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Automatically recognizing pornographic images from the Web is a vital step to purify Internet environment. Inspired by the rapid developments of deep learning models, we present a deep architecture of convolutional neural network (CNN) for high accuracy pornographic image recognition. The proposed architecture is built upon existing CNNs which accepts input images of different sizes and incorporates...
This paper presents an method that matches points and line segments jointly on wide-baseline stereo images. In both two images to be matched, line segments are extracted and those spatially adjacent ones are intersected to generate V-junctions. To match V-junctions from the two images, we extract for each of them an affine and scale invariant local region and describe it with SIFT. The putative V-junction...
In real-time anomaly detection problems, reducing the dimensionality and improving recognition rate are two most crucial problems. The unbalanced data distribution is one of main reasons of leading to low recognition rate. In this paper, a hybrid approach using Tabu search (TS) and ensemble classification algorithm is proposed. Tabu search is simultaneously applied to select features and weights of...
In this paper we address the problem of large image retrieval from millions of images. Recently, deep convolutional neural network has demonstrated superior performance in a number of computer vision applications. We propose to adapt the existing architecture targeted towards image classification to directly learn features for efficient image retrieval. We extend the Weighted Approximate Rank Pairwise(WARP)...
By combining self-training method of the semi-supervised learning with two-dimensional principal component analysis (2DPCA), a semi-supervised learning based face recognition method is proposed. On the basis of two-dimensional principal component analysis, few labeled samples are used to obtain classifier. Then unlabeled samples are classified by the classifier. And according to the self-training...
In order to solve the problem about the recognition accuracy, Tchebichef moment invariants and support vector machine (SVM) are adopted to recognize the vehicle-logo. It extracts six invariant moments of the object as feature vectors, and then uses the support vector machines (SVM) to recognize vehicle-logo. Tchebichef moment invariants perform significantly better than Hu moment invariants and Zernike...
This paper presents a vision-based geometric alignment system for aligning the projectors in an arbitrarily large display wall. Existing algorithms typically rely on a single camera view and degrade in accuracy as the display resolution exceeds the camera resolution by several orders of magnitude. Naive approaches to integrating multiple zoomed camera views fail since small errors in aligning adjacent...
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