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There have been significant progresses in single image super-resolution (SR) using deep convolutional neural network. In this paper, we propose a modified deep convolutional neural network model incorporated with image texture priors for single image SR. The model consist of a particular feature extraction layer followed by image reconstruction process, aiming to centralize on the image texture information...
We propose an object detection system that depends on position-sensitive grid feature maps. State-of-the-art object detection networks rely on convolutional neural networks pre-trained on a large auxiliary data set (e.g., ILSVRC 2012) designed for an image-level classification task. The image-level classification task favors translation invariance, while the object detection task needs localization...
This paper proposed a new 2-dimension-code-like feature based on the Haar-like feature proposed by Viola et al. The feature can be calculated in different scales rapidly once the integral image is calculated and this characteristic is inherited from the Haar-like feature. Instead of reducing the dimensions after calculating redundant features, we calculate feature vectors with fixed dimensions for...
Our goal is to detect people in highly articulated poses, including bending, crouching, etc. Such formidable diversity in human poses makes detection much more difficult than for pedestrian poses. ??Divide-and-conquer?? is a favorable strategy for detecting objects with large intra class variations, which splits object instances into several subcategories and trains relatively simple classifiers for...
Support Vector Machines (SVM) has drawn extensive interests due to its attractive properties, based on which some dimensionality reduction methods have been proposed. However, SVM here only serves as a feature extractor rather than a classifier, the extracted features are in turn used as inputs to other different classifiers. In this paper, a novel and simpler SVM-induced Dimensionality Reduction...
In this paper, a novel method based on former cases for plastic surgery prediction is presented. This method takes a pre-operative frontal facial picture as an input. Landmarks of the face are then extracted and constitute a distance vector. As a set of facial parameters, such a vector is entered into either a support vector regression (SVR) predictor or a k-nearest neighbor (KNN) predictor which...
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