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Since convolutional neural network (CNN) lacks an inherent mechanism to handle large scale variations, we always need to compute feature maps multiple times for multiscale object detection, which has the bottleneck of computational cost in practice. To address this, we devise a recurrent scale approximation (RSA) to compute feature map once only, and only through this map can we approximate the rest...
A major challenge in matching between vision and language is that they typically have completely different features and representations. In this work, we introduce a novel bridge between the modality-specific representations by creating a co-embedding space based on a recurrent residual fusion (RRF) block. Specifically, RRF adapts the recurrent mechanism to residual learning, so that it can recursively...
The interoperability testing of CTCS-3 Level Train Control System guarantees the safe operation of train running on different lines. It makes great sense to achieve automatic analysis of interoperability testing results, which could improve the efficiency and accuracy of testing. In this paper, a research was conducted on automatic analysis of testing results for on-board equipment of train control...
This paper targets on the problem of set to set recognition, which learns the metric between two image sets. Images in each set belong to the same identity. Since images in a set can be complementary, they hopefully lead to higher accuracy in practical applications. However, the quality of each sample cannot be guaranteed, and samples with poor quality will hurt the metric. In this paper, the quality...
Boundary and edge cues are highly beneficial in improving a wide variety of vision tasks such as semantic segmentation, object recognition, stereo, and object proposal generation. Recently, the problem of edge detection has been revisited and significant progress has been made with deep learning. While classical edge detection is a challenging binary problem in itself, the category-aware semantic...
This paper proposes a front object recognition system for the safety of driving, which is based on sensor fusion with laser range finder and stereo vision. The study utilizes a stereo vision system for objects detection. Since the calculation time of the stereo vision algorithm is too long, in order to detect objects immediately, a laser range finder is integrated to improve the calculation time and...
This paper presents an interactive visual analysis tool created for studying collections of video data. Our driving application is cellular behavior studies that use microscopy imaging methods. The studies routinely generate large amounts of videos with various experimental conditions. It is very time-consuming for the scientists to watch each video and manually extract features-of-interest for further...
Over the years, computer vision researchers have spent an immense amount of effort on designing image features for the visual object recognition task. We propose to incorporate this valuable experience to guide the task of training deep neural networks. Our idea is to pretrain the network through the task of replicating the process of hand-designed feature extraction. By learning to replicate the...
This paper proposes a static-dynamic hybrid malware detecting scheme for Android applications. While the static analysis could be defeated by transformation technique sometimes and dynamic analysis needs a high complexity, the suggested methods can automatically deliver an unknown App to static or dynamic analysis path according to whether the Android App can be decompiled(its feature) which overcomes...
In distributed multiview video coding (DMVC), the quality of side information (SI) is crucial for decoding and the reconstruction of the Wyner-Ziv (WZ) frames. Generally, its quality is influenced by two main reasons. One reason is that the moving object of the WZ frames can be easily misestimated because of fast motion. The other is that the background around the moving object is also easily misestimated...
Current smartphones are integrated with rich sensors, which provides a good opportunity for the smartphone sensor data mining. By mining these data, we are able to analyze the user's behaviors. This paper describes HARLib, a human activity recognition library on the Android operating system. We use accelerometer built-in smartphone to recognize the user's activities, including walking, running, sitting,...
In the dynamic social network, how to use data mining tools to find the hidden dynamic knowledge in the social network has become the focus of the study. It can be applied to a wide range of areas with good practical value and application significance. We propose a novel algorithm called iDBMM based on the improvement of DBMM algorithm. At first, iDBMM algorithm classifies the training set to obtain...
As the microblogging service (such as Weibo) is becoming popular, spam becomes a serious problem of affecting the credibility and readability of Online Social Networks. Most existing studies took use of a set of features to identify spam, but without the consideration of the overlap and dependency among different features. In this study, we investigate the problem of spam detection by analyzing real...
In this paper, background pixels mutation detection and Hu invariant moments based traffic signs segmentation are combined in traffic signs detection. Considering the gray histogram information in S space, it has good segmentation effects as a global threshold selection method, which can greatly reduce the processing time of the subsequent work. Then using moment invariant theory to extract standard...
In this paper we present a novel approach for discrimination of frontal face in video, using integral channel features(ICF) and Adaboost. We have two stages for this approach based on classification, the first stage is training process, we utilize ICF exacted from training database to train strong classifier, which is implemented by Adaboost. The second stage is discriminating process by scoring,...
An algorithm was proposed for recognizing uncut lawn in order to improve the efficiency of robotic mowers. Image data were captured on uncut lawn. After operations on image data of edge detection, image binaryzation, and image erosion, freeman chain code was used for larger target contour extraction and contour filling, and then the filling area was thinned and the thinned skeleton was pruned. After...
In order to track degradation trend of bearing performance using shock feature hidden in vibration signal, a best Morlet wavelet transform-based extraction method of full information energy entropy was proposed through integrating Morlet wavelet transform technology and full information technology. The optimization of Morlet wave shape factor was controlled by the minimum Shannon entropy. The information...
Dense sample video patches have been used for video representation in action recognition and achieve better performance than sparse spatiotemporal local features. However, two problems of this method must be considered. First one, many video patches are from background other than human body. Second one, the descriptor is not reliable, since it is neither shift nor scale invariant. To solve these two...
Linear Discriminant Analysis (LDA) and its nonlinear kernel variation Generalized Discriminant Analysis (GDA) are the most popular supervised dimensionality reduction methods for fault diagnosis. However, we argue that they probably provide suboptimal results for fault diagnosis due to the Fisher's criterion they use. This paper proposes a new supervised dimensionality reduction method named Locality...
Partial Least Squares are introduced to build the response surface for multi-collinearity problems, which can effectively work on the problems of small sized samples and multiple correlations. However, this approach is a linear method, which is not capable to deal with the non-linear response surface model. To solve this problem, in this paper, we propose two improved algorithms called Local Partial...
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