Feature Descriptor Vector Research Articles

Efficient coarse registration method using translation- and rotation-invariant local descriptors towards fully automated forest inventory

In this paper, we present a simple, efficient, and robust algorithm for 2D coarse registration of two point clouds. In the proposed algorithm, the locations of some distinct objects are detected from the point cloud data, and a rotation- and translation-invariant feature descriptor vector is computed for each of the detected objects based on the relative locations of the neighboring objects. Subsequently, the feature descriptors obtained for the different point clouds are compared against one another by using the Euclidean distance in the feature space as the similarity criterion. By using the nearest neighbor distance ratio, the most promising matching object pairs are found and further used to fit the optimal Euclidean transformation between the two point clouds. Importantly, the time complexity of the proposed algorithm scales quadratically in the number of objects detected from the point clouds. We demonstrate the proposed algorithm in the context of forest inventory by performing coarse registration between terrestrial and airborne point clouds. To this end, we use trees as the objects and perform the coarse registration by using no other information than the locations of the detected trees. We evaluate the performance of the algorithm using both simulations and three test sites located in a boreal forest. We show that the algorithm is fast and performs well for a large range of stem densities and for test sites with up to 10 ​000 trees. Additionally, we show that the algorithm works reliably even in the case of moderate errors in the tree locations, commission and omission errors in the tree detection, and partial overlap of the data sets. We also demonstrate that additional tree attributes can be incorporated into the proposed feature descriptor to improve the robustness of the registration algorithm provided that reliable information of these additional tree attributes is available. Furthermore, we show that the registration accuracy between the terrestrial and airborne point clouds can be significantly improved if stem positions estimated from the terrestrial data are matched to stem positions obtained from the airborne data instead of matching them to tree top positions estimated from the airborne data. Even though the 2D coarse registration algorithm is demonstrated in the context of forestry, the algorithm is not restricted to forest data and it may potentially be utilized in other applications, in which efficient 2D point set registration is needed.

Pairwise registration for terrestrial laser scanner point clouds based on the covariance matrix

ABSTRACT Automatic pairwise registration for unordered terrestrial laser scanner (TLS) point clouds is the pre-requisite for many applications, including 3D model reconstruction, cultural heritage management and landslide monitoring. However, most of the existing registration methods are still suffering from several limitations: (1) difficult to find the correct corresponding point pairs from the source and target point cloud, (2) low accuracy of pairwise registration and (3) the iterative process of registration is time-consuming. To overcome these challenges, based on the covariance matrix, this paper presents a robust and descriptive feature descriptor vector (FDV) to locally describe a point, by which the corresponding point pairs are obtained and the registration matrix is calculated. First, the FDVs of the original point cloud’s keypoints are calculated. Second, the corresponding point pairs are found via their FDVs. Third, the coarse transformation matrix is calculated by the corresponding point pairs using the singular value decomposition (SVD) algorithm, and which is further refined by the iterative closest point (ICP) algorithm to get a better result. Finally, the experiments are conducted on the Autonomous Systems Lab (ASL) and ISPRS datasets; our results show that the proposed algorithm can obtain good registration performance, and outperforms the compared methods.

Ensemble Visual Content Based Search and Retrieval for Natural Scene Images

Background: Content Based Image Retrieval (CBIR) is one of the fields for information retrieval where similar images are retrieved from database based on the various image descriptive parameters. The image descriptor vector is used by machine learning based systems to store, learn and template matching. These feature descriptor vectors locally or globally demonstrate the visual content present in an image using texture, colour, shape, and other information. Objective: The main vision of this paper is to categorize and evaluate those algorithms, which were proposed in the interval of last 10 years. In addition, experiment is performed using a hybrid content descriptors methodology that helps to gain the significant results as compared with state-of-art algorithms. Methods: In past, several algorithms were proposed to fetch the variety of contents from an image based on which the image is retrieved from database. But, the precision and recall for the gained results using single content descriptor is not significant. The proposed system architecture uses the hybrid ensemble feature set for image matching. The hybrid parameters include globally and locally defined feature extraction methodologies. Results: The hybrid methodology decreases the error rate and improves the precision and recall for large natural scene images dataset having more than 20 classes. The overall combination will provide almost 97% accurate results which is better than the existing literature results. Conclusion: In conclusion the experimentation result suggests that the use of the local feature extraction mechanism is better when compared with the global feature extraction methodology.

High-speed real-time augmented reality tracking algorithm model of camera based on mixed feature points

At this stage of augmented reality, simple feature descriptions are mainly used in camera real-time motion tracking, but this is prone to the problem of unstable camera motion tracking. Aiming at the balance between real-time performance and stability, a new method model of real-time camera motion tracking based on mixed features was proposed. By comprehensively using feature points and feature lines as scene features, feature extraction, optimization, and fusion are used to construct hybrid features, and the hybrid features are unified for real-time camera parameter estimation. An image feature optimization method based on scene structure analysis is proposed to meet the computing constraints of mobile terminals. An iterative feature line-screening method is proposed to calculate a stable feature line set, and based on the scene feature composition and feature geometry, a hybrid feature is adaptively constructed to improve the tracking stability of the camera. Based on improved SIFT feature matching target detection and tracking algorithm, a hybrid feature point detection operator detection algorithm is used to achieve rapid feature point extraction, and the speed of descriptor generation is reduced by reducing the feature descriptor vector dimension. The experimental results prove that the proposed target detection and tracking algorithm has good real-time and robustness, and improves the success rate of target detection and tracking.

Improved photometric acceptance testing in image feature extraction tasks

Image correspondence is established by “matching” the feature descriptors of the interest points in the target image to that of the reference image. By acceptance testing, we refer to a postmatching hypothesis test used to screen out potential false matches—conventionally using descriptor test statistics. We propose a new acceptance testing strategy that does not rely on the descriptor test statistics exclusively. The contribution we bring is to demonstrate that, unlike feature matching, acceptance testing may incorporate additional photometric values of the scene to improve the recall rate. We show experimentally that the acceptance testing strategy that incorporates image feature detection statistics we refer to as detector response-ratio thresholding that are usually excluded from the feature descriptor vectors has a superior recall–precision performance compared to the state-of-the-art feature extraction techniques.

Compact and Low-Complexity Binary Feature Descriptor and Fisher Vectors for Video Analytics.

In this paper, we propose a compact and low-complexity binary feature descriptor for video analytics. Our binary descriptor encodes the motion information of a spatio-temporal support region into a low-dimensional binary string. The descriptor is based on a binning strategy and a construction that binarizes separately the horizontal and vertical motion components of the spatio-temporal support region. We pair our descriptor with a novel Fisher Vector (FV) scheme for binary data to project a set of binary features into a fixed length vector in order to evaluate the similarity between feature sets. We test the effectiveness of our binary feature descriptor with FVs for action recognition, which is one of the most challenging tasks in computer vision, as well as gait recognition and animal behavior clustering. Several experiments on the KTH, UCF50, UCF101, CASIA-B, and TIGdog datasets show that the proposed binary feature descriptor outperforms the state-of-the-art feature descriptors in terms of computational time and memory and storage requirements. When paired with FVs, the proposed feature descriptor attains a very competitive performance, outperforming several state-of-the-art feature descriptors and some methods based on convolutional neural networks.

Local Deep Descriptor for Remote Sensing Image Feature Matching

Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate that the feature vector generated by the DescNet could acquire more matched points than those generated by hand-crafted feature Scale Invariant Feature Transform (SIFT) descriptor and other networks. On average, the matched points acquired by DescNet was almost twice those acquired by other methods. Finally, we analyzed the advantages of our created training dataset Invar-Dataset and DescNet and gave the possible development of training deep descriptor network.

A novel method for SIFT features matching based on feature dimension matching degree

We proposes a method for fast matching SIFT feature points based on SIFT feature descriptor vector element matching. First, we discretize each dimensional feature element into an array address based on a fixed threshold value and store the corresponding feature point labels in an address. If the same dimensional feature element of the descriptor vector has the same discrete value, their feature point labels may fall into the same address. Secondly, we search the mapping address of the feature descriptor vector element to obtain the matching state of the corresponding dimensions of the feature descriptor vector, thus obtaining the number of dimensions matching between feature points and feature dimension matching degree. Then we use the feature dimension matching degree to obtain the suspect matching feature points. Finally we use the Euclidean distance to eliminate the mismatching feature points to obtain accurate matching feature point pairs. The method is essentially a high-dimensional feature vector matching method based on local feature vector element matching. Experimental results show that the new algorithm can guarantee the number of matching SIFT feature points and their matching accuracy and that its running time is similar to that of HKMT, RKDT and LSH algorithms

Ear recognition in 3D using 2D curvilinear features

This study presents a novel approach for human recognition using co-registered three-dimensional (3D) and 2D ear images. The proposed technique is based on local feature detection and description. The authors detect feature key-points in 2D ear images utilising curvilinear structure and map them to the 3D ear images. Considering a neighbourhood around each mapped key-point in 3D, a feature descriptor vector is computed. To match a probe 3D ear image with a gallery 3D ear image for recognition, first highly similar feature key-points of these images are used as correspondence points for an initial alignment. Afterwards, a fine iterative closest point matching is performed on entire data of the 3D ear images being matched. An extensive experimental analysis is performed to demonstrate the recognition performance of the proposed approach in the presence of noise and occlusions, and compared with the available state-of-the-art 3D ear recognition techniques. The recognition rate of the proposed technique is found to be 98.69% on the University of Notre Dame-Collection J2 dataset with an equal error rate of 1.53%.

Local directional ternary pattern: A New texture descriptor for texture classification

In this paper, the three level descriptions from LTP and the directional features from LDP are combined to form a new local feature descriptor, referred to as local directional ternary pattern (LDTP) for texture classification. LDTP is a framework, which consists in encoding both contrast information and directional pattern features in a compact way based on local derivative variations. To achieve robustness, the proposed operator first computes for each pixel within its 3 × 3 overlapping grayscale image patch, on the one hand, eight directional edge responses using the eight Frei–Chen masks, and on the other hand, central edge response through the 2nd derivative of Gaussian filter to capture more detailed information. This allows producing a more discriminative encoding than several state-of-the art methods based only on intensity information. Then, spatial relationships among the neighboring pixels through the edge responses are exploited independently with the help of both LDP’s and LTP’s concepts to enhance the discrimination capability. Indeed, the implicit utilization of both concepts of LTP and LDP encodes more information in comparison to the existing directional and derivative methods in less space, and simultaneously allows discriminating more textures. Finally, the resultant LDTP pattern is divided into two distinct parts: local directional ternary pattern upper (LDTPU) and local directional ternary pattern lower (LDTPL), and the final feature descriptor vector is obtained by linear concatenation of both LDTPU and LDTPL histograms. The experiments carried out on nine publicly available texture datasets demonstrated that the proposed LDTP descriptor achieves classification performance, which is competitive or better than several recent and old state-of-the-art LBP variants. Statistical significance of the achieved accuracy improvement by the proposed descriptor has been also demonstrated through the Wilcoxon signed rank test applied on all the tested datasets.

A Distributed Wireless Camera System for the Management of Parking Spaces.

The importance of detection of parking space availability is still growing, particularly in major cities. This paper deals with the design of a distributed wireless camera system for the management of parking spaces, which can determine occupancy of the parking space based on the information from multiple cameras. The proposed system uses small camera modules based on Raspberry Pi Zero and computationally efficient algorithm for the occupancy detection based on the histogram of oriented gradients (HOG) feature descriptor and support vector machine (SVM) classifier. We have included information about the orientation of the vehicle as a supporting feature, which has enabled us to achieve better accuracy. The described solution can deliver occupancy information at the rate of 10 parking spaces per second with more than 90% accuracy in a wide range of conditions. Reliability of the implemented algorithm is evaluated with three different test sets which altogether contain over 700,000 samples of parking spaces.

Fingertip Detection Using Histogram of Gradients and Support Vector Machine

<p>One important application in computer vision is detection of objects. One important application in computer vision is detection of objects. This paper discusses detection of fingertips by using Histogram of Gradients (HOG) as the feature descriptor and Support Vector Machines (SVM) as the classifier. The SVM is trained to produce a classifier that is able to distinguish whether an image contains a fingertip or not. A total of 4200 images were collected by using a commercial-grade webcam, consisting of 2100 fingertip images and 2100 non-fingertip images, were used in the experiment. Our work evaluates the performance of the fingertip detection and the effects of the cell’s size of the HOG and the number of the training data have been studied. It has been found that as expected, the performance of the detection is improved as the number of training data is increased. Additionally, it has also been observed that the 10 x 10 size gives the best results in terms of accuracy in the detection. The highest classification accuracy obtained was less than 90%, which is thought mainly due to the changing orientation of the fingertip and quality of the images. </p>

Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine.

BackgroundLung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool.MethodsThe stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules.ResultsThe clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%.ConclusionsThe CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.

Precise Cross-Range Scaling for ISAR Images Using Feature Registration

This letter proposes a precise cross-range scaling algorithm for inverse synthetic aperture radar (ISAR) images by estimating the effective rotation angle through coordinate locations of feature points extracted from two sequenced subaperture ISAR images. In the approach, we first extract adequate feature points and feature descriptor vectors from these two images by scale-invariant feature transform and speeded-up robust features. Then, a two-stage registering scheme is employed to match these feature points to link the two images. Consequently, the effective rotation angle is efficiently and robustly estimated by evaluating a cost function based on the coordinate locations of the matched feature points. Experiments of simulated and real signals validate this proposal.

Binarized eigenphases applied to limited memory face recognition systems

Most of the algorithms proposed for face recognition involve considerable amount of computations and hence they cannot be used on devices constrained with limited memory. In this paper, we propose a novel solution for efficient face recognition problem for the systems that utilize low memory devices. The new technique applies the principal component analysis to the binarized phase spectrum of the Fourier transform of the covariance matrix constructed from the MPEG-7 Fourier Feature Descriptor vectors of the images. The binarization step that is applied to the phases adds many interesting advantages to the system. It will be shown that the proposed technique increases the face recognition rate and at the same time achieves substantial savings in the computational time, when compared to other known systems. Experiments on two independent databases of face images are reported to demonstrate the effectiveness of the proposed technique.