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Saliency detection in images attracts much research attention for its usage in numerous multimedia applications. In this paper, we propose a saliency detection method based on optimization for RGBD images. With RGBD images, our method utilizes the depth channel to enhance the identification of background and foreground regions. We firstly generate new depth image by using non-linear transformation...
A variety of methods have been proposed for object level saliency detection, which is useful for many content-based computer vision applications. Unlike most previous work that integrate multiple low level cues to compute the saliency map, this paper presents a novel hierarchical optimization model. First, we compute a rough saliency map using HS method, and then, boundary and foreground seeds are...
We present an approach which enables real-time augmentation of an environment composed of materials with different texture and reflectance properties without the need of application-specific hardware or extensive preparation. Our solution uses a set of RGB images of a reconstructed model to optimize the reflectance parameters and light location. Each image is decomposed into its specular and diffuse...
In this paper, a new co-segmentation model by incorporating active contours based method and rewarding strategy is represented. We first generate co-segmentation energy function from two aspects. One is foreground similarity between image pairs. The other is background consistency in each single image. Then, we optimize the energy function through a mutual optimization approach. We verify the proposed...
In this paper, we propose a stereo matching algorithm with occlusion handling. In order to detect occlusion, we obtain an initial disparity map via optimization based on modified constant-space belief propagation (CSBP). Such a method is advantageous due to its low complexity. The initial disparity maps provide clue for occlusion detection. From such clue, an energy function for occlusion detection...
In this paper, we present a novel propagation-based stereo matching algorithm. Firstly, we select highly reliable depth points (i.e., support points) from both the high-textured areas and the low-textured ones in the rectified images. Then we propagate these support points along adjacent neighboring structure to produce the depths of other points in the image based on the triangulation derived from...
Classical frameworks for global 1D discrete optimisation: dynamic programming (DP) and belief propagation (BP) — presume well-posed problems with unique solutions. Ill-posed problems, being the most common in applied pattern recognition and computer vision, are regularised to restore well-posedness. However, typical heuristic regularisation does not guarantee that a set of multiple equivalent solutions...
This paper proposes a depth measurement error model of consumer depth cameras such as Microsoft KINECT, and its calibration method. These devices are originally designed for video game interface, thus, the obtained depth map are not enough accurate for 3D measurement. To decrease these depth errors, several models have been proposed, however, these models consider only camera-related parameters. Since...
Because manual image annotation is both expensive and labor intensive, in practice we often do not have sufficient labeled images to train an effective classifier for the new image classification tasks. Although multiple labeled image data sets are publicly available for a number of computer vision tasks, a simple mixture of them cannot achieve good performance due to the heterogeneous properties...
Computer vision imply several morphological and mathematical process. Image compression requires methods which involves algorithms and image processing techniques. AHBE system (Algorithm of Huffman combined with Bit Extraction method) provides a novel method to image compression when high quantity of light is present. Preprocessing images is useful in robotics applications to avoid noised data providing...
Shift-map image processing is a new framework based on energy minimization over a large space of labels. The optimization utilizes alpha-expansion moves and iterative refinement over a Gaussian pyramid. In this paper we extend the range of applications to image registration. To do this, new data and smoothness terms have to be constructed. We note a great improvement when we measure pixel similarities...
Purely bottom-up, unsupervised segmentation of a single image into foreground and background regions remains a challenging task for computer vision. Co-segmentation is the problem of simultaneously dividing multiple images into regions (segments) corresponding to different object classes. In this paper, we combine existing tools for bottom-up image segmentation such as normalized cuts, with kernel...
Projector-camera systems use computer vision to analyze their surroundings and display feedback directly onto real world objects, as embodied by spatial augmented reality. To be effective, the display must remain aligned even when the target object moves, but the added illumination causes problems for traditional algorithms. Current solutions consider the displayed content as interference and largely...
In this paper, we present a system for estimating the shadow field from a single natural image. The user of our system is provided with a broad brush to roughly specify the shadow boundary. As the user finishes drawing a stroke, the system starts to estimate the shadow field around the stroke and generates pretty accurate result even if the underlying surface is highly textured. Since the strokes...
Motion-based video segmentation remains an important problem in video processing. A promising approach that has received significant attention formulates the problem as an energy minimization within a MAP-MRF framework. While a great deal of progress has been made toward finding robust and computationally reasonable motion segmentation methods, automatically generating such a segmentation that performs...
Segmentation, or partitioning images into internally homogeneous regions, is an important first step in many computer vision tasks. In this paper, we attack the segmentation problem using an ensemble of low cost image segmentations. These segmentations are reconciled by applying recent techniques from the consensus clustering literature which exploit a non-negative matrix factorization (NMF) framework...
Typical computer vision systems usually include a set of components such as a preprocessor, a feature extractor, and a classifier that together represent an image processing pipeline. For each component there are different operators available. Each operator has a different number of parameters with individual parameter domains. The challenge in developing a computer vision system is the optimal choice...
Potato quality control has improved in the last years thanks to automation techniques like machine vision, mainly making the classification task between different quality degrees faster, safer and less subjective. We present a system that classifies potatoes depending on their external defects and diseases. Firstly, some image processing techniques are used to segment and analyze the potatoes. Then,...
In this paper we propose a method that smartly improves occlusion handling in stereo matching using trinocular stereo. The main idea is based on the assumption that any occluded region in a matched stereo pair (middle-left images) in general is not occluded in the opposite matched pair (middle-right images). Then two disparity space images (DSI) are merged in one composite DSI. The proposed integration...
In this paper, we present a new approach for recovering spacetime-consistent depth maps from multiple video sequences captured by stationary, synchronized and calibrated cameras for depth based free viewpoint video rendering. Our two-pass approach is generalized from the recently proposed region-tree based binocular stereo matching method. In each pass, to enforce temporal consistency between successive...
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