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This paper describes an efficient image matting method by combining color and depth information. First, the depth image is segmented by variational level set. Then morphological operators, dilation and erosion, are used to form trimap of ROI (Region of Interest). Finally, with preprocessed depth image, color image and trimap as inputs, an RGB-D Bayesian matting method is proposed to estimate the alpha...
Diffusion Weighted MR Imaging (DWI) is routinely used for early detection of cerebral ischemic stroke. DWI with higher b-values (b=2000) provide improved sensitivity, higher conspicuity and reduced artifacts and thus improve the detectability of smallest infarcts than conventional DWI (b=1000). We propose a novel framework for accurately detecting stroke regions by combining information from multiple...
Logo spotting is of a great interest because it enables to categorize the document images of a digital library of scanned documents according to their sources, without any costly semantic analysis of their textual transcript. In this paper, we present an approach for logo spotting, based on the matching of keypoints extracted both from the query document images and a given set of logos (gallery) using...
Vision is an essential part in robotic systems, where attention plays an important role to cope with the complexity of the real world. Attention mechanisms have been proposed in the past to guide search and also segmentation of objects. Building on recent advances in affordable 3D sensing we first attend to objects using a novel saliency map, based on color and depth information. We then segment attended...
We propose a method for automatic segmentation of categorized objects from a collection of images in the same category, which employs a single auto-context model learned from all images without the need of using pixel level labels. Instead of extracting the salient objects from each image one by one, we extract the objects from all images simultaneously. The segmentation of the salient objects is...
Detecting and identifying Regions of Interest (ROIs) is an important task for navigation and retrieval services. In this paper, we focus on indoor scene images and detect object regions such as shop signs and merchandise. Our method is based on two approaches; 1) Indoor structure analysis from a single image by learning the types of scenes. 2) Detect ROIs by taking advantage of the relationship of...
Semantic interpretation and understanding of images is an important goal of visual recognition research and offers a large variety of possible applications. One step towards this goal is semantic segmentation, which aims for automatic labeling of image regions and pixels with category names. Since usual images contain several millions of pixel, the use of kernel-based methods for the task of semantic...
In this paper, we study and evaluate the application to image segmentation of a p-Laplacian based relaxation of the Cheeger Cut problem. Based on a l1 relaxation of the initial clustering problem, we show that these methods can outperform usual well-known graph based approaches, e.g., min-cut/max-flow algorithm or l2 spectral clustering, for unsupervised and very weakly supervised image segmentation...
This paper addresses the problem of remote sensing image multi-scale classification by: (i) showing that using multiple scales does improve classification results, but not all scales have the same importance; (ii) showing that image descriptors do not offer the same contribution at all scales, as commonly thought, and some of them are very correlated; (iii) introducing a simple approach to automatically...
This paper proposes an unsupervised bottom-up boundary detection algorithm, which is an improved surround suppression model based on orientation contrast. First, the candidate boundary set is obtained by the edge focusing algorithm. Second, the orientation contrast map is constructed using the response of Gabor filter. The suppression term is computed on orientation contrast map using steerable filter,...
This paper presents a method for recognizing aerial image categories based on matching graphlets(i.e., small connected subgraphs) extracted from aerial images. By constructing a Region Adjacency Graph (RAG) to encode the geometric property and the color distribution of each aerial image, we cast aerial image category recognition as RAG-to-RAG matching. Based on graph theory, RAG-to-RAG matching is...
Numerous videos are uploaded on video websites; most of them employ several kinds of camera operations for expanding FOV, emphasizing events, and expressing cinematic effect. To generate a profile of heterogeneous types of videos, an automatic video profiling method has been proposed to include both spatial and temporal information in a 2D image scroll. In this paper, we propose a uniformed scheme...
Breast ultrasound images are an important diagnostic factor for breast cancer detection. However, ultrasound imaging is intrinsically degraded by noise, resulting in a difficult detection of masses or nodules, and, most importantly, the evaluation of their size and shape. Computer-aided diagnosis figures as a major help factor, when it comes to analyzing this type of medical imaging. A fully automated...
We propose a novel method to segment the moving object in video clips. In this work, we introduce a region trajectory generation model based on graph clustering. Point trajectories are widely used to measure the motion similarity because of their unambiguity. However, region trajectories preserve object boundaries, while optical flow based point trajectories always ‘over-smooth’ to the background...
We present a region matching algorithm which establishes correspondences between regions from two segmented images. An abstract graph-based representation conceals the image in a hierarchical graph, exploiting the scene properties at two levels. First, the similarity and spatial consistency of the image semantic objects is encoded in a graph of commute times. Second, the cluttered regions of the semantic...
In this work, we propose a novel segmentation method based on the continuous max-flow (CMF) formulation of the Potts model incorporating the statistical shape model. We increase the robustness and accuracy of the Potts model by using the prior shape knowledge from the Principal Component Analysis (PCA) to represent the desired shape. Our multi-label model can segment several objects simultaneously...
Complementary information, when combined in the right way, is capable of improving clustering and segmentation problems. In this paper, we show how it is possible to enhance motion segmentation accuracy with a very simple and inexpensive combination of complementary information, which comes from the column and row spaces of the same measurement matrix. We test our approach on the Hopkins155 dataset...
In this paper, we propose a novel active contour method for image segmentation, which utilizes the advantages of the GAC and the LRAC methods. We consider the smoothing force of the GAC method and local region-based force of the LRAC method. The advantages of our method are as follows. First the proposed method a new region-based signed pressure force function, which can efficiently stop the contours...
2D Gel Electrophoresis image analysis is widely recognized as one of the most crucial processes following a proteomic experiment. Amongst its stages, detection and segmentation are the most challenging ones. The available software packages and techniques fail to detect and segment some of the real spots while they often detect a vast number of spurious spots. In this paper, an original approach to...
Gestalt principles have been studied for about a century and were used for various computer vision approaches during the last decades, but became unpopular because the many heuristics employed proved inadequate for many real world scenarios. We show a new methodology to learn relations inferred from Gestalt principles and an application to segment unknown objects, even if objects are stacked or jumbled...
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