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In this paper, we present a new image matting algorithm that achieves state-of-the-art performance on a benchmark dataset of images. This is achieved by solving two major problems encountered by current sampling based algorithms. The first is that the range in which the foreground and background are sampled is often limited to such an extent that the true(More)
Color information is leveraged by color sampling-based matting methods to find the best known samples for foreground and background color of unknown pixels. Such methods do not perform well if there is an overlap in the color distribution of foreground and background regions because color cannot distinguish between these regions and hence, the selected(More)
We present a new example-based method to colorize a gray image. As input, the user needs only to supply a reference color image which is semantically similar to the target image. We extract features from these images at the resolution of superpixels, and exploit these features to guide the colorization process. Our use of a superpixel representation speeds(More)
Matting is a useful tool for image and video editing where foreground objects need to be extracted and pasted onto a different background. A matte is represented by α which defines the opacity of a pixel and is a value in [0, 1], with 0 for background (B) pixels and 1 for foreground (F) pixels. There are three main approaches for image matting: In(More)
Depth information has been shown to affect identification of visually salient regions in images. In this paper, we investigate the role of depth in saliency detection in the presence of (i) competing saliencies due to appearance , (ii) depth-induced blur and (iii) centre-bias. Having established through experiments that depth continues to be a significant(More)
Detection of salient regions in images is useful for object based image retrieval and browsing applications. This task can be done using methods based on the human visual attention model [1], where feature maps corresponding to color, intensity and orientation capture the corresponding salient regions. In this paper, we propose a strategy for combining the(More)
Human motion detection is a fundamental task for many computer vision tasks. The most popular method for motion detection is background subtraction where a background model needs to be maintained. In this paper an entropy based method for human motion detection is described which makes no use of background model. The difference image between consecutive(More)
In this paper, we present a new classification scheme based on Support Vector Machines (SVM) and a new texture feature, called texture correlogram, for high−level image classification. Originally, SVM classifier is designed for solving only binary classification problem. In order to deal with multiple classes, we present a new method to dynamically build up(More)
A new motion feature for video indexing is proposed in this paper. The motion content of the video at pixel level, is represented as a Pixel Change Ratio Map (PCRM). The PCRM enables us to capture the intensity of motion in a video sequence. It also indicates the spatial location and size of the moving object. The proposed motion feature is the motion(More)