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In this paper, we propose a patched-based deep Boltzmann shape priors for visual tracking. The shape priors are generated from deep Boltzmann machine network. The network consists of three layers of hidden and visible units. The generated shapes not only maintain general shapes from a variety of poses, but also entail local modifications with high probability.
This paper proposed a workshop to introduce the use of computational tools and methods to analyze educational data. The workshop will demonstrate three different contexts in which these tools can be used to visualize and characterize patterns within educational data, and validate them using statistical techniques. Participants in this workshop will have the opportunity to learn how to implement these...
We propose a method for transferring an arbitrary style to only a specific object in an image. Style transfer is the process of combining the content of an image and the style of another image into a new image. Our results show that the proposed method can realize style transfer to specific object.
This paper addresses the problem of maritime vessel identification by exploiting the state-of-the-art techniques of distance metric learning and deep convolutional neural networks since vessels are the key constituents of marine surveillance. In order to increase the performance of visual vessel identification, we propose a joint learning framework which considers a classification and a distance metric...
This study aims to provide an overview on the intersection and interaction between architecture, urban modeling, planning fields and computer vision field. The reflection of the methods and approaches of fields such as visual recognition, natural language processing, data mining and data visualization onto architecture and urban studies are investigated and potentials of inter/transdisciplinary encounters...
With commercial prosthetic hands, executing some everyday movements, for example, concurrent grasp and bending of the wrist to pick up an object from a high shelf, is very challenging. We hypothesised that after the loss of the hand, the flexibility of the nervous system enables prosthesis users to bypass the innate biomechanical constraints on upper-limb muscles and joints. We show that users are...
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Computing matching cost by Convolutional neural networks(CNNs) work well in fetching accurate dense disparity maps. But these methods still have problems: (1) they always employ equal weights for left and right images in convolutional layers, losing relational information of patches; (2) they don't solve the balance between patches' size and processing efficiency, the larger size the more information...
Deep convolutional neural networks is a recently developed method that yields very successful results in image classification. Deep neural networks, which have a high number of parameters, require a large amount of data to avoid overfitting during training. For applications in which the available data is not adequate to train a deep neural network from the scratch, deep neural networks trained for...
As one of the basic properties of image, texture undoubtedly affect the image saliency. We introduce a texture contrast based salient region detection method, which first divide an input image into several nearly uniform super pixels, then analyze the texture feature and calculate the texture differences between regions to detect salient region. In order to obtain a better saliency map, we also optimize...
This paper deals with the performance evaluation of image segmentation. The goal of this work is to show some techniques that enable the comparison of different segmentation results. We first present a visualization method that facilitates the visual evaluation of a segmentation result. Next, we focus on unsupervised evaluation criteria that do not take into account any a priori knowledge to quantify...
Recognizing human actions in video has gradually attracted much attention in computer vision community, however, it also faces many realistic challenges caused by background clutter, viewpoint changes, variation of actors appearance. These challenges reflect the difficulty of obtaining a clean and discriminative video representation for classification. Recently, VLAD (Vector of Locally Aggregated...
A frequency domain algorithm using the image anisotropic is proposed for visual saliency in this paper. Based on the SSS algorithm, we generate 8 saliency maps corresponding 8 Gaussian kernels. Then, use the Renyi entropy to divide these saliency maps into two classes and select one optimal map for each class. Finally, combine these two optimal maps for the finally saliency map. The experimental comparison...
It is well known that using the correct features for pattern recognition is far more important than using a sophisticated classifier. A high order classifier, given inadequate features, will produce poor results. Low-level formed are combined to form mid-level features, which have much more discriminating power. Yet, the challenge of feature selection is often neglected in the literature. The literature...
This paper proposes a general method for size optimization in dense sampling to obtain a better representation of an image. Our method can be utilized to improve the performance of image classification and other tasks. We discuss the spatial consistency in global-scope restrained descriptors, by analyzing the appropriate sampling size. We apply the low rank method to solve the representative matrix...
Typically, the saliency map of an image is usually inferred by only using the information within this image. While efficient, such single-image-based methods may fail to obtain reliable results, because the information within a single image may be insufficient for defining saliency. In this paper, we propose a novel idea of learning with labeled images and adopt a new paradigm called sample specific...
Detection of salient regions in natural scenes is useful for computer vision applications, such as image segmentation, object recognition, and image retrieval. In this paper, we propose a new bottom-up visual saliency detection method after analyzing the weakness of the frequency tuned saliency detection method. The proposed method uses the YCbCr color space to present the image and computes the Mahalanobis...
In this paper, we proposed intensity comparison based compact descriptor for mobile visual search. For practical mobile applications, the low complexity and the descriptor size are more preferable, and many algorithms such as SURF, CHoG, and PCA-SIFT have been proposed. However, these approaches focused on not the feature description but the extraction time and the size of the feature. This paper...
This paper introduces a novel video presentation term spatial-temporal pyramid sparse coding (STPSC) which characterizes both the spatial and temporal aspects of the video. Specifically, the co-occurrences of visual words are computed with respect to the spatial layout and the sequencing of the features in the video. The representation captures both the spatial arrangement and the temporal relationship...
Visual vocabulary is now widely used in many video analysis tasks, such as event detection, video retrieval and video classification. In most approaches the vocabularies are solely based on statistics of visual features and generated by clustering. Little attention has been paid to the interclass similarity among different events or actions. In this paper, we present a novel approach to mine the interclass...
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