Algorithm For Feature Points Research Articles

An image-based bridge displacement field measurement method with self-vibration elimination

Displacement is a critical indicator that reflects the degeneration of overall stiffness for bridges. However, for most bridges, contact displacement measurement is difficult to achieve due to the lack of reference points and technical limitations. In this paper, a unique dual-camera visual sensor system for the whole field displacement measurement of bridge undersurfaces was developed. The visual sensor system relies on feature point algorithm and several algorithms are involved to improve the accuracy and efficiency. The dual-camera equipment can independently eliminate the impact of the camera vibration, thereby solving the problem of camera vibration caused by ground vibrations and wind loads at outdoor inspection sites when measuring bridge displacement. In a laboratory testing environment, a steel plate simulated a bridge and different experiments were conducted to test the visual system in situations with and without vibration disturbances to the camera. The results verify the accuracy of the developed visual sensor, especially the effectiveness of the vibration reduction method.

An Improved Method for Stable Feature Points Selection in Structure-from-Motion Considering Image Semantic and Structural Characteristics.

Feature matching plays a crucial role in the process of 3D reconstruction based on the structure from motion (SfM) technique. For a large collection of oblique images, feature matching is one of the most time-consuming steps, and the matching result directly affects the accuracy of subsequent tasks. Therefore, how to extract the reasonable feature points robustly and efficiently to improve the matching speed and quality has received extensive attention from scholars worldwide. Most studies perform quantitative feature point selection based on image Difference-of-Gaussian (DoG) pyramids in practice. However, the stability and spatial distribution of feature points are not considered enough, resulting in selected feature points that may not adequately reflect the scene structures and cannot guarantee the matching rate and the aerial triangulation accuracy. To address these issues, an improved method for stable feature point selection in SfM considering image semantic and structural characteristics is proposed. First, the visible-band difference vegetation index is used to identify the vegetation areas from oblique images, and the line feature in the image is extracted by the optimized line segment detector algorithm. Second, the feature point two-tuple classification model is established, in which the vegetation area recognition result is used as the semantic constraint, the line feature extraction result is used as the structural constraint, and the feature points are divided into three types. Finally, a progressive selection algorithm for feature points is proposed, in which feature points in the DoG pyramid are selected by classes and levels until the number of feature points is satisfied. Oblique images of a 40-km2 area in Dongying city, China, were used for validation. The experimental results show that compared to the state-of-the-art method, the method proposed in this paper not only effectively reduces the number of feature points but also better reflects the scene structure. At the same time, the average reprojection error of the aerial triangulation decrease by 20%, the feature point matching rate increase by 3%, the selected feature points are more stable and reasonable.

High-precision Registration Algorithm and Parallel Design Method for High-Resolution Optical Remote Sensing Images

In optical remote sensing image reconstruction, image registration is an important issue to address in order to ensure satisfactory reconstruction performance. In this study, a multi-frame image registration algorithm for high-resolution images and its parallel design method are proposed. The algorithm realizes an improved feature point detection method based on an adaptive gradient bilateral tensor filter and carries out weighted Gaussian surface sub-pixel interpolation to obtain more accurate corner positions, which better guarantees the registration accuracy. On this basis, multi-scale expansion is carried out to generate descriptors for image registration. In addition, the operation-level parallel analysis and design are carried out on a GPU platform based on compute unified device architecture (CUDA), and the memory model of the GPU is utilized reasonably. The task-level parallel analysis and design are carried out based on the GPU stream model. Moreover, based on the open multi-processing (OpenMP) platform, a multi-core CPU carries out parallel design at the operation level and task level, which realizes post-processing operations such as optical remote sensing images loading, accurate matching, and coordinate mapping, thereby effectively improving registration speed. Compared with feature point algorithms and deep learning algorithm, our algorithm and its parallel design significantly improve the registration accuracy and speed of high-resolution optical remote sensing images.

Comparison of feature point detectors for multimodal image registration in plant phenotyping.

With the introduction of multi-camera systems in modern plant phenotyping new opportunities for combined multimodal image analysis emerge. Visible light (VIS), fluorescence (FLU) and near-infrared images enable scientists to study different plant traits based on optical appearance, biochemical composition and nutrition status. A straightforward analysis of high-throughput image data is hampered by a number of natural and technical factors including large variability of plant appearance, inhomogeneous illumination, shadows and reflections in the background regions. Consequently, automated segmentation of plant images represents a big challenge and often requires an extensive human-machine interaction. Combined analysis of different image modalities may enable automatisation of plant segmentation in “difficult” image modalities such as VIS images by utilising the results of segmentation of image modalities that exhibit higher contrast between plant and background, i.e. FLU images. For efficient segmentation and detection of diverse plant structures (i.e. leaf tips, flowers), image registration techniques based on feature point (FP) matching are of particular interest. However, finding reliable feature points and point pairs for differently structured plant species in multimodal images can be challenging. To address this task in a general manner, different feature point detectors should be considered. Here, a comparison of seven different feature point detectors for automated registration of VIS and FLU plant images is performed. Our experimental results show that straightforward image registration using FP detectors is prone to errors due to too large structural difference between FLU and VIS modalities. We show that structural image enhancement such as background filtering and edge image transformation significantly improves performance of FP algorithms. To overcome the limitations of single FP detectors, combination of different FP methods is suggested. We demonstrate application of our enhanced FP approach for automated registration of a large amount of FLU/VIS images of developing plant species acquired from high-throughput phenotyping experiments.

Image stitching algorithm based on fast feature extraction and perceptual hash

The classic feature point image stitching algorithm is time-consuming in feature extraction, and an image sparse matrix method is proposed to determine the feature points. This method first uses Laplace operator to extract the image gradient and set the threshold to obtain the sparse matrix of image segmentation, then using features from accelerated segment test (FAST) detection algorithm for feature point. Finally, the speeded-up robust features (SURF) descriptor is given to increase the stability of the feature points. This method solves the problem of time-consuming feature extraction, making the real-time image mosaic possible. In the process of image mosaic, this paper presents a method using Kalman filter to predict the overlap region, thus reducing the computational complexity. In addition, because there is always a mismatch between feature points matching, this paper proposes a method using perceptual hashing to refine the matching pair, which solves the error caused by mismatching for the next image registration and improves the registration accuracy. Experimental results show that the proposed algorithm improves the speed and precision of image mosaic.

Image stitching algorithm based on fast feature extraction and perceptual hash

The classic feature point image stitching algorithm is time-consuming in feature extraction, and an image sparse matrix method is proposed to determine the feature points. This method first uses Laplace operator to extract the image gradient and set the threshold to obtain the sparse matrix of image segmentation, then using features from accelerated segment test (FAST) detection algorithm for feature point. Finally, the speeded-up robust features (SURF) descriptor is given to increase the stability of the feature points. This method solves the problem of time-consuming feature extraction, making the real-time image mosaic possible. In the process of image mosaic, this paper presents a method using Kalman filter to predict the overlap region, thus reducing the computational complexity. In addition, because there is always a mismatch between feature points matching, this paper proposes a method using perceptual hashing to refine the matching pair, which solves the error caused by mismatching for the next image registration and improves the registration accuracy. Experimental results show that the proposed algorithm improves the speed and precision of image mosaic.

Multi-camera based Images through Feature Points Algorithm for HDR Panorama

With the spread of various kinds of cameras such as digital cameras and DSLR and a growing interest in high-definition and high-resolution images, a method that synthesizes multiple images is being studied among various methods. High Dynamic Range (HDR) images store light exposure with even wider range of number than normal digital images. Therefore, it can store the intensity of light inherent in specific scenes expressed by light sources in real life quite accurately. This study suggests feature points synthesis algorithm to improve the performance of HDR panorama recognition method (algorithm) at recognition and coordination level through classifying the feature points for image recognition using more than one multi frames.

应用尺度不变特征变换的多源遥感影像特征点匹配

Many traditional feature point algorithms can not handle more complex nonlinear brightness changes because the gray between multi-source remote sensing images is nonlinear changes.To cover the shortage,a Scale Invariant Feature Transform(SIFT)feature matching algorithm of multi-source remote sensing images was proposed.First,the approximate linear gray value between multi-source remote sensing images was achieved through linear fitting of the bands of the images.Then,an improved SIFT algorithm was adopted to match the fitted remote sensing images.Finally,the random sample Consensus algorithm was used to remove the false matching point pairs.In comparison with other feature matching algorithms(SIFT,Gradient Location Orientation Hologram(GLOH),RSSIFT).The experimental results show that the feature matching rate increases by about 4% between ETM+ panchromatic and multispectral images and the number of correct matches of key points increases by about 8point pairs between CBERS-02Band HJ-1Bimages.It concludes that the proposed method significantly outperforms many state-of-the-art methods under multi-source remote sensing images.

Visual Saliency from Image Features with Application to Compression

Image feature point algorithms and their associated regional descriptors can be viewed as primitive detectors of visually salient information. In this paper, a new method for constructing a visual attention probability map using features is proposed. (Throughout this work, we use SURF features yet the algorithm is not limited to SURF alone.). This technique is validated using comprehensive human eye-tracking experiments. We call this algorithm “visual interest” (VI) since the resultant segmentation reveals image regions that are visually salient during the performance of multiple observer search tasks. We demonstrate that it works on generic, eye-level photographs and is not dependent on heuristic tuning. We further show that the descriptor-matching property of the SURF feature points can be exploited via object recognition to modulate the context of the attention probability map for a given object search task, refining the salient area. We fully validate the VI algorithm through applying it to salient compression using a pre-blur of non-salient regions prior to JPEG and conducting comprehensive observer performance tests. When using the object contextualisation, we conclude that JPEG files are around 33 % larger than they need to be to fully represent the task-relevant information within them. We finally demonstrate the utility of the segmentation as a region of interest in JPEG2000 compression to achieve superior image quality (measured statistically using PSNR and SSIM) over the automatically selected salient image regions while reducing the image filesize by down to 25 % of that of the original. Our technique therefore delivers superior compression performance through the detection and selective preservation of visually salient information relevant to multiple observer tasks. In contrast to the state of the art in task-directed visual attention models, the VI algorithm reacts only to the image content and requires no detailed prior knowledge of the scene nor of the ultimate observer task.

Feature point matching in image sequences

One of the vital problems in motion analysis is to match a set of feature points over an image sequence. In this paper, we solve this problem by relying on continuity of motion which is known to play an important role in the perception of apparent motion in the human visual system. We propose a relaxation algorithm for feature point matching where the formation of smooth trajectories over space and time is favored. Experimental results on laboratory generated and real scene sequences are presented to demonstrate the merit of out approach.