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There has been a surge in research interest in learning feature representation of networks in recent times. Researchers, motivated by the recent successes of embeddings in natural language processing and advances in deep learning, have explored various means for network embedding. Network embedding is useful as it can exploit off-the-shelf machine learning algorithms for network mining tasks like...
In this paper, we first provide a new perspective to divide existing high performance object detection methods into direct and indirect regressions. Direct regression performs boundary regression by predicting the offsets from a given point, while indirect regression predicts the offsets from some bounding box proposals. In the context of multioriented scene text detection, we analyze the drawbacks...
Accurate calling of structural variations such as deletions with short sequence reads from high-throughput sequencing is an important but challenging problem in the field of genome analysis. There are many existing methods for calling deletions. At present, not a single method clearly outperforms all other methods in precision and sensitivity. A popular strategy used by several authors is combining...
Deep convolutional neural networks (DCNN) have recently achieved state-of-the-art performance on handwritten Chinese character recognition (HCCR). However, most of DCNN models employ the softmax activation function and minimize cross-entropy loss, which may loss some inter-class information. To cope with this problem, we demonstrate a small but consistent advantage of using both classification and...
Writer adaptation is an important topic in handwriting recognition, which can further improve the performance of writer-independent recognizer. In this paper, we propose combining the neural network classifier with style transfer mapping (STM) for unsupervised writer adaptation, which only require writer-specific unlabeled data, and therefore is more common and efficient compared to supervised adaptation...
This paper is aimed to study the impact of sEMG feature weight on the recognition of similar grasping gesture, of which the classification performance is hindered by their alike underlying muscle activation pattern. The sEMG were collected from six forearm hand muscles (EPB, EPI, FDS, PL, MB, ED) when subjects conducted a 4-second different grasping gestures. Then empirical mode decomposition (EMD)...
Discriminative feature extraction (DFE) is an effective linear dimensionality reduction method for pattern recognition. It improves the recognition performance via optimizing subspace projection axes and classifier parameters simultaneously. In this paper, we propose a nonlinear extension of DFE, called discriminative quadratic feature extraction (DQFE), for which feature vectors are firstly mapped...
Against the problem of extracting the color aberration of high-reflect-rate ceramic tile surface texture, author came up a way of using wavelet transformation to extract the image texture features. Basically, the way is suggesting using two dimension wavelet decomposition on each passage of the image and extract energy characteristic from each detailed sub-graph. This energy signal merges the message...
This paper describes the Chinese handwriting recognition competition held at the 12th International Conference on Document Analysis and Recognition (ICDAR 2013). This third competition in the series again used the CASIA-HWDB/OLHWDB databases as the training set, and all the submitted systems were evaluated on closed datasets to report character-level correct rates. This year, 10 groups submitted 27...
Human faces undergo considerable amount of variations across ages. This paper proposes an age-invariant face verification method by using a Local Classifier Ensemble Model (LCEM). First, reference points are located based on an extended Active Shape Model and faces are aligned afterwards. Second, a face is grouped into several non-overlapping patches and each group is further divided into several...
The paper presents an efficient and low-cost method for automatically detecting and tracking the moving object from astronomical CCD image sequences, using a combination of active contours and shape feature similarities. An object detection algorithm is firstly implemented following some image preprocessing steps, in order to locate all the major objects in each image. Next, an object tracking method...
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