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In recent years, both online retail and video hosting service have been exponentially grown. In this paper, a novel deep neural network, called AsymNet, is proposed to explore a new cross-domain task, Video2Shop, targeting for matching clothes appeared in videos to the exactly same items in online shops. For the image side, well-established methods are used to detect and extract features for clothing...
Robotic graspable object recognition is a crucial ingredient in many exciting autonomous manipulation applications. However, identifying complex image features from limited data remains largely unsolved. In this paper, we leverage the advantages of two kinds of feature representation approaches, kernel descriptors and deep neural networks, to present a novel hierarchical feature learning framework...
Region-based Image Retrieval (RBIR), which bases itself on image segmentation rather than global features or key-point-based local features, is a branch of Content-based Image Retrieval. This paper proposes a novel RBIR-oriented image segmentation algorithm named Edge Integrated Minimum Spanning Tree (EI-MST). The difference between EI-MST and the traditional MST-based methods is that EI-MST generates...
We present the performance evaluation of different whole-image descriptors in visual loop closure detection. A whole-image descriptor here is defined as the one that does not require keypoint detection and is therefore fast to extract. In addition, it can be extremely compact to reduce storage requirement. This type of image descriptors are attracting an increasing amount of interest in appearance-based...
We propose a simple and effective method for visual loop closure detection in appearance-based robot SLAM. Unlike the Bag-of-Words (BoW hereafter) approach in most existing work of the problem, our method uses direct feature matching to detect loop closures and therefore avoid the perceptual aliasing problem caused by the vector quantization process of BoW. We show that a tree structure can be efficient...
This study proposes several categories of stimulus types under the visual oddball speller paradigm with application to brain-computer interface (BCI). Motion (including translation and rotation), zoom in/out, pattern rotation and sharpening types are tested and analysed. Results show that rotation and zooming type could obtain comparative or higher accuracies as well as information transfer rates...
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