdaBoost makes full use of the source scene data that is most similar to the target scene data so that only small number of labeled target scene data could help improve the performance significantly. The features used for classification are Histogram of Oriented Gradient features of the appearance based instances. The experiment results show the outstanding performance of the transfer learning method comparing with traditional machine learning algorithm.
A key problem of image Based Visual Servo (IBVS) System is to track objects in image sequences. Thus, the tracking algorithm plays an important role in improving the efficiency of IBVS systems. In this paper, a novel ...
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ISBN:
(纸本)9781457701221
A key problem of image Based Visual Servo (IBVS) System is to track objects in image sequences. Thus, the tracking algorithm plays an important role in improving the efficiency of IBVS systems. In this paper, a novel tracking algorithm called Modified CamShift Guided Particle Filter (MCAMSGPF) is proposed, which interpolated Speeded-Up Robust Features (SURF) into the framework of conventional CamShift Guided Particle Filter (CAMSGPF) tracking method. This new algorithm outperforms conventional CAMSGPF and other baseline trackers with respect to tracking robustness in the clutter background of similar colors and occlusions. We also proposed a new system model to implement and test the new algorithm in a real time moving IBVS system, which is applied in a mobile robot with an on-board camera.
Head pose plays an important role in Human- Computer interaction, and its estimation is a challenge problem compared to face detection and recognition in computer vision. In this paper, a novel and efficient method is...
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Head pose plays an important role in Human- Computer interaction, and its estimation is a challenge problem compared to face detection and recognition in computer vision. In this paper, a novel and efficient method is proposed to estimate head pose in real-time video sequences. A saliency model based segmentation method is used not only to extract feature points of face, but also to update and rectify the location of feature points when missing happened. This step also gives a benchmark for vector generation in pose estimation. In subsequent frames feature points will be tracked by sparse optical flow method and head pose can be determined from vectors generated by feature points between successive frames. Via a voting scheme, these vectors with angle and length can give a robust estimation of the head pose. Compared with other methods, annotated training data set and training procedure is not essential in our method. Initialization and re-initialization can be done automatically and are robust for profile head pose. Experimental results show an efficient and robust estimation of the head pose.
Recently, human action recognition has been a popular and important topic in computer vision. However, except some conventional problems such as noise, low resolution etc., viewinvariant recognition is one of the most...
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ISBN:
(纸本)9781457701221
Recently, human action recognition has been a popular and important topic in computer vision. However, except some conventional problems such as noise, low resolution etc., viewinvariant recognition is one of the most challenging problems. In this paper, we focus on solve multi-view action recognition from surveillance video. To detect moving objects from complicated backgrounds, this paper employs improved Gaussian mixed model, which uses K-means clustering to initialize the model and it gets better motion detection results for surveillance videos. We demonstrate the silhouette representation ”Envelope Shape” can solve the viewpoint problem in surveillance videos. The experiment results demonstrate that our human action recognition system is fast and efficient on CASIA activity analysis database.
Searching interesting regions in aerial video is a new and challenging problem. This paper presents an approach to detect visual interesting regions in aerial video using pLSA topic model. Traditional interesting regi...
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ISBN:
(纸本)9781457701221
Searching interesting regions in aerial video is a new and challenging problem. This paper presents an approach to detect visual interesting regions in aerial video using pLSA topic model. Traditional interesting region detection approaches just use bottom-up information, such as color, orientation and movement etc. Our proposed method can discover the semantic content of the whole image, the co-occurrence of local image patches via pLSA model, and consequently improve detection result significantly in real world scenes. First, we extract frames from aerial video as documents. Then we use vector quantized SIFT descriptors as words. Third, we discover topics (e.g. plants, roads, buildings) and the relation among them using pLSA model. Finally, we can detect interesting regions as we need according to calculated models. Experimental observations show the success of our approach on interesting region detection in aerial video.
Traffic flow detection plays an important role in Intelligent Transportation Systems(ITS).Video based traffic flow detection system is the most widely used strategy in *** this circumstance,we design and implement a v...
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ISBN:
(纸本)9781457718342
Traffic flow detection plays an important role in Intelligent Transportation Systems(ITS).Video based traffic flow detection system is the most widely used strategy in *** this circumstance,we design and implement a video based traffic flow detection system which is called MyTD in this *** takes advantages of both shadow removal and optical flow ***,we introduce the current development of ITS and focus on the video based traffic detection technology,which is the key to ***,a shadow removal algorithm combining information in both RGB and HSV color spaces is ***,based on the Iterative Pyramidal LK Optical Flow Algorithm,a vehicle tracking function is realized by OpenCV,as well as a Connected Components Labeling function and the vehicle counting ***,MyTD is implemented and tested based on the algorithm presented *** results show the outstanding performance of our method comparing with traditional optical flow algorithms.
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