Interest Point Detection Algorithms Research Articles

Image mosaicking using improved auto-sorting algorithm and local difference-based harris features

Image mosaicking is an image processing technique which is useful for tiling images. Image Mosaicing stitches many correlated images to get a picture of a greater field of view. General-purpose cameras, which have a low field of view, can not create images with a higher field of view while mosaicking can help us achieve it. One important step in an image mosaicking framework is the auto-sorting algorithm, which is to be performed to minimize registration errors in the mosaic image. Another step in mosaicking is the detection of interest points for matching of the source images obtained after auto-sorting. However, in the presence of noisy and pseudo-periodic structures in the source images, the existing auto-sorting methods generally produce distortions in the final mosaic image. Secondly, most of the popular interest point detection algorithms do not specifically consider computational issues. So, this work mainly addresses the above-mentioned problems which are generally encountered during image mosaicking. The problem of image auto-sorting can be partially solved by adopting a phase correlation strategy. In our method, the sorting procedure is further improved by deploying the structural similarity index (SSIM) measure instead of using the phase correlation. The issue of high time complexity of conventional corner detectors is reduced by using our proposed local difference operation in place of standard Sobel edge detector. Experimental results show the efficacy of the proposed method.

Interest point detection method based on multi‐scale Gabor filters

In this study, a novel interest point detection algorithm which combines image intensity variation and edge contour information is proposed. Firstly, the Canny edge contour detector is used to extract the edge map. Secondly, the imaginary parts of multi-scale Gabor filters are applied to smooth input image and then the normalised information entropies at various scales can be acquired. Finally, multiplication of different normalised information entropies will be served as a new measure for interest point that can also be used for interest point detection. This method has two advantages: on the one hand, detection accuracy is greatly improved because combination information is adopted to extract interest points including contour shape information, grey variation of edge pixels and their neighbours; on the other hand, non-interest points can be well inhabited due to multi-scale product in detector is served as an interest point measure. Also, desirable noise robustness and time efficiency are validated through experiments. Compared with four other state-of-art methods, the proposed method shows excellent performance in terms of geometric transformations and localisation accuracy of repeated interest points.

Local detectors and compact descriptors for visual search: A quantitative comparison

Local Visual detectors and descriptors have been studied for many years, but their applications (e.g. mobile visual search) in large volume, low-cost, low-power embedded systems have been limited or negligible to date. One reason is the lack of a worldwide industry standard. MPEG Compact Descriptors for Visual Search (CDVS) working group filled this gap by defining a high-performance extraction stage and the bitstream syntax at its output in order to achieve interoperability between different implementations of clients and servers. In a previous work, we presented an analysis of various gray-level interest point detection and description algorithms, which was also contributed to CDVS. This work extends the previous analysis using the MPEG CDVS Test Model framework to consider additional detectors and the use of color descriptors.

A Novel Approach for Interest Point Detection via Laplacian-of-Bilateral Filter

Scale-invariant feature transform (SIFT) algorithm, one of the most famous and popular interest point detectors, detects extrema by using difference-of-Gaussian (DoG) filter which is an approximation to the Laplacian-of-Gaussian (LoG) for improving speed. However, DoG filter has a strong response along edge, even if the location along the edge is poorly determined and therefore is unstable to small amounts of noise. In this paper, we propose a novel interest point detection algorithm, which detects scale space extrema by using a Laplacian-of-Bilateral (LoB) filter. The LoB filter, which is produced by Bilateral and Laplacian filter, can preserve edge characteristic by fully utilizing the information of intensity variety. Compared with the SIFT algorithm, our algorithm substantially improves the repeatability of detected interest points on a very challenging benchmark dataset, in which images were generated under different imaging conditions. Extensive experimental results show that the proposed approach is more robust to challenging problems such as illumination and viewpoint changes, especially when encountering large illumination change.

A vision processor with a unified interest point detection and matching hardware for accelerating stereo matching algorithm

In this paper, a unified interest-point detection and matching hardware with an optimized memory architecture is proposed for a real-time stereo-matching system. In order to support a stereo-matching algorithm, the unified datapath in the hardware performs not only interest-point detection and matching algorithms such as features from the accelerated segment test (FAST) and binary robust independent elementary feature (BRIEF) in real time but also a census transform, which is widely used in stereo matching. To achieve maximum performance, we propose two special memory architectures: 1) reconfigurable image memory (RIM) and 2) point cloud index memory system (PCIM). RIM is a unified memory architecture that loads pixel values from a raw image patch. Since FAST, BRIEF, and the census transform have different and complex memory access patterns, the miss rate of the memory access may increase. To optimize the memory operation, RIM can change its memory configuration according to the algorithm. PCIM is a dedicated memory system that utilizes the geometric information of the cameras in order to reduce the off-chip memory bandwidth. Based on the geometric information, PCIM removes most of the redundant candidates. Since PCIM minimizes the off-chip memory bandwidth using a dedicated cache, the performance degradation is negligible compared with the exact-nearest-neighbor method. Area-based stereo matching is accelerated based on the general-purpose computing on graphics processing units (GPGPU) architecture because the search range is adaptively reduced according to the disparity of the matched correspondences. The overall hardware consists of 1.20-M logic gates and consumes a maximum of 185 mW. Interest point detection and matching accelerator achieves 106 frames/s in 1080p full high-definition video (HD) resolution at a 200-MHz operating frequency with 3500 descriptors per image.

AN EMPIRICAL EVALUATION OF INTEREST POINT DETECTORS

Image interest point extraction and matching across images is a commonplace task in computer vision–based applications, across widely diverse domains, such as 3D reconstruction, augmented reality, or tracking. We present an empirical evaluation of state-of-the-art interest point detection algorithms measuring several parameters, such as efficiency, robustness to image domain geometric transformations—that is, similarity—affine or projective transformations, as well as invariance to photometric transformations such as light intensity or image noise.

Interest point detection for reconstruction in high granularity tracking detectors

This paper presents an investigation of the use of interest point detection algorithms from image processing applied to reconstruction of interactions in high granularity tracking detectors. Their purpose is to extract keypoints fromthe data as input to higher level reconstruction algorithms, replacing the roleof human operators in event selection and reconstruction guidance.Simulations of νμ+40Ar→μ−+p events with aνμ energy of 0.7GeVin a small liquidargon time projection chamber are used as a concrete example of a modern highgranularity tracking detector. Data from the simulations are used to characterize the localization of interest points to physical features and the efficiency of finding interest points associated with the primary vertex and track ends is measured at thechosen beam energy. A high degree of localization is found, with 93% of detected interest points found within 5mm of a physical feature at the simulated energy of 0.7GeV. Working in two 2D projections, the primary vertex and both track ends are found in both projections in 85% of events at 0.7GeV. It is also shown that delta electrons can be detected.

Illumination robust interest point detection

Most interest point detection algorithms are highly sensitive to illumination variations. This paper presents a method to find interest points robustly even under large non-uniform photometric changes. The method, which we call illumination robust feature extraction transform (IRFET), determines salient interest points in an image by calculating and analyzing contrast signatures. A contrast signature shows the response of an interest point detector with respect to a set of contrast stretching functions. The IRFET is generic and can be used with most interest point detectors. In this paper, we demonstrate that the IRFET improves the repeatability rate of the Harris corner detector significantly (by around 25% on average in the experiments).