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In crowdsourced testing, it is beneficial to automatically classify the test reports that actually reveal a fault – a true fault, from the large number of test reports submitted by crowd workers. Most of the existing approaches toward this task simply leverage historical data to train a machine learning classifier and classify the new incoming reports. However, our observation on real industrial data...
Software defect prediction, which predicts defective code regions, can help developers find bugs and prioritize their testing efforts. To build accurate prediction models, previous studies focus on manually designing features that encode the characteristics of programs and exploring different machine learning algorithms. Existing traditional features often fail to capture the semantic differences...
It is well known that the handwritten Chinese text recognition is a difficult problem since there are a large number of classes. In order to solve this problem, we proposed a whole new framework for unconstrained handwritten Chinese text recognition. The core module of the framework is the heterogeneous CNN trained by deep knowledge. The experimental results showed that our proposed method could achieve...
With the development of deep learning, many difficult recognition problems can be solved by deep learning models. For handwritten character recognition, the CNN is used the most. In order to improve the performance of CNN, many new models have been proposed and in which the relaxation CNN [35] is widely used. The relaxation CNN has more complicated structure than CNN while the recognition time is...
Due to its wide applications in practice, face recognition has been an active research topic. With the availability of adequate training samples, many machine learning methods could yield high face recognition accuracy. However, under the circumstance of inadequate training samples, especially the extreme case of having only a single training sample, face recognition becomes challenging. How to deal...
Because of the various appearance (different writers, writing styles, noise, etc.), the handwritten character recognition is one of the most challenging task in pattern recognition. Through decades of research, the traditional method has reached its limit while the emergence of deep learning provides a new way to break this limit. In this paper, a CNN-based handwritten character recognition framework...
In this paper, the part-based recognition method is introduced and applied to the arbitrary font recognition. The principle of the part-based method is to represent the character image as a set of parts and then recognize the image by finding the most possible parts set from the reference database. Since the part-based method does not rely on the global structure of a character, it is supposed to...
Offline handwritten text recognition is a very challenging problem. Aside from the large variation of different handwriting styles, neighboring characters within a word are usually connected, and we may need to segment a word into individual characters for accurate character recognition. Many existing methods achieve text segmentation by evaluating the local stroke geometry and imposing constraints...
In order to forecast ammunition storage reliability better, the paper researched a forecasting method based on neural network which is with the ability of actualizing multi-nonlinear mapping from input to output, and discussed steps of forecasting based on radial basis function (RBF) network. The storage reliability of one new-style ammunition is forecasted based on RBF network. The results show that...
Both appearance and shape play important roles in object localization and object detection. In this paper, we propose a new superedge grouping method for object localization by incorporating both boundary shape and appearance information of objects. Compared with the previous edge grouping methods, the proposed method does not subdivide detected edges into short edgels before grouping. Such long,...
This paper reports a trial of handwritten text recognition by a part-based method. The part-based method recognizes individual characters by their parts without considering their whole shape. This realizes great robustness to severe deformations. This robustness is also effective for text recognition. Especially, for handwritten texts whose segmentation into individual characters is very difficult...
An approach toward pedestrian detection applied to natural images using improved Random Forest (RF) is proposed. We take a more discriminative method for object part detection by applying the feature of pixel-based. We firstly train a pedestrian random forest which directly maps the image patch appearance to the probabilistic vote about the possible location of the object centroid. For a testing image...
Object localization in an image is usually handled by searching for an optimal subwindow that tightly covers the object of interest. However, the subwindows considered in previous work are limited to rectangles or other specified, simple shapes. With such specified shapes, no subwindow can cover the object of interest tightly. As a result, the desired subwindow around the object of interest may not...
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