Image-feature matching based on Local Invariant Feature Extraction (LIFE) methods has proven to be successful, and SIFT is one of the most effective. SIFT matching uses only local texture information to compute the correspondences. A number of approaches have been presented aimed at enhancing the image-features matches computed using only local information such as SIFT. What most of these appro...

This paper presents a novel framework for matching video sequences using the spatiotemporal segmentation of videos. Instead of using appearance features for region correspondence across frames, we use interest point trajectories to generate video volumes. Point trajectories, which are generated using the SIFT operator, are clustered to form motion segments by analyzing their motion and spatial ...

We propose a new method for object tracking in image sequences using template matching. To update the template, appearance features are smoothed temporally by robust and adaptive Kalman filters, one to each pixel, making the method robust against severe occlusions. The method is also robust to abrupt changes of lighting conditions, especially when photometric invariant color features are used t...

We propose a block motion estimation (ME) algorithm that meets high quality requirements and allows for cost efficient VLSI realizations. It relies on a set of rules common to all recent fast BMA algorithms and has been designed in order to allow for easy and prolific data reuse.

|In this paper, we investigate hardware implementation of block matching algorithms (BMAs) for motion estimation of moving sequences. Using systolic arrays, we propose VLSI architectures for the two-stage BMA and full search (FS) BMA. The two-stage BMA using integral projections reduces greatly computational complexity with its performance comparable to that of the FS BMA. The proposed hardware...

This paper presents a complete method for vehicle detection and tracking in a fixed setting based on computer vision. Vehicle detection is performed based on Scale Invariant Feature Transform (SIFT) feature matching. With SIFT feature detection and matching, the geometrical relations between the two images is estimated. Then, the previous image is aligned with the current image so that moving v...

This paper presents a new VLSI architecture for full-search block matching algorithm. The proposed architecture has two specific features: (1) it lhas a processor element (PE) array which provides sufficient computational power, where PE's work in a semi-systolic style and (2) it contains stream memory banks which provide scheduled data flow to reduce idle operations within PE array. By exploit...

In this paper, a new convex matching pursuit scheme is proposed for tackling large-scale sparse coding and subset selection problems. In contrast with current matching pursuit algorithms such as subspace pursuit (SP), the proposed algorithm has a convex formulation and guarantees that the objective value can be monotonically decreased. Moreover, theoretical analysis and experimental results sho...

This paper describes an improvement to the Cellular Associative Neural Network, an architecture based on the distributed model of a cellular automaton, allowing it to perform scale invariant pattern matching. The use of tensor products and superposition of patterns allows the system to recall patterns at multiple resolutions simultaneously. Our experimental results show that the architecture is...

In recent years, data collected from remote sensing satellite and aerophotography have been showing a geometric sequence increase. A method of Scale Invariant Feature Transform (SIFT) algorithm could be employed for the automatic geometric fine correction. This method could avoid the impact of the rotation and zooming of template matching during the image matching process, and it can also save ...