This paper describes an automatic dense correspondence approach to match two given isometric or nearly isometric 3D shapes which have non-rigid deformations. Our method is to improve the described ability of the assignment matrix as much as possible and solve the resolution composed of assignment matrices by using a combinatorial optimization algorithm. First, we construct two linear assignment matrices by using the SHOT and HKS descriptor, which can promote similar points into correspondence. Then, we construct a quadratic assignment matrix by using the heat distribution matrix, which can align a set of pairwise descriptors between a pair of points. In the final, we create a new objective function consisting of three assignment matrices which can adequately describe the matching relationship between points on two non-rigid deformed shapes, and the final optimal solution is obtained by solving the objective function using the projected descent optimization procedure. We show that high-quality dense correspondences can be established for a wide variety of model pairs which may have different poses, surface details. The effectiveness of this method is proven by geodesic error distance statistics from two commonly used datasets with ground truth, and we find that our algorithm is better than other state-of-the-art methods.
Content-based Image Retrieval (CBIR) has been studied over decades and starting from conventional local handcrafted methods to CNN-based methods many works have achieved the best performances in retrieval tasks using query expansion, average query expansion, and query fusion techniques. This work presents a novel approach to revisit the large-scale image retrieval benchmarks Oxford building and Paris building using the SIFT and CNN-based approach. In this paper, we have revised two image retrieval methods and combined the approaches for better performance on image retrieval tasks by describing the annotation errors that have not discussed earlier. The new extensive queries were added for each dataset, making it difficult for the retrieval query phase. VGG-16 network used and RootSIFT applied for feature extraction step whereas T-embedding and democratic aggregation applied on the local descriptors. Query expansion which is an extensive technique for retrieval accuracy is used to check the validation of the proposed pipeline, and our framework achieved the state-of-the-art in addressing the retrieval results compared to other CBIR methods.
One of the ways to diagnose cancer is to obtain images of the cells under the microscope through biopsies. Because the images of the stained cells are very complicated, there is a great deal of interference with the doctor's observations. To address this issue, we propose a new method for segmenting glandular cavity from gastric cancer cell images. Our method combines local correntropy-based K-means (LCK) clustering method and morphological operations to divide the image into complete glandular cavity and remove all extra-cavity interference areas. Our method does not require human interaction. The acquired image boundary features and internal information are complete, allowing doctors to diagnose cancer more quickly and efficiently.
With the rapid development of the mobile Internet,more and more people are using smart phones to access the Internet, especially Android devices, which have become the most popular devices of the moment. Although today's mobile operating systems do their best to provide users with a secure Internet environment, due to the open source nature of Android, it is still unable to completely stop the outbreak of Android malware. Although existing source-based static detection and behavior-based dynamic detection can identify mobile malware, many problems still exist,such as low detection efficiency and difficulty in deployment. In order to solve these problems, we propose DroidDetector, a detection engine that can automatically detect whether an app is a malware or not by using off-line trained machine learning models for network traffic analysis. DroidDetector uses the VPNService class provided by the Android SDK to intercept network traffic (it does not require root permission). All data analysis are performed on the server,which consumes minimun cache and resource on mobile devices. We extract the length of the first 8 packets of network traffic as features and use Support Vector Machine(SVM) classification algorithm to train the model. In an evaluation experiment of 53107 TCP packet length feature tuples samples, DroidDetector can achieve 95. 68% detection confidence.
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