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Identification of invariant image descriptors is an integral task for many computer vision applications such as image registration, object recognition, and object tracking. The detected features should be invariant to geometric transformations such as rotation and translation, as well photometric variations due to differing lighting conditions. In this work, we propose a unique and effective region...
The spatial geometrical features of the objects are widely used in the image matching and retrieval. The popular algorithms of the spatial feature detection are designed for gray images, which failed to make use of the color information in color images. However, the color information is absolutely nontrivial in discrimination of different objects, vacancy of which would lead to miss judgment. Based...
SIFT (Scale Invariant Feature Transform) has proved to be the most robust local invariant feature descriptor in object recognition and matching. Being designed mainly for the gray images, SIFT shows its vulnerability when deal with color images. To overcome this problem and increase the descriptor's distinctiveness, we introduce a new descriptor, a combination of the SIFT approach and the improved...
Broadcast sports video report is expected be the generated player data in real time. Therefore, uniform number recognition need to fast computing. Covariance feature has possibilities to do it in real time. We present a method for uniform number recognition using the covariance feature as a region descriptor. The covariance feature is represented by the covariance matrix of image features such as...
In the field of nanotechnology, tracking freely swimming microorganisms under a microscope is difficult. An image of the target often rotates and changes shape as it moves. Further, the difficulty increases with background changes and fluctuations of colors and the brightness because of differences in the surrounding environment. To address these problems, we propose a tracking method combined with...
We are focused on how to describe a common image point distinctively, make its descriptor concise and invariant to general image transformations. We use neighborhood pixel characteristics, including HSV color space, Gaussian-weighted gradient magnitudes and orientations, sampled in specific window around interest point to enhance the description. The enhanced point descriptor (EPD) is a covariance...
This paper presents a three-step framework to remove the highlight exists on objects in certain conditions. Unlike traditional HDR (High Dynamic Range) technology requires multiple registrated image sequence; our method needs only two arbitrary images. SURF (Speeded Up Robust Features) matching algorithm is first applied to find corresponding point pairs between images; homography is then found by...
This paper presents a vision-based obstacle avoidance design using a monocular camera onboard a mobile robot. An image processing procedure is developed to estimate distances between the robot and obstacles based-on inverse perspective transformation (IPT) in image plane. A robust image processing solution is proposed to detect and segment navigatable ground plane area within the camera view. The...
Aiming at the cartoon industry which has tremendous applications, a cartoon scene matching system is designed using an improved matching algorithm which fuses color feature and CSIFT (Colored invariant feature transform) feature. The global color statistical feature is obtained by calculating the distribution of three parameters: H, S and I. The CSIFT global feature is calculated based on the color...
In this paper, an improved Poisson matting method is proposed to segment participants in real-time at a tele-presence session from their background. In order to improve the matting process, we introduce the concept of color distance and extend the standard Poisson matting using patch matching. The idea of patch based matching algorithm, which is widely used in texture synthesis is adopted here to...
In this paper, a new method for content-based video copy detection is presented. This method includes fingerprint extracting and matching phases. In the fingerprint extracting phase, a video is represented by a set of Speeded Up Robust Features (SURF), which outperforms other local features. In the fingerprint matching phase, the Locality Sensitive Hashing (LSH) is applied to efficiently detect video...
In this paper, we present the fusional feature composed of Affine-SIFT, MSER and color moment invariants. The fusional feature is more robust and distinctive than a single local feature. Instead of adding three local features together simply, an efficient two-level matching strategy is devised with the fusional feature, which speeds up the establishment of the local correspondences. To remove partial...
In this paper, we propose a hybrid approach for addressing feature-based matching problem. We aim to obtain robust and accurate correspondence between features from image frames under unknown and unstructured environments. The approach incorporates image texture analysis, 2-D analytic signal theory and color modeling. It takes advantage of geometric invariant property in texture and monogenic signal...
Finding reliable correspondence in two or more images remains a difficult and critical step in many computer vision tasks. The performance of descriptors determines the matching results directly. Compared with other descriptors, the Scale Invariant Feature Transform (SIFT) has been used widely for its superiority in invariant attributes, while it will fail in the case of locally visual aliasing. To...
This paper presents a novel design of visual servo control of a mobile manipulator for autonomous grasping of a target object. In this design, scale invariant feature transform (SIFT) algorithm is adopted to search and recognize the object to grasp. Random sample consensus (RANSAC) algorithm is used to remove outliers and find the refined homography matrix between database and current image. Robust...
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...
This paper presents a novel approach for matching 2D points between a video projector and a digital camera. Our method is motivated by camera-projector applications for which the projected image needs to be warped to prevent geometric distortion. Since the warping process often needs geometric information on the 3D scene that can only be obtained from triangulation, we propose a technique for matching...
This paper presents a novel approach that achieves complete matching of 3D dynamic surfaces. Surfaces are captured from multi-view video data and represented by sequences of 3D manifold meshes in motion (3D videos). We propose to perform dense surface matching between 3D video frames using geodesic diffeomorphisms. Our algorithm uses a coarse-to-fine strategy to derive a robust correspondence map,...
We propose a novel local feature descriptor named Enhanced Point Descriptor (referred to as EPD) for dense stereo matching applications. The existing local feature descriptors, e.g., SIFT and SURF, can only be used to represent sparse image extreme points which make stereo matching sparsely. We design EPDs to represent common image points. To generate an EPD, we first build image characteristics vectors...
Matching images taken from widely different viewpoints is still an open problem being extremely challenging. In this paper is presented a new method that manipulates effectively the color in order to improve the matching performances of the well-known SIFT operator for the wide-baseline case. Without exploiting additional information, the algorithm employs a new model that preserves efficiently the...
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