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Salient region-based image retrieval is one of the hotspots in the domain of content-based image retrieval; however the metrics about region saliency is not in the uniform frame. The research on visual attention has shown that the factors including color, texture, scale and position influence on visual perception mostly. Consequently, the algorithm of salient region extraction is proposed by using...
In this paper, a simple method to extract regions of interest (ROI) from images is proposed. In the field of image processing, intensity, color and orientation are commonly used features for saliency map generation in most visual attention model. However, texture feature can contribute to the guidance of attention in a bottom-up model. We consider texture contrast as a component of final saliency...
Image saliency attempts to describe the most conspicuous part in an input image by mimicking human visual selective attention mechanism. Naturally, it could be adopted for improving object recognition. To demonstrate the effectiveness of saliency in object recognition, this paper proposes a salient hierarchical model. First, the traditional saliency model is modified for more robust saliency estimation...
The paper describes a new method of detecting human figures in the video scene in real time. This problem can be found, for example, in the protection of buildings where unauthorized persons have access, surveillance of persons in common areas such as shopping centers, airport lounges, etc. For the detection of the contour of a human figure the HOG algorithm is often used which detects the human figure...
In this paper, a scheme is proposed for solving segmentation problem when people engage in body contact in a video sequence. First, the body parts belonging to each interacting person are extracted using the deformable triangulation technique. The color blobs of each person are learned by Gaussian mixtures model on the fly before the person is interacting with another. Finally, those learned blob...
This paper addresses the problem of extracting perceptually dominant color names of images. Our approach is motivated by the principle that the pixels corresponding to one dominant color name identified by human are often context dependent, spatially connected and form a perceptually meaningful region. Our algorithm first learns the probabilistic mapping from a RGB color to a color name. Then, a double-threshold...
Natural interaction between application user of a Virtual Environment (VE) and autonomous characters is a key challenge in enhancing the realism in virtual environments. Traditional interaction methods with autonomous characters such as virtual humans using keyboard and mouse do not provide an intuitive user experience. This paper presents an approach that enables user to communicate and control virtual...
Human gesture as a natural interface plays an utmost important role for achieving intelligent Human Computer Interaction (HCI). Human gestures include different components of visual actions such as motion of hands, facial expression, and torso, to convey meaning. So far, in the field of gesture recognition, most previous works have focused on the manual component of gestures. In this paper, we present...
Selective visual attention is a kind of mechanism of the primate visual system for rapidly focusing on attractive objects or regions in visual environment. Numerous visual attention models have been developed and optimized over the past decades. Most of the existing models concentrate on static monocular image, but little attention has been devoted to stereo depth information which is an important...
In order to reduce the impact of image background and illumination in face locating, this dissertation has put forward a new algorithm to locate human eyes, applying YCbCr model to extract human face region, and then locating eyes correctly according to geometry and pixel features of human eyes. Experimental results show that this algorithm can be applicable in images with different backgrounds and...
In order to solve the problem that Omni-directional faces, which was in images with complex context, couldn't be detected, an eye-core based face detection model was proposed. In the proposed model, the technique of HSI based skin detection combined with eye-core detection was used to detect eyes, and then image rotation, features extraction from images and neural network based classification were...
Visual attention is useful for computer vision and it has been applied in image compression and object recognition. In existing methods on saliency detection, most of them are unrelated to the depth feature. So we propose a bottom-up saliency detection model that combines the depth feature with region contrast based saliency model and the precision and recall rate of our algorithm is higher than those...
Social network analysis is a popular topic in social science. However, it needs a lot of human labor to get the information in psychological analysis. In this paper, we propose a multi-camera based evaluation system which can automatically track and recognize the human activities in an environment, and then build the corresponding social network and personality graphs. The proposed system contains...
We present the framework for a color contrast enhancement using an illumination estimation, color balancing and color dynamic range expansion based on characteristics of a object reflectance, effect of illumination and human face color depending on human race. The method aims to emulate the way in which the human visual system discriminates original color and opposite color for increasing color contrast...
Computer vision is a field that includes methods for acquiring, processing, analyzing and understanding images. In the embedded world, computer vision applications have to fight with limited processing power and limited resources to achieve optimized algorithms and high performance. This paper presents work on implementing a human tracking system on both Intel based PC platform and embedded systems...
One challenge when tracking objects is to adapt the object representation depending on the scene context to account for changes in illumination, coloring, scaling, etc. Here, we present a solution that is based on our earlier approach for object tracking using particle filters and component-based descriptors. We extend the approach to deal with changing backgrounds by using a quick training phase...
We explore recently proposed Bayesian nonparametric models of image partitions, based on spatially dependent Pitman-Yor processes. These models are attractive because they adapt to images of varying complexity, successfully modeling uncertainty in the structure and scale of human segmentations of natural scenes. By developing substantially improved inference and learning algorithms, we achieve performance...
Mining patterns of human behavior from large-scale mobile phone data has potential to understand certain phenomena in society. The study of such human-centric massive datasets requires new mathematical models. In this paper, we propose a probabilistic topic model that we call the distant n-gram topic model (DNTM) to address the problem of learning long duration human location sequences. The DNTM is...
In this paper, we address a practical problem of cross-scenario clothing retrieval — given a daily human photo captured in general environment, e.g., on street, finding similar clothing in online shops, where the photos are captured more professionally and with clean background. There are large discrepancies between daily photo scenario and online shopping scenario. We first propose to alleviate the...
We propose a novel mode of feedback for image search, where a user describes which properties of exemplar images should be adjusted in order to more closely match his/her mental model of the image(s) sought. For example, perusing image results for a query “black shoes”, the user might state, “Show me shoe images like these, but sportier.” Offline, our approach first learns a set of ranking functions,...
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