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Many real-world problems involve multi-view high-dimension-small-sample-size data analysis, such as multi-omics data. The combination of multi-view databases is supposed to provide a better biological significance. However, the multi-view data always contain noise and outlying entries that result in inaccurate and unreliable. It has become an urgent need how to effectively analyze these data. We proposed...
We present an approach to jointly detect mitotic events spatially and temporally in time-lapse phase contrast microscopy images. In particular, we combine a convolutional neural network (CNN) and a long short-term memory (LSTM) network to detect mitotic events in patch sequences. The CNN-LSTM network can be trained end-to-end to simultaneously learn convolutional features within each frame and temporal...
Soccer video semantic analysis has attracted a lot of researchers in the last few years. Many methods of machine learning have been applied to this task and have achieved some positive results, but the neural network method has not yet been used to this task from now. Taking into account the advantages of Convolution Neural Network(CNN) in fully exploiting features and the ability of Recurrent Neural...
Recognizing human action from low-resolution (LR) videos is essential for many applications including large-scale video surveillance, sports video analysis and intelligent aerial vehicles. Currently, state-of-the-art performance in action recognition is achieved by the use of dense trajectories which are extracted by optical flow algorithms. However, the optical flow algorithms are far from perfect...
We present a computational model for image super-resolution. Apart from using deep Convolutional Neural Network to map between the low-resolution images and high-resolution images, we adopt stepwise refinement method to improve the reconstruction results and introduce the back projection algorithm to avoid diffusion of error pixels.
This paper presents an automatic event detection system fusing low and mid level features for soccer videos. We first employ an improved approach for Shot Boundary Detection with color and our mean-gradient feature. Then we classify the shots into two view types. We also perform a template-based replay detection for each shot. Play-break sequences are then generated using a rule-based method. We devise...
In recent years, object tracking is often formulated as detection tasks. Although many methods of machine learning and statistical learning can effectively achieve discriminate target model, but they usually require a large amount of training samples for satisfactory precision. Combining advantages of ensemble learning and the support vector machine, this paper proposes a simple tracking method based...
Feature selection only using wrapper method in high-dimensional data space is always time-consuming. A new feature selection method, named fast static particle swarm optimization, is proposed for tackling this problem. It treats the whole initial feature set as a static particle swarm in which no new particle would be generated in high dimensional space, and the proposed method takes filter and wrapper...
Changes and murmurs of heart sounds often provide the earliest information of abnormal heart status, which makes heart sound monitor and diagnose an important mean for cardiovascular disease. Compared to other heart monitoring approaches, such as EGG, Color Doppler and Magnetic Resonance Imaging Diagnosis, heart sound detection is more portable, much safer and cheaper, and needs lower professional...
Convexity (concavity) is a bottom-up cue to assign figure-ground relation in the perceptual organization [18]. It suggests that region on the convex side of a curved boundary tend to be figural. To explore the validity of this cue in the task of salient object detection, we segment the images in a test dataset into superpixels, and then locate the concave arcs and their bounding boxes along boundary...
In this paper, we propose an example-based facial sketch hallucination approach. Given a face image, its sketch image will be automatically hallucinated by learning from a training set, which includes a lot of face images and their corresponding sketch images. Our algorithm involves three stages. In the first stage which is called ??feature extracting??, we create the feature pyramid for each face...
In this paper, we proposed a novel approach to image completion for overlapping chromosomes. In our system, only given missing regions, the task can be performed automatically without human intervention. We address the problem of image completion for overlapping chromosomes in the context of a discrete global optimization problem. In order to reconstruct the original chromosome image as faithfully...
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