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In recent years, interest point based feature such as SIFT and SURF are widely used for image matchings. While these features are robust to changes in scales, rotations, and local geometric deformations, they are usually less able to simultaneously handle viewpoint changes and large illumination changes. In this paper, we will cope with this challenging problem. The basic idea is to relight one of...
Point cloud is one of the primitive representations of 3D data nowadays. Despite that much work has been done in 2D image matching, matching 3D points achieved from different perspective or at different time remains to be a challenging problem. This paper proposes a 3D local descriptor based on 3D self-similarities. We not only extend the concept of 2D self-similarity [1] to the 3D space, but also...
This paper presents a new image description and matching process based on internal self-similarity property of images. Various definitions of self-similarity are explored to find the best one for image matching. The method also ensures rotation and scale invariance and computational efficiency through a feature detection process. Experiments demonstrate that the proposed method increases robustness...
We present a retrieval-based tracking system that requires less computational time and cost. The system tracks a user's location through a small portion of an image captured by the camera, and then refines the camera pose by propagating matchings to the whole image. Augmented information such as building names and locations will be delivered to the user. The progressive way to process image data not...
Finding corresponding image points is a challenging computer vision problem, especially for confusing scenes with surfaces of low textures or repeated patterns. Despite the well-known challenges of extracting conceptually meaningful high-level matching primitives, many recent works describe high-level image features such as edge groups, lines and regions, which are more distinctive than traditional...
Many well-known existing image matching methods are based on local texture analysis, and consequently have difficulty handling low-textured 3D objects, such as those man-made buildings and road networks in urban scenes. In this paper, we propose our urban images matching method utilizing multiple novel clues. Specifically, we explore robust image features generated by interest regions and edge groups...
We present a wide-baseline image matching approach based on line segments. Line segments are clustered into local groups according to spatial proximity. Each group is treated as a feature called a Line Signature. Similar to local features, line signatures are robust to occlusion, image clutter, and viewpoint changes. The descriptor and similarity measure of line signatures are presented. Under our...
Augmented Virtual Environments (AVE) are very effective in the application of surveillance, in which multiple video streams are projected onto a 3D urban model for better visualization and comprehension of the dynamic scenes. One of the key issues in creating such systems is to estimate the parameters of each camera including the intrinsic parameters and its pose relative to the 3D model. Nowadays,...
A new feature descriptor is presented for object and scene recognition. The new approach, called CDIKP, uniquely combines the scale-invariant feature detection with a robust projection kernel technique to produce highly efficient feature representation. The produced feature descriptors are highly-compact in comparisons to the state-of-the-art, do not require any pretraining step, and show superior...
This paper proposes a general feature selection approach for real-time image matching systems. To demonstrate the idea??s effectiveness, we focus on the issue of rotational invariance. Most current image matching methods compute and align local image patches to a uniform dominant orientation, which are either too computationally expensive for real-time systems or insufficiently robust. In contrast...
This paper presents a high-performance image matching and recognition system for rapid and robust detection, matching and recognition of scene imagery and objects in varied backgrounds. Advanced image processing and pattern recognition technologies provide the system with object distinctiveness, robustness to occlusions, and invariance to scale and geometric distortions. Extensive experiments and...
This paper proposes a novel matching method for realtime finding the correspondences among different images containing the same object. The method utilizes an efficient Kernel Projection scheme to descript the image patch around a detected feature point. In order to achieve invariance and tolerance to geometric distortions, it combines a training stage based on generated synthetic views of the object...
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