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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)
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)
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)
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)
Detecting visually attentive regions of an image is a challenging but useful issue in many multimedia applications. In this paper, we describe a method to extract visual attentive regions in images using subspace estimation and analysis techniques. The image is represented in a 2D space using polar transformation of its features so that each region in the(More)
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)
We present a new method to classify human activities by leveraging on the cues available from depth images alone. Towards this end, we propose a descriptor which couples depth and spatial information of the segmented body to describe a human pose. Unique poses (i.e. codewords) are then identified by a spatial-based clustering step. Given a video sequence of(More)
Saliency detection is useful for high level applications such as adaptive compression, image retargeting, object recognition, etc. In this paper, we introduce an effective region-based solution for saliency detection. We first use the adaptive mean shift algorithm to extract superpixels from the input image, then apply Gaussian Mixture Model (GMM) to(More)