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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...
When user uploads a video clip to the video sharing websites, a video thumbnail needs to be generated as the cover to represent the video content. In this paper, a novel video thumbnail generation framework is presented. For generating a good thumbnail, three criteria are considered: (1) the thumbnail should be distinct, in order to make user feel more pleasant; (2) the thumbnail should be easily...
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...
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...
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...
The measurement or evaluation and clinical significance of human sperm morphology has always been and still is a controversial aspect of the semen analysis for the determination of a male's fertility potential. The evaluation of sperm size, shape and morphological smear characteristics should be assesed by carefully observing a stained sperm sample under a microscope. In order to avoid subjectivity,...
We propose a sentence generation method that describes images. We do not use image processing technique in our proposed method. Human annotated image tags are used as image information to generate sentence. By using human annotated tags, we think this enables to describe image more relevant and user specific. Our method uses Kyoto University's case frame data and Google N-gram to generate candidate...
We describe a novel learning scheme for hidden dependencies in video streams. The proposed scheme aims to transform a given sequential stream into a dependency structure of particle populations. Each particle population summarizes an associated segment. The novel point of the proposed scheme is that both of dependency learning and segment summarization are performed in an unsupervised online manner...
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...
We propose an unsupervised image segmentation method based on texton similarity and mode seeking. The input image is first convolved with a filter-bank, followed by soft clustering on its filter response to generate textons. The input image is then superpixelized where each belonging pixel is regarded as a voter and a soft voting histogram is constructed for each superpixel by averaging its voters'...
We present a generic framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs in depth maps. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α-β swap Graph-cuts algorithm. Moreover,...
Supervoxel segmentation has strong potential to be incorporated into early video analysis as superpixel segmentation has in image analysis. However, there are many plausible supervoxel methods and little understanding as to when and where each is most appropriate. Indeed, we are not aware of a single comparative study on supervoxel segmentation. To that end, we study five supervoxel algorithms in...
While bottom-up and top-down processes have shown effectiveness during predicting attention and eye fixation maps on images, in this paper, inspired by the perceptual organization mechanism before attention selection, we propose to utilize figure-ground maps for the purpose. So as to take both pixel-wise and region-wise interactions into consideration when predicting label probabilities for each pixel,...
Many cues have been proposed for contour detection or image segmentation. These include low-level image gradients to high-level information such as the identity of the objects in the scene or 3D depth understanding. While state-of-the-art approaches have been incorporating more cues, the relative importance of the cues is unclear. In this paper, we examine the relative importance of low-, mid- and...
Depth ordering is instrumental for understanding the 3D geometry of an image. Humans are surprisingly good at depth ordering even with abstract 2D line drawings. In this paper we propose a learning-based framework for depth ordering inference. Boundary and junction characteristics are important clues for this task, and we have developed new features based on these attributes. Although each feature...
Curve fragments, as opposed to unorganized edge elements, are of interest and use in a large number of applications such as multiview reconstructions, tracking, motion-based segmentation, and object recognition. A large number of contour grouping algorithms have been developed, but progress in this area has been hampered by the fact that current evaluation methodologies are mainly edge-based, thus...
Automatic detection of human cell is one of the most common investigation methods that may be used as part of a computer aided medical decision making system. In this paper we present an efficient algorithm, based on the cluster analysis and the vector quantization techniques for human cell image detection. First, we perform the edge detection methods to specify the desired region of any object in...
We solve the problem of localizing and tracking household objects using a depth-camera sensor network. We design and implement Kin sight that tracks household objects indirectly -- by tracking human figures, and detecting and recognizing objects from human-object interactions. We devise two novel algorithms: (1) Depth Sweep -- that uses depth information to efficiently extract objects from an image,...
The human brain with all its faculties and intricacies has fascinated many generations of researchers [1] and will likely be the final frontier of science. Understanding the principles underlying the brain's higher-order cognitive functions is indeed a major challenge and will profoundly impact our views on what defines a human being. On a more down-to-earth level, knowledge of the structure, function,...
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