An Image-based Docking Method for Mobile Robot

Efficient detection and reliable matching of image features constitute a fundamental task in computer vision. When real-time operation is required, the solution to this problem becomes a real challenge, because of increased processing requirements. Scale Invariant Feature Transform (SIFT) is considered as a stable and robust algorithm for the extraction of invariant features, however special hardware is required for high frame-rate applications, in order to overcome its computational complexity in real-time. In this paper, a complete processor for SIFT feature matching in video sequences is presented. It performs SIFT feature extraction and matching, as well as rejection of false correspondences using the random sample consensus (RANSAC) algorithm. The processor was evaluated using metrics like response, repeatability and recall. It was found that the proposed fixed-point hardware implementation has a comparable performance with software implementations of SIFT. The processor architecture can process more than 36 fps with resolution 640×480, when it is implemented in Cyclone IV FPGA technology.

Image stitching is one of the branches of computer vision. It combines two or more images for a scene to acquire a high-resolution panoramic image. An invariant local function often uses to stitch two images together. Since the flat plate of a digital radiography (DR) system does not cover all parts of the body, the whole bone structure image cannot seize in a single scan. To solved this problem, image stitching is broadly utilized by medical systems to stitch DR images, which can be helpful for scoliosis or lower extremity deformities in the diagnosis, and pre-operative planning are of great importance. In this paper, the stitching and retrieval of medical images planned. To conquer the background noise in medical images, and improve the recovery of quality and stitching rapidity of medical images, a random sample consensus (RANSAC) algorithm is useful to stitching the images of Chest digital radiography by scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) feature extraction. Down-sampling utilizes to lessen the size of the images and reduction the measure of calculation. In the interim, the phase correlation is engaged to discover the overlapping region. After feature matching and perspective transformation, the stitched image is gotten dependent on the homography. At last, experimentation has finished showing the presentation.

Most visual navigation relies on point matching and many of the matching point pairs are faulty. Generally, they are eliminated by the Random Sample Consensus (RANSAC) algorithms. However, RANSAC algorithms are often time-consuming and their parameters are hard to adjust. For such a purpose, a new method is proposed to eliminate the error matching points of every two consecutive images based on motion information. The proposed method is applicable to the cases that the motion information is available and the interest points are all from a horizontal plane. It does not require iteration and thus time-efficient. To demonstrate the effectiveness, the proposed method is tested on synthetic data of both simulation points and real images. The results show that the proposed method has better performance over RANSAC algorithms.

An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive modeling tool widely used in the image processing field for cleaning datasets from noise. RANSAC could be used as a “one stop shop” algorithm for developing and validating QSAR models, performing outlier removal, descriptors selection, model development and predictions for test set samples using applicability domain. For “future” predictions (i.e., for samples not included in the original test set) RANSAC provides a statistical estimate for the probability of obtaining reliable predictions, i.e., predictions within a pre-defined number of standard deviations from the true values. In this work we describe the first application of RNASAC in material informatics, focusing on the analysis of solar cells. We demonstrate that for three datasets representing different metal oxide (MO) based solar cell libraries RANSAC-derived models select descriptors previously shown to correlate with key photovoltaic properties and lead to good predictive statistics for these properties. These models were subsequently used to predict the properties of virtual solar cells libraries highlighting interesting dependencies of PV properties on MO compositions.

At present, most of the existing seal image registration methods are based on shape, and most of the methods can't tolerate the image scale change. To overcome these problems, a general seal registration and identification approach is proposed in this paper. This method uses the classic scale invariant feature transform (SIFT) algorithm to extract the key points. To register the seal accurately, the Random Sample Consensus (RANSAC) algorithm and the Least Squares Method (LSM) are used to obtain the transformational matrix. Then the invariant features are extracted based on residual image. At last, the Normal Bayesian classifier (NBC), K-Nearest Neighbor classifier (K-NN) and the Support Vector Machine (SVM) are combined to classify the feature vector. The experiments demonstrate the identification ability of the proposed approach.

On 6 September 2008, two optical satellites, HJ-1 A and B (HJ-1 A/B), were successfully launched from China. However, the system geometric correction products of the HJ-1 A/B charge-coupled device (HJ-1 images) have low geometric precision and need to be corrected. The HJ-1 images have a large aspect angle, a wide swath width, and a large image size. Furthermore, the local geometric distortions are too complex in one scene. Given these characteristics of HJ-1 images, geometric correction is still a challenging work. This article proposes an automatic geometric precision correction system (GPCS) based on the automatic registration between HJ-1 images and Landsat Thematic Mapper images. First, the coarse image matching method based on geometric-restricted scale-invariant feature transform (SIFT) is used to determine the coarse global transformation between the HJ-1 image and the reference image. Second, inspired by the hierarchical method of non-rigid registration for medical images, a hierarchical image matching approach is proposed based on the combination of SIFT feature points and template matching. This approach decomposes a matching problem of a whole image into numerous matching problems of image blocks and can overcome the impact of local distortions in HJ-1 images. Hierarchical random sample consensus (RANSAC) based on digital elevation model (H-RANSAC) is used to remove incorrect control points. Third, an HJ-1 image is rectified using a triangulated irregular network. Finally, the automatic evaluation method based on automatic image matching between the corrected HJ-1 image and the reference image is adopted to evaluate the geometric precision. On the one hand, experiments on eight HJ-1 images demonstrate the efficiency and accuracy of the different steps of GPCS. On the other hand, experiments on 1000 HJ-1 images also demonstrated the robustness, accuracy, and suitability for batch processing.

Copy-move forgery is a general widespread type of digital image forgery, where a segment of an image is attached into a new portion of the similar image to hide or replicate the parts which are called forgered image. The forgered image appears original, as the objective region in spite of being forged, has attained the fundamental qualities of the similar image itself. The capability of the copy-move forgery detection (CMFD) technique is lacked due to some post-processing functions, like JPEG compression scaling, or rotation, etc. Therefore, this paper intends to develop a CMFD using Scale-invariant feature transform (SIFT), best-fin-first algorithm (BBF) and RANdom SAmple Consensus (RANSAC) directed by grey wolf optimization (GWO) algorithm. Initially, the keypoints are selected using SIFT principle, and BBF algorithm identifies the matched keypoints using keypoint threshold. Further, SIFT feature descriptor is determined, and the final extracted paired keypoints are given to RANSAC algorithm to remove all the mismatched keypoints. In this CMFD model, the parameters such as parameters keypoint threshold, maximum distance of inliers in RANSAC and distance threshold in SIFT features are optimized using GWO. The foremost purpose of this research work is maximizing the number of paired keypoints. Hence the proposed model is termed as GWO-based parameter optimization for CMFD (GWPO-CMD). The proposed model is compared over several other meta-heuristic-based keypoint threshold selections and proves its efficiency through diverse analysis.

This research focuses on developing an Android-based cattle identification system that is applicable and easy to use. This system uses a Scale Invariant Feature Transform (SIFT) algorithm to extract features from the muzzle images, and Random Sample Consensus (RANSAC) algorithm to eliminate features incompatibility. The system is experimented with four threshold values, i.e. 10, 15, 20, and 30 using a total data of 460 muzzle images. In the first experiment, 3 images from each individual are used in the training stage and 2 images are used as the data test. In the second experiment, 5 images from each cattle are used in the training stage and 5 images are used as the data test. Data used in training stage are 244 images and in testing stage is 816 images. From the experiment, the highest accuracy rate is 98.1% with threshold values of 15 and 20. The execution time is also calculated to measure the processing time of the system. The average time taken to store an image to the database is 1.3 seconds. The main contribution of this research is technology implementation and more portable muzzle identification for local cattle in Makassar.

Orthognathic surgery is applied to restore esthetical facial profile and functional occlusion for patients with dentofacial deformity. Virtual surgical planning (VSP) is indispensable for precise and individualized treatment. Manually designing osteotomy planes is time-consuming and highly experience-dependent. This study aimed to develop and validate an automatic osteotomy plane design method based on deep learning. Methods: A deep learning model, Ortho-OPD (orthognathic osteotomy planes de signer), was proposed, consisting of a segmentation network and the random sample consensus (RANSAC) algorithm. The segmentation network was based on a convolutional neural network (CNN), orthognathic segmenting the craniomaxillofacial (CMF) CT data. Osteotomy planes were then defined by the RANSAC algorithm. Ortho-OPD was trained on 71 samples and tested on 31 cases. The performance was evaluated quantitatively and qualitatively. Results: Ortho-OPD functioned smoothly, and all cases were successfully performed. The 3D boundary-sensitive loss was employed to optimize precision. Evaluation metrics included accuracy and clinical efficiency. The mean dice similarity coefficient (DSC) was 0.920.032 in CMF seg mentation. Ortho-OPD showcased excellent productivity, taking an average of about 9 seconds to complete virtual bimaxillary osteotomy compared to manual work. The angular errors between the predicted planes and ground truth planes, plus the shortest distance from the neural tube or the adjacent apical points to predicted planes, were examined, indicating no significant difference and reliability for preserving vital anatomical structures. Overall, the automatic osteotomy plane design from raw CT data was realized using Ortho-OPD, composed of CNN and RANSAC, providing an efficient and ideal alternative in orthognathic osteotomy planning.

Abstract. Existing 3D indoor mapping of RGB-D data are prominently point-based and feature-based methods. In most cases iterative closest point (ICP) and its variants are generally used for pairwise registration process. Considering that the ICP algorithm requires an relatively accurate initial transformation and high overlap a weighted closed-form solution for RGB-D data registration is proposed. In this solution, we weighted and normalized the 3D points based on the theoretical random errors and the dual-number quaternions are used to represent the 3D rigid body motion. Basically, dual-number quaternions provide a closed-form solution by minimizing a cost function. The most important advantage of the closed-form solution is that it provides the optimal transformation in one-step, it does not need to calculate good initial estimates and expressively decreases the demand for computer resources in contrast to the iterative method. Basically, first our method exploits RGB information. We employed a scale invariant feature transformation (SIFT) for extracting, detecting, and matching features. It is able to detect and describe local features that are invariant to scaling and rotation. To detect and filter outliers, we used random sample consensus (RANSAC) algorithm, jointly with an statistical dispersion called interquartile range (IQR). After, a new RGB-D loop-closure solution is implemented based on the volumetric information between pair of point clouds and the dispersion of the random errors. The loop-closure consists to recognize when the sensor revisits some region. Finally, a globally consistent map is created to minimize the registration errors via a graph-based optimization. The effectiveness of the proposed method is demonstrated with a Kinect dataset. The experimental results show that the proposed method can properly map the indoor environment with an absolute accuracy around 1.5% of the travel of a trajectory.

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

Acoustic source localization has been an hot research topic with widespread applications in many fields. In the noisy environment or when the reverberation is considerable, the source localization problem becomes challenging and many existing algorithms deteriorate. The paper proposes a robust algorithm which combines the RANdom SAmple Consensus (RANSAC) algorithm, and the Generalized Cross-Correlation (GCC) based Time Difference of Arrival (TDOA). Experiments in real world data show that the proposed algorithm has significantly better performance than the traditional algorithm.

Pilot study of feature-based algorithm for breech face comparison

It is important to get reliable measurement data in Electrical impedance tomography (EIT) for getting satisfactory reconstruction result. Because EIT inverse problem is non-linear and ill-posed, incorrect data such as measured data through fault electrode can effect the estimation of resistivity distribution. So, EIT measurement system with methods for checking reliability has been developed. However, most existing methods require extra cost to evaluate reliability of EIT system. This paper presents modified Gauss-Newton (GN) method based on random sample consensus (RANSAC) algorithm for finding fault electrodes and getting good reconstructed image with faulty data for two-phase flow application. Also, suitable residual equation is proposed for determining the threshold to use RANSAC algorithm. Numerical simulations are performed to validate the performance of the proposed method. The results show that the proposed method has a good reconstruction performance compared to the conventional GN method.

Road detection is a crucial problem for intelligent vehicles and mobile robots. Most of the methods proposed nowadays only achieve reliable results in relatively well-arranged environments. In this paper, we proposed a stereovision-based road boundary detection method by combining homography estimation and MRF-based belief propagation to cope with challenging scenarios such as unstructured roads with unhomogeneous surfaces. In the method, each pixel in the reference image is firstly labeled as ¿road¿ or ¿non-road¿ by minimizing a well defined energy function that accounts for the planar road region. Subsequently, both of the road boundaries are generated using Catmull-Rom splines based on RANdom SAmple Consensus (RANSAC) algorithm with varying road structure models to help the intelligent vehicle understand the structure as well as safe range of current road. In the suggested framework, both intensity and geometry information of road scenarios are used to contain all the regions belonging to the planar road plane, and the left and right road boundaries are generated separately using a robust fitting algorithm to handle different road structures. Therefore, more accurate as well as robust detection of the road can be expected. Experimental results on a wide variety of typical but challenging scenarios have demonstrated the effectiveness of the proposed method.