This article examines how high-speed internet development across Chinese provinces affects firm employment and average annual wages. The authors exploit a national policy reform by which the government launched a project to improve internet speed in 2000 and devise a difference-in-differences with a continuous treatment method for empirical identification. The authors first find that high-speed internet significantly increases employment and slightly reduces average wages. Specifically, after one percentage point increase in the number of fixed-line users per capita at the province level, there is a 0.14 percent increase in firm employment and a 0.06 percent decrease in average wages, respectively. Also, this paper shows that the gains and losses from high-speed internet on firm performance are likely to have risen from the higher probability of firm entry and the improvement of productivity in existing firms. Third, the authors find rich heterogeneity across firm ownership, size, and regional characteristics.

Visual tracking can be modeled as a binary classification problem, and the classic classifiersupport vector machine (SVM) based methods have been demonstrated encouraging performance in recent object tracking benchmarks. However, the performance of SVM is too sensitive to noisy training data during online update. In this paper, we propose an efficient dual-SVM based tracker to improve classification performance for visual tracking. The tracker proposed consists of two models: the holistic model and the part model. To learn the holistic model, the support instances are derived from the RMI-SVM trained in a deep feature space. As for the part model to highlight local structure of the target, a linear SVM is learned to further encode local details of the target, selecting candidate instances from the support instances by the confidence as input. To fuse the holistic model and the part model, we design a simple but efficient decision strategy (LEVER) to enforce the dual-SVM to focus on the target. The proposed LEVER is updated incrementally to capture changes of the appearance of the target. Extensive experimental results show that the proposed tracker performs favorably against state-of-the-art methods.

This paper proposes a robust visual tracking method by designing a collaborative model. The collaborative model employs a two-stage tracker and a HOG-based detector, which exploits both holistic and local information of the target. The two-stage tracker learns a linear classifier from the patches of original images and the HOG-based detector trains a linear discriminant analysis classifier with the object exemplar. Finally, a result decision making strategy is developed by considering both the original template and the appearance variations, making the tracker and the detector collaborate with each other. The proposed method has been evaluated on OTB-50, OTB-100 and Temple-Color datasets, and results demonstrate that the proposed method is able to effectively address the challenging cases such as scale variation and out-of-view and gets better performance than the state-of-the-art trackers.

For visual tracking, the target object is represented by an appearance model and the location of the target is estimated in each frame. Numerous tracking algorithms model the appearance of the target with a confidence score and rarely take into account the semantic information of the target. In this paper, we propose an efficient tracking algorithm that models the appearance of the target based on semantic segmentation. The overall architecture consists of two parts: the segmentation part and the tracking part. In the segmentation part, an attention model is employed, providing spatial highlights of the candidate region of the target. In the tracking part, the tracker is constructed by an online updated convolutional neural networks to identify the target in subsequent frames, taking advantage of the segmentation information of the target from the segmentation part. To enhance the performance of this architecture, we design an incremental updated prior map taking both the segmentation signal and the tracking signal into consideration. Extensive experiments on two benchmarks including OTB-50, OTB-100, and Temple-Color, show that the proposed method outperforms other trackers.

This paper proposes a novel saliency detection method by developing a deeply-supervised recurrent convolutional neural network (DSRCNN), which performs a full image-to-image saliency prediction. For saliency detection, the local, global, and contextual information of salient objects is important to obtain a high quality salient map. To achieve this goal, the DSRCNN is designed based on VGGNet-16. Firstly, the recurrent connections are incorporated into each convolutional layer, which can make the model more powerful for learning the contextual information. Secondly, side-output layers are added to conduct the deeply-supervised operation, which can make the model learn more discriminative and robust features by effecting the intermediate layers. Finally, all of the side-outputs are fused to integrate the local and global information to get the final saliency detection results. Therefore, the DSRCNN combines the advantages of recurrent convolutional neural networks and deeply-supervised nets. The DSRCNN model is tested on five benchmark datasets, and experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art saliency detection approaches on all test datasets.

Reliable localization of the optic disc (OD) is important for retinal image analysis and ophthalmic pathology screening. This paper presents a novel method to automatically localize ODs in retinal fundus images based on directional models. According to the characteristics of retina vessel networks, such as their origin at the OD and parabolic shape of the main vessels, a global directional model, named the relaxed biparabola directional model, is first built. In this model, the main vessels are modeled by using two parabolas with a shared vertex and different parameters. Then, a local directional model, named the disc directional model, is built to characterize the local vessel convergence in the OD as well as the shape and the brightness of the OD. Finally, the global and the local directional models are integrated to form a hybrid directional model, which can exploit the advantages of the global and local models for highly accurate OD localization. The proposed method is evaluated on nine publicly available databases, and achieves an accuracy of 100% for each database, which demonstrates the effectiveness of the proposed OD localization method.

This paper proposes a novel saliency detection method based on graph-structural agglomerative clustering (GSAC). In this method, a number of intermediate images with consecutive number of regions are firstly created by using GSAC to the input image with the maximum incremental path integral criterion. Then an initial salient map is computed based on the boundary connectivity of the regions in the intermediate images, with enforcing the early formed objects in the clustering process. Finally, the initial salient map is refined to get the final salient map by using the reconstruction errors of sparse coding and the object-bias prior. The experimental results demonstrate that the proposed method greatly outperforms the state-of-the-art approaches on two standard benchmark datasets.

This paper presents a novel text line segmentation method based on matched filtering and top-down grouping for handwritten documents. The proposed method consists of three distinct steps. Firstly, the foreground pixel density (FPD) of handwritten document image (HDI) is estimated, then FPD is used to decide the size of the generated filter which is the convolution of a band-shape filter and an isotropic LoG filter. Secondly, the centers of the text lines (CTLs) are extracted by performing filtering, binarizing, thinning and top-down grouping operation on HDI. Finally, the overlapping connected-components (OCCs) which travel through multiple text lines are separated, and then all OCCs are assigned to a label of CTLs by the nearest neighbor principle. The proposed method is tested on two public databases, and the experimental results show that the proposed method outperforms the state-of-the-art text line segmentation approaches in both of these databases.

This paper proposes a novel offline text-independent writer identification method based on scale invariant feature transform (SIFT), composed of training, enrollment, and identification stages. In all stages, an isotropic LoG filter is first used to segment the handwriting image into word regions (WRs). Then, the SIFT descriptors (SDs) of WRs and the corresponding scales and orientations (SOs) are extracted. In the training stage, an SD codebook is constructed by clustering the SDs of training samples. In the enrollment stage, the SDs of the input handwriting are adopted to form an SD signature (SDS) by looking up the SD codebook and the SOs are utilized to generate a scale and orientation histogram (SOH). In the identification stage, the SDS and SOH of the input handwriting are extracted and matched with the enrolled ones for identification. Experimental results on six public data sets (including three English data sets, one Chinese data set, and two hybrid-language data sets) demonstrate that the proposed method outperforms the state-of-the-art algorithms.

Contactless palmprint recognition is an effective technology for improving the user friendliness of palmprint recognition. The main challenge of contactless palmprint recognition is the intra-class variations due to the hand deformation caused by contactless image acquisition. Traditional palmprint feature extraction and matching algorithms usually require that the query image is well aligned with the gallery image, which cannot be always assured in contactless occasions. In this work, the scale invariant feature transform (SIFT) is applied for contactless palmprint feature extraction and matching. SIFT features are invariant to image rotation, translation, and scale variations, and hence are promising to solve the deformation problem. Moreover, an iterative RANSAC algorithm is proposed to refine the matched SIFT points. The iterative RANSAC retains more matched SIFT points than the traditional RANSAC algorithm despite the points comply with different transformation models. Experiments show a great improvement of verification accuracy on a public contactless palmprint database.