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Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
Minimally invasive surgical and diagnostic systems rely on endoscopic images of internal organs to assist medical tasks. Specular highlights are common on those images due to the strong reflectivity of the mucus layer on the organs and the relatively high intensity of the light source. This is a significant source of error that can affect the systems' performance. In this paper, we propose a segmentation...
Depth Image Based Rendering (DIBR) is a technology which converts two dimensional images to three dimension using colour image and its associated depth image. The performance of any DIBR system depends on the perfection of the depth image. Holes/disocclusion will occur in the virtual views generated if the depth map is not perfect. Since holes occur in the virtual views when the intensity changes...
Conventional unsupervised image segmentation methods return many superpixels or object parts and thus tend to over-segmentation. In this paper, we present a novel post-processing approach for unsupervised object-level image segmentation (UnOLIS). Starting with the results of any conventional unsupervised segmentation method, we first combine a global region-based saliency and a robust background feature...
Foreground segmentation is a fundamental method in computer vision. Traditional foreground segmentation algorithms are sensitive to blurry degree of background, smooth foreground regions and camouflage foreground. To deal with these problems, we use light field images as input by exploiting its focusness cue. In this paper, we propose an automatic foreground segmentation algorithm for light field...
In 3D video (3DV) and free-viewpoint video (FVV), it is vitally important to detect the errors and assess depth quality. However, since ground-truth depth maps are often unattainable, assessing depth quality without reference becomes an imperative task for many applications. This research considers the texture-plus-depth format in 3DV and FVV, and focuses on the misalignment error at depth discontinuities...
Kinect depth maps often contain missing data, or "holes", for various reasons. Most existing Kinect-related research treat these holes as artifacts and try to minimize them as much as possible. In this paper, we advocate a totally different idea - turning Kinect holes into useful information. In particular, we are interested in the unique type of holes that are caused by occlusion of the...
We propose two methods for detecting transparent text in images and recovering the background behind the text. Although text detection in natural scenes is an active research area, most current methods are focused on non-transparent text. To detect transparent text, we developed an adaptive edge detection method for edge-based text detection that can accurately detect text even under low contrast,...
Road detection is an important task in intelligent transportation system (ITS). Over the past few decades, several vision-based approaches for road detection have been proposed and most of them are based on color information. However, color information may result in false road detection under variation of illumination conditions. To deal with illumination problems, we propose an illumination invariant...
In this paper, we present a new method for text extraction in real scene images. We propose first a skeleton based descriptor to describe the strokes of the text candidates that compose a spatial relation graph. We then apply the graph cuts algorithm to label the nodes of the graph as text or non-text. We finally refine the resulted text lines candidates by classifying them using a kernel SVM. To...
The state-of-the-art interactive image segmentation algorithms are sensitive to the user inputs and often unable to produce an accurate boundary with a small amount of user interaction. They frequently rely on laborious user editing to refine the segmentation boundary. In this paper, we propose a robust and accurate interactive method based on the recently developed continuous-domain convex active...
Inspired by the success of MRF models for solving object segmentation problems, we formulate the binarization problem in this framework. We represent the pixels in a document image as random variables in an MRF, and introduce a new energy (or cost) function on these variables. Each variable takes a foreground or background label, and the quality of the binarization (or labelling) is determined by...
In this paper, a novel fast approach is proposed to achieve image segmentation in color image. This method helps to refine the foreground regions and achieves the goal of robust color image segmentation throw the following four steps. First, modified Karhunen-Loeve transform is performed to reduce the redundant component, thus selecting the most important part of the color images. Second, a multi-threshold...
Video text provides high-level semantic information. However, due to the complex background in video, it is of great difficulty to extract text efficiently. Although many methods hold assumptions on single feature, such as texture, connected areas etc., there are still some problems in dealing with multilingual text extraction because of its quite different appearance. In this paper, the color and...
This paper presents a new method for robust color image segmentation based on tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. First, an adaptation of tensor voting to both image denoising and robust edge detection is applied. Second, pixels in the filtered image are classified into likely-homogeneous and likely-inhomogeneous by means of the...
A novel method for line matching is proposed. The basic idea is to use tentative point correspondences, which can be easily obtained by keypoint matching methods, to significantly improve line matching performance, even when the point correspondences are severely contaminated by outliers. When matching a pair of image lines, a group of corresponding points that may be coplanar with these lines in...
License plate recognition systems have been used extensively for many applications. In order to recognize a license plate efficiently, however, the location of the license plate must be detected in the first place. In our method, the car image first is divided into a set of 5??5 non-overlapping block and a local direction is defined for each block. The M??N car image is converted to a direction image...
In this paper, we present a local graph matching based method for tracking cells and cell divisions in noisy images. We work with plant cells, where the cells are tightly clustered in space and computing correspondences across time can be very challenging. The local graph matching method is able to track the cells and cell divisions even when significant portions of the images are corrupted due to...
Mobile robots rely on their ability of scene recognition to build a topological map of the environment and perform location-related tasks. In this paper, we describe a novel lightweight scene recognition method using an adaptive descriptor which is based on color features and geometric information for omnidirectional vision. Our method enables the robot to add nodes to a topological map automatically...
This paper presents a graph based scheme for color text recognition in images and videos, which is particularly robust to complex background, low resolution or video coding artifacts. This scheme is based on a novel method named the image text recognition graph (iTRG) composed of five main modules: an image text segmentation module, a graph connection builder module, a character recognition module,...
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