Speeded Up Robust Features Research Articles

Multi-sensor image registration by combining local self-similarity matching and mutual information

Automatic multi-sensor image registration is a challenging task in remote sensing. Conventional image registration algorithms may not be applicable when common underlying visual features are not distinct. In this paper, we propose a novel image registration approach that integrates local self-similarity (LSS) and mutual information (MI) for multi-sensor images with rigid/nonrigid radiometric and geometric distortions. LSS is a well-performing descriptor that captures common, local internal layout features for multi-sensor images, whereas MI focuses on global intensity relationships. First, potential control points are identified by using the Harris algorithm and screened based on the self-similarity of their local surrounding internal layouts. Second, a Bayesian probabilistic model for matching the ensemble of the LSS features is introduced. Third, a particle swarm optimization (PSO) algorithm is adopted to optimize the point and region correspondences for maximum self-similarity and MI and, ultimately, a robust mapping function. The proposed approach is compared with several conventional image registration algorithms that are based on the sum of squared differences (SSD), scale-invariant feature transforms (SIFT), and speeded-up robust features (SURF) through the experimental registration of pairs of Landsat TM, SPOT, and RADARSAT SAR images. The results demonstrate that the proposed approach is efficient and accurate.

Visual Feature Based Image Forgery Detection

In present days, the images are building up in digital form and which may hold essential information. Such images can be voluntarily forged or manipulated using the image processing tools to abuse it. It is very complicated to notice the forgery by naked eyes. In particular, the copy move forgery is enormously demanding one to expose. Hence, this paper put forwards a method to determine the copy move forgery by extracting the visual feature called speed up robust features (SURF). In the direction to quantitatively analyze the performance, the metrics namely false positive rate and true positive rate are estimated and also comparative study is carried out by previous existing methods.

Remote sensing image mosaic technology based on SURF algorithm in agriculture

The remote sensing technology of unmanned aerial vehicle (UAV) is a low altitude remote sensing technology. The technology has been widely used in military, agricultural, medical, geographical mapping, and other fields by virtue of the advantages of fast acquisition, high resolution, low cost, and good security. But limited by the flying height of UAV and the focal length of the digital camera, the single image obtained by the UAV is difficult to form the overall cognition of the ground farmland area. In order to further expand the field of view, it is necessary to mosaic multiple single images acquired by UAV into a complete panoramic image of the farmland. In this paper, aiming at the problem of UAV low-altitude remote sensing image splicing, an image mosaic technique based on Speed Up Robust Feature (SURF) is introduced to achieve rapid image splicing. One hundred fifty ground farmland remote sensing images collected by UAV are used as experimental splicing objects, and the image splicing is completed by the global stitching strategy optimized by Levenberg-Marquardt (L-M). Experiments show that the strategy can effectively reduce the influence of cumulative errors and achieve automatic panoramic mosaic of the survey area.

Augmented Reality of the Middle Ear Combining Otoendoscopy and Temporal Bone Computed Tomography.

Augmented reality (AR) may enhance otologic procedures by providing sub-millimetric accuracy and allowing the unification of information in a single screen. Several issues related to otologic procedures can be addressed through an AR system by providing sub-millimetric precision, supplying a global view of the middle ear cleft, and advantageously unifying the information in a single screen. The AR system is obtained by combining otoendoscopy with temporal bone computer tomography (CT). Four human temporal bone specimens were explored by high-resolution CT-scan and dynamic otoendoscopy with video recordings. The initialization of the system consisted of a semi-automatic registration between the otoendoscopic video and the 3D CT-scan reconstruction of the middle ear. Endoscope movements were estimated by several computer vision techniques (feature detectors/descriptors and optical flow) and used to warp the CT-scan to keep the correspondence with the otoendoscopic video. The system maintained synchronization between the CT-scan image and the otoendoscopic video in all experiments during slow and rapid (5-10 mm/s) endoscope movements. Among tested algorithms, two feature-based methods, scale-invariant feature transform (SIFT); and speeded up robust features (SURF), provided sub-millimeter mean tracking errors (0.38 ± 0.53 mm and 0.20 ± 0.16 mm, respectively) and an adequate image refresh rate (11 and 17 frames per second, respectively) after 2 minutes of procedure with continuous endoscope movements. A precise augmented reality combining video and 3D CT-scan data can be applied to otoendoscopy without the use of conventional neuronavigation tracking thanks to computer vision algorithms.

Towards 3D Pipe Reconstruction Employing Affine Transformations from Video Information

The development of a virtual scenario is desirable in a number of hostile environments such as in mining, or sewer pipelines to inspect its current condition and to identify possible failures. Obtaining a virtual image through the reconstruction of the mineshaft or inner pipe is a valuable tool for assessment and fault detection. In this paper, we present results for our 3D reconstruction approach, which proposes to employ regions with affine transformations that are used as dense correspondences. These features are calculated through local correspondences, such as the SIFT (Scale-invariant feature transform) and SURF (Speeded Up Robust Features). The correspondences are required to obtain the orientation and position of the camera and to get the structure of the scene leading to the creation of the 3D environment. The algorithm is presented as well as its results, which highlights the potential of the method.

Classification of Microscopic Image of Herbal Plants from Its Powder Using Speeded-Up Robust Features

Classification of Microscopic Image of Herbal Plants from Its Powder Using Speeded-Up Robust Features

Towards Zero Re-Training for Long-Term Hand Gesture Recognition via Ultrasound Sensing.

While myoelectric pattern recognition is a prevailing way for gesture recognition, the inherent nonstationarity of electromyography signals hinders its long-term application. This study aims to prove a hypothesis that morphological information of muscle contraction detected by ultrasound image is potentially suitable for long-term use. A set of ultrasound-based algorithms are proposed to realize robust hand gesture recognition over multiple days, with user training only at the first day. A markerless calibration algorithm is first presented to position the ultrasound probe during donning and doffing; an algorithm combining speeded-up robust features and bag-of-features model being immune to ultrasound probe shift and rotation is then introduced; a self-enhancing classification method is next adopted to update classification model automatically by incorporating useful knowledge from testing data; finally the performance of long-term hand gesture recognition with zero re-training is validated by a six-day experiment of six healthy subjects, whose outcomes strongly support the hypothesis with about 94% of gesture recognition accuracy for each testing day. This study confirms the feasibility of adoption of ultrasound sensing for long-term musculature related applications.

Recognition of the Gastric Molecular Image Based on Decision Tree and Discriminant Analysis Classifiers by using Discrete Fourier Transform and Features

ABSTRACTThis article presents the development and evaluation of a computerized decision support system (DSS), aiming to Show the feasibility and potential toward maximizing the benefits of a new algorithm by combining the machine-learning techniques which are not used in the literature for automatic recognition of the gastric images. The object of this article is fivefold: first, the features Maximally Stable Extremal Regions (MSER), Speeded Up Robust Features (SRF), and Binary Robust Invariant Scalable Keypoints (BRISK) of histopathological gastric images were analyzed. Second, the Fourier Transform (FT) was applied to these properties which were calculated to equalize the dimensions of the obtained features. Third, MS and LE size reduction methods have been applied. Fourth, the decision tree (DT) and discriminant analysis (DA) classifiers are used to classify the histopathological gastric images. Fifth, these classification results have been compared. In this article, the highest accuracy result obtained by using the SRF_FT_MS_DT method is found to be 86.66%. Fast and multimodality computerized DSS can beneficial to patients for early detection of gastric diseases. It may facilitate early diagnosis of the disease.

Research on Scalable Real-Time Image Mosaic Technology Based on Improved SURF

Due to the presence of light and angle distortion, the image mosaic results is not ideal, which makes it difficult to identify subsequent ground object. To tackle this problem, this paper proposes an image mosaic technology based on the improved SURF (Speed-Up Robust Features) algorithm. After extracting image feature points, using Hessian matrix trace algorithm for image matching, and then the RANSAC algorithm purified the image feature point pairs. Finally, the image was stitched using a weighted smoothing algorithm. Experiments verify the effectiveness of this algorithm for image stitching with deviated scale and angle of view. At the same time, the algorithm is applied to the field of scalable real-time images stitching, and has achieved good results.

A JOINT EFFORT OF SPEEDED-UP ROBUST FEATURES ALGORITHM AND A DISPARITY-BASED MODEL FOR 3D INDOOR MAPPING USING RGB-D DATA

Abstract In this paper, we present a method for 3D mapping of indoor environments using RGB-D data. The contribution of our proposed method is two-fold. First, our method exploits a joint effort of the speed-up robust features (SURF) algorithm and a disparity-to-plane model for a coarse-to-fine registration procedure. Once the coarse-to-fine registration task accumulates errors, the same features can appear in two different locations of the map. This is known as the loop closure problem. Then, the variance-covariance matrix that describes the uncertainty of transformation parameters (3D rotation and 3D translation) for view-based loop closure detection followed by a graph-based optimization are proposed to achieve a 3D consistent indoor map. To demonstrate and evaluate the effectiveness of the proposed method, experimental datasets obtained in three indoor environments with different levels of details are used. The experimental results shown that the proposed framework can create 3D indoor maps with an error of 11,97 cm into object space that corresponds to a positional imprecision around 1,5% at the distance of 9 m travelled by sensor.

Motion Detection and Face Recognition for CCTV Surveillance System

Closed Circuit Television (CCTV) is currently used in daily life for a variety purpose. Development of the use of CCTV has transformed from a simple passive surveillance into an integrated intelligent control system. In this research, motion detection and facial recognation in CCTV video is done to be a base for decision making to produce automated, effective and efficient integrated system. This CCTV video processing provides three outputs, a motion detection information, a face detection information and a face identification information. Accumulative Differences Images (ADI) used for motion detection, and Haar Classifiers Cascade used for facial segmentation. Feature extraction is done with Speeded-Up Robust Features (SURF) and Principal Component Analysis (PCA). The features was trained by Counter-Propagation Network (CPN). Offline tests performed on 45 CCTV video. The test results obtained a motion detection success rate of 92,655%, a face detection success rate of 76%, and a face detection success rate of 60%. The results concluded that the process of faces identification through CCTV video with natural background have not been able to obtain optimal results. The motion detection process is ideal to be applied to real-time conditions. But in combination with face recognition process, there is a significant delay time.

Diagnosis of breast tissue in mammography images based local feature descriptors

Breast cancer is one of the leading causes of death by cancer among women. The high mortality rates and the occurrence of this cancer worldwide show the importance of the investigation and development of means for the detection and early diagnosis of this disease. Computer-Aided Detection and Diagnosis systems have been developed to improve diagnostic accuracy by radiologists. This work proposes a method for discriminating patterns of malignancy and benignity of masses in digitized mammography images through the analysis of local features. The method comparatively applies the Scale-Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF), Oriented Fast and Rotated BRIEF (ORB) and Local Binary Pattern (LBP) descriptors for local feature extraction. These features are represented by a Bag of Features (BoF) model, applied to provide new representations of the data and to reduce its dimensionality. Finally, the features are used as input for the Support Vector Machine (SVM), Adaptive Boosting (Adaboost) and Random Forests (RF) classifiers to differentiate malignant and benign masses. The method obtained significant results, reaching 100% sensitivity, 99.65% accuracy and 99.24% specificity for benign and malignant mass classification.

A photogrammetry-based image registration method for multi-camera systems – With applications in images of a tree crop

In precision agriculture, estimating crop yield using remote sensing techniques is an active research field. To achieve high accuracies, researchers frequently combined different imaging sources, such as colour (Red, Green, Blue [RGB]) images, thermal images, and near-infrared images. However, fusing information from those images has been a difficult task. Therefore, accurate image registration methods are necessary. This study aimed to develop a thermal-colour camera system which will register thermal images with colour images of tree canopies in preparation of information fusion and fruit detection. The registration method created in this study was based on photogrammetry. In preparation of registration, a camera system was built, consisting of a thermal camera and two colour cameras. Camera calibration, image intersection, and space resection were combined in a single step named ‘stereo-calibration’, to compute cameras' parameters and poses. Speeded-up robust features (SURF) were used to find points of interest from colour images. Random sample consensus (RANSAC) was utilised to search for optimal homography transforms between thermal and colour images. In addition, this study created a procedure for accurate registrations of regions of interest in thermal-colour image pairs, utilising structural similarity (SSIM) index. The proposed method offered pixel-level registration accuracy and achieved an average accuracy of 3 pixels in 640 × 480 – pixel citrus canopy images.

Alzheimer'S Disease Classification Using Bag-Of-Words Based On Visual Pattern Of Diffusion Anisotropy For DTI Imaging.

Diffusion tensor imaging (DTI) has recently been added to the large scale of studies for Alzheimer's Disease (AD) to investigate the White Matter (WM) defects that are not detectable using structural MRI. In this paper, we extracted Speeded Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) features, based on the visual diffusion patterns of Fractional Anisotropy (FA), and Mean Diffusivity (MD) maps, to build bag-of-words AD-signature for the hippocampal area. The experiments were accomplished with a subset of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset formed of AD patients (n = 35), Early Mild Cognitive Impairment (EMCI) (n=6), Late Mild Cognitive Impairment (LMCI) (n=24) and cognitively healthy elderly Normal Controls (NC) (n=31). The preliminary studied experiments give promising results that would consider the proposed system as an accurate and useful tool to capture the AD leanness with accuracy of 87% and 89% for FA and MD maps respectively.

The Phi-OTDR Signals’ Storage with Image Style

The distributed vibration signal has a large amount of data. It can waste insufficient storage space and have low efficiency of data analysis. Therefore, the paper proposes to save data information in the image format. The images have the original signal feature. The Phi-OTDR is pre-processed by the smooth filter. After the filter, the signal is stored by the image format. The image is extracted by the improved Speeded-Up Robust Features. The signal feature is kept in the image. Then a series of experiments prove the methods effectiveness.

Kurdish Sign Language Recognition System

Deaf people all around the world face difficulty to communicate with the others. Hence, they use their own language to communicate with each other. On the other hand, it is difficult for deaf people to get used to technological services such as websites, television, mobile applications, and so on. This project aims to design a prototype system for deaf people to help them to communicate with other people and computers without relying on human interpreters. The proposed system is for letter-based Kurdish Sign Language (KuSL) which has not been introduced before. The system would be a real-time system that takes actions immediately after detecting hand gestures. Three algorithms for detecting KuSL have been implemented and tested, two of them are well-known methods that have been implemented and tested by other researchers, and the third one has been introduced in this paper for the 1st time. The new algorithm is named Gridbased gesture descriptor. It turned out to be the best method for the recognition of Kurdish hand signs. Furthermore, the result of the algorithm was 67% accuracy of detecting hand gestures. Finally, the other well-known algorithms are named scale invariant feature transform and speeded-up robust features, and they responded with 42% of accuracy.

ISAR image scattering center association based on speeded-up robust features

It is difficult for the imaging radar of the single antenna to correlate the scattering centers in 3D reconstruction. Therefore, an association method of scattering centers is proposed to get the transformational matrix of the adjacent image frames through rough matching of the Speeded-up Robust Features (SURF) and determine the association results of the scattering centers with a minimum of 2 norms after back projection. At last, it is verified through the simulation experiment and the measured data. The experimental results indicate that this method is simple with small computation, strong robustness and stable imaging effect. It is applicable to the association of target scattering centers in multiple perspectives. The experimental results prove the feasibility of the method.

Object recognition based on foreground detection using X-ray imaging

ABSTRACTDevelopment of effective terrorism-prevention techniques has assumed international importance since the 9/11 attacks. X-ray examination technology is a nondestructive screening method that enables the contents and internal structures of objects to be viewed without destroying the packaging or the external structures. Owing to its speed and efficiency compared to manual inspection, this technology is especially useful for the inspection of checked luggage and cargo, especially for inspecting items that cannot be disassembled. This paper presents developed training software for automated X-ray image recognition for checked luggage. To obtain the optimum classification result, an algorithm is designed using the C++ language. In Taiwan, the intensity of X-ray equipment is 10 times that of a typical X-ray baggage inspection machine. The use of reflected thermal neutrons in this equipment can reveal the presence of drugs, weapons, or explosives that are hidden in luggage. Using the Harris corner detection method and the speeded-up robust features (SURF) algorithm, the corner and feature points are obtained. In an evaluation, the proposed system uses a pattern-matching method and SURF to detect a 9-mm pistol. Compared to the results of a gun template image, the similarity obtained by the proposed method is increased by 0.85 to 1.81%.

Visual Information Fusion through Bayesian Inference for Adaptive Probability-Oriented Feature Matching.

This work presents a visual information fusion approach for robust probability-oriented feature matching. It is sustained by omnidirectional imaging, and it is tested in a visual localization framework, in mobile robotics. General visual localization methods have been extensively studied and optimized in terms of performance. However, one of the main threats that jeopardizes the final estimation is the presence of outliers. In this paper, we present several contributions to deal with that issue. First, 3D information data, associated with SURF (Speeded-Up Robust Feature) points detected on the images, is inferred under the Bayesian framework established by Gaussian processes (GPs). Such information represents a probability distribution for the feature points’ existence, which is successively fused and updated throughout the robot’s poses. Secondly, this distribution can be properly sampled and projected onto the next 2D image frame in , by means of a filter-motion prediction. This strategy permits obtaining relevant areas in the image reference system, from which probable matches could be detected, in terms of the accumulated probability of feature existence. This approach entails an adaptive probability-oriented matching search, which accounts for significant areas of the image, but it also considers unseen parts of the scene, thanks to an internal modulation of the probability distribution domain, computed in terms of the current uncertainty of the system. The main outcomes confirm a robust feature matching, which permits producing consistent localization estimates, aided by the odometer’s prior to estimate the scale factor. Publicly available datasets have been used to validate the design and operation of the approach. Moreover, the proposal has been compared, firstly with a standard feature matching and secondly with a localization method, based on an inverse depth parametrization. The results confirm the validity of the approach in terms of feature matching, localization accuracy, and time consumption.

An efficient computerized decision support system for the analysis and 3D visualization of brain tumor

The quality of health services provided by medical centers varies widely, and there is often a large gap between the optimal standard of services when judged based on the locality of patients (rural or urban environments). This quality gap can have serious health consequences and major implications for patient’s timely and correct treatment. These deficiencies can manifest, for example, as a lack of quality services, misdiagnosis, medication errors, and unavailability of trained professionals. In medical imaging, MRI analysis assists radiologists and surgeons in developing patient treatment plans. Accurate segmentation of anomalous tissues and its correct 3D visualization plays an important role inappropriate treatment. In this context, we aim to develop an intelligent computer-aided diagnostic system focusing on human brain MRI analysis. We present brain tumor detection, segmentation, and its 3D visualization system, providing quality clinical services, regardless of geographical location, and level of expertise of medical specialists. In this research, brain magnetic resonance (MR) images are segmented using a semi-automatic and adaptive threshold selection method. After segmentation, the tumor is classified into malignant and benign based on a bag of words (BoW) driven robust support vector machine (SVM) classification model. The BoW feature extraction method is further amplified via speeded up robust features (SURF) incorporating its procedure of interest point selection. Finally, 3D visualization of the brain and tumor is achieved using volume marching cube algorithm which is used for rendering medical data. The effectiveness of the proposed system is verified over a dataset collected from 30 patients and achieved 99% accuracy. A subjective comparative analysis is also carried out between the proposed method and two state-of-the-art tools ITK-SNAP and 3D-Doctor. Experimental results indicate that the proposed system performed better than existing systems and assists radiologist determining the size, shape, and location of the tumor in the human brain.