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Motion estimation from underwater images is an active research area of the vision system devoted to the applications of robots. In this paper, a vision based system for tracking the motion of moving objects is presented. The aim of this paper is to give an optimal performance against radiometric features such as non-uniform lighting, blurring and noise. The moving object detection is performed by...
Graph Matching (GM) plays an essential role in computer vision and machine learning. The ability of using pairwise agreement in GM makes it a powerful approach in feature matching. In this paper, a new formulation is proposed which is more robust when it faces with outlier points. We add weights to the one-to-one constraints, and modify them in the process of optimization in order to diminish the...
Object tracking is one of the most important components in numerous applications of computer vision. In this paper, the target is represented by a series of binary patterns, where each binary pattern consists of several rectangle pairs in variable size and location. As complementary to traditional binary descriptors, these patterns are extracted in both the intensity domain and the gradient domain...
In this paper, we present an improved version of MOBIL descriptor [1] (Improved MOments based BInary differences for Local description), which introduces two main contributions. The first one is the use of geometric information for the binary test instead of the classical intensity binary test, to get more precision in the description step. The second one is to attribute two bits for each test, to...
In this paper, we present one underwater image stitching model combined with the Scale Invariant Feature Transform (SIFT) and the wavelet fusion. Poor visibility in the sea and the variations in the illumination, viewpoints, etc., have been comprehensively taken into consideration for image matching. Wavelet fusion is then made full use of to undertake the underwater image mosaic. It is shown in the...
The segmentation of video sequences into regions underlying a coherent motion is one of the most useful processing for video analysis and coding. In this paper, we propose an algorithm that exploits the advantages of both top-down and bottom-up techniques for motion field segmentation. To remove camera motion, a global motion estimation and compensation is first performed. Local motion estimation...
The segmentation of video sequences into regions underlying a coherent motion is one of the most useful processing for video analysis and coding. In this paper, we propose an algorithm that exploits the advantages of both top-down and bottom-up techniques for motion field segmentation. To remove camera motion, a global motion estimation and compensation is first performed. Local motion estimation...
This paper proposes and discusses the use of motion-oriented connected operators for sprite creation. Motion-oriented connected operators are tools allowing the simplification of frames by removing objects that do not follow a given motion. They combine features of filtering and segmentation tools. They are, however, less computationally expensive than most motion-oriented segmentation algorithms...
Robust object tracking is a challenging task in computer vision. Color features have been popularly used in visual tracking. However, most conventional color-based trackers either rely on luminance information or use simple color representations for image description. During the tracking sequences, the perceived color of the target may change because of the varying lighting conditions. In this paper,...
Local feature descriptor plays a fundamental role in many visual tasks, and its rotation invariance is a key issue for many recognition and detection problems. This paper proposes a novel rotation invariant descriptor by ordinal pyramid pooling of local Fourier transform features based on their radial gradient orientations. Since both the low-level feature and pooling strategy are rotation invariant,...
Definition and extraction of local features play a very important role in image retrieval (IR), pattern recognition and computer vision. Fast growth of technology today calls for local features to be as compact as possible toward real-time and limited bandwidth applications. In this paper, we study the problem of representing images in a compact way to achieve low bit-rate transmission while maintaining...
Repetitive patterns exist widely in real world images, and matching images with plenty of repetitive patterns remains a challenging task. We present in this paper a novel feature matching algorithm of images with notably repetitive patterns, in which a reliable initial correspondence set is established, purified and propagated using a voting strategy, incorporating a local geometrical constraint....
Detecting moving objects in video sequences is a vital task in many computer vision applications. Many different algorithms have been proposed to detect moving objects in successive frames. Gaussian Mixture Model (GMM) is a well-known algorithm that is robust against repetitive motions, illumination changes and long-term scene changes. Adaptive Noise Cancelation (ANC) is another algorithm that has...
Identifying the parameters of a model such that it best fits an observed set of data points is fundamental to the majority of problems in computer vision. This task is particularly demanding when portions of the data has been corrupted by gross outliers, measurements that are not explained by the assumed distributions. In this paper we present a novel method that uses the Least Quantile of Squares...
This paper presents a way of using the Iteratively Reweighted Least Squares (IRLS) method to minimize several robust cost functions such as the Huber function, the Cauchy function and others. It is known that IRLS (otherwise known as Weiszfeld) techniques are generally more robust to outliers than the corresponding least squares methods, but the full range of robust M-estimators that are amenable...
Despite recent advances, the extraction of optical flow with large displacements is still challenging for state-of the-art methods. The approaches that are the most successful at handling large displacements blend sparse correspondences from a matching algorithm with an optimization that refines the optical flow. We follow the scheme of Deep-Flow [33]. We first extract sparse pixel correspondences...
We identify a novel instance of the background subtraction problem that focuses on extracting near-field foreground objects captured using handheld cameras. Given two user-generated videos of a scene, one with and the other without the foreground object (s), our goal is to efficiently generate an output video with only the foreground object (s) present in it. We cast this challenge as a spatio-temporal...
We propose a new categorical object recognition algorithm robust to scale changes. We first partition an input image into k regions by using depth data from an RGB-D sensor, and then we estimate the object scale for each partitioned region. Finally, scaled model is applied to recognize the object.
Video has difficulty to maintain consistent intensity and color tone from frame to frame. Particularly, it happens when imaging device such as black box camera has to deal with fast changing illumination environment. However, conventional automatic white balance algorithms cannot handle this good enough to maintain tone consistency, which is observed in most commercial black box products. In this...
In this paper, we address the problem of interactive image segmentation which segments an image based on user-supplied scribbles. For this purpose, we propose a novel framework that provides consistent performance robust to the location of input seeds. Most of the existing methods, especially random walk-based approaches, strongly depend on initial seed positions, which differ from one user to another...
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