scholarly journals Recognition and Grasping of Disorderly Stacked Wood Planks Using a Local Image Patch and Point Pair Feature Method

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6235
Author(s):  
Chengyi Xu ◽  
Ying Liu ◽  
Fenglong Ding ◽  
Zilong Zhuang
Keyword(s):  
Feature Matching ◽  
Recognition Rate ◽  
Image Features ◽  
Geometric Feature ◽  
Feature Descriptor ◽  
Image Patch ◽  
Feature Description ◽  
Point Pair ◽  
Image Patches ◽  
Local Image

Considering the difficult problem of robot recognition and grasping in the scenario of disorderly stacked wooden planks, a recognition and positioning method based on local image features and point pair geometric features is proposed here and we define a local patch point pair feature. First, we used self-developed scanning equipment to collect images of wood boards and a robot to drive a RGB-D camera to collect images of disorderly stacked wooden planks. The image patches cut from these images were input to a convolutional autoencoder to train and obtain a local texture feature descriptor that is robust to changes in perspective. Then, the small image patches around the point pairs of the plank model are extracted, and input into the trained encoder to obtain the feature vector of the image patch, combining the point pair geometric feature information to form a feature description code expressing the characteristics of the plank. After that, the robot drives the RGB-D camera to collect the local image patches of the point pairs in the area to be grasped in the scene of the stacked wooden planks, also obtaining the feature description code of the wooden planks to be grasped. Finally, through the process of point pair feature matching, pose voting and clustering, the pose of the plank to be grasped is determined. The robot grasping experiment here shows that both the recognition rate and grasping success rate of planks are high, reaching 95.3% and 93.8%, respectively. Compared with the traditional point pair feature method (PPF) and other methods, the method present here has obvious advantages and can be applied to stacked wood plank grasping environments.

scholarly journals Bipolar Log-Intensity-Variance Histogram Method for Local Image Patch Intensity Change Measurements

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Han Wang ◽  
Quan Shi ◽  
Zhihuo Xu ◽  
Ming Wei ◽  
Hanseok Ko
Keyword(s):  
Intensity Change ◽  
Image Patch ◽  
Fixed Position ◽  
Histogram Method ◽  
Probabilistic Measure ◽  
Object Movement ◽  
Image Patches ◽  
Intensity Changes ◽  
Sigmoid Functions ◽  
Local Image

For a fixed-position camera, the intensity changes of an image pixel are often caused by object movement or illumination change. This paper focuses on such a problem: given two adjacent local image patches, how can the causes of intensity change be determined? A bipolar log-intensity-variance histogram is proposed to describe the intensity variations on the chaos phase plot subspace. This is combined with two sigmoid functions to construct a probabilistic measure function. Experimental results show that the proposed measurements are more effective and robust than conventional methods to the cause of variation in image intensity.

scholarly journals Hardware Friendly Robust Synthetic Basis Feature Descriptor

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 847 ◽  
Author(s):  
Dong Zhang ◽  
Lindsey Ann Raven ◽  
Dah-Jye Lee ◽  
Meng Yu ◽  
Alok Desai
Keyword(s):  
Computer Vision ◽  
Feature Matching ◽  
Good Alternative ◽  
Image Features ◽  
Feature Descriptor ◽  
Binary Feature ◽  
Computer Vision Applications ◽  
Computational Simplicity ◽  
Discrete Manner ◽  
Predetermined Number

Finding corresponding image features between two images is often the first step for many computer vision algorithms. This paper introduces an improved synthetic basis feature descriptor algorithm that describes and compares image features in an efficient and discrete manner with rotation and scale invariance. It works by performing a number of similarity tests between the feature region surrounding the feature point and a predetermined number of synthetic basis images to generate a feature descriptor that uniquely describes the feature region. Features in two images are matched by comparing their descriptors. By only storing the similarity of the feature region to each synthetic basis image, the overall storage size is greatly reduced. In short, this new binary feature descriptor is designed to provide high feature matching accuracy with computational simplicity, relatively low resource usage, and a hardware friendly design for real-time vision applications. Experimental results show that our algorithm produces higher precision rates and larger number of correct matches than the original version and other mainstream algorithms and is a good alternative for common computer vision applications. Two applications that often have to cope with scaling and rotation variations are included in this work to demonstrate its performance.

scholarly journals Optimization and Implementation of Synthetic Basis Feature Descriptor on FPGA

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 391
Author(s):  
Dah-Jye Lee ◽  
Samuel G. Fuller ◽  
Alexander S. McCown
Keyword(s):  
High Speed ◽  
Feature Detection ◽  
Hardware Implementation ◽  
Feature Matching ◽  
Image Features ◽  
Image Feature ◽  
Superior Performance ◽  
Feature Descriptor ◽  
Binary Descriptors ◽  
Field Programmable

Feature detection, description, and matching are crucial steps for many computer vision algorithms. These steps rely on feature descriptors to match image features across sets of images. Previous work has shown that our SYnthetic BAsis (SYBA) feature descriptor can offer superior performance to other binary descriptors. This paper focused on various optimizations and hardware implementation of the newer and optimized version. The hardware implementation on a field-programmable gate array (FPGA) is a high-throughput low-latency solution which is critical for applications such as high-speed object detection and tracking, stereo vision, visual odometry, structure from motion, and optical flow. We compared our solution to other hardware designs of binary descriptors. We demonstrated that our implementation of SYBA as a feature descriptor in hardware offered superior image feature matching performance and used fewer resources than most binary feature descriptor implementations.

scholarly journals A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shoujun Tang ◽  
Mohammad Shabaz
Keyword(s):  
Face Recognition ◽  
Basal Ganglia ◽  
Recognition Rate ◽  
Great Accuracy ◽  
Recognition Algorithm ◽  
Image Features ◽  
Face Image ◽  
Geometric Feature ◽  
Face Images ◽  
High Recognition Accuracy

Face recognition is one of the popular areas of research in the field of computer vision. It is mainly used for identification and security system. One of the major challenges in face recognition is identification under numerous illumination environments by changing the direction of light or modifying the lighting magnitude. Exacting illumination invariant features is an effective approach to solve this problem. Conventional face recognition algorithms based on nonsubsampled contourlet transform (NSCT) and bionic mode are not capable enough to recognize the similar faces with great accuracy. Hence, in this paper, an attempt is made to propose an enhanced cerebellum-basal ganglia mechanism (CBGM) for face recognition. The integral projection and geometric feature assortment method are used to acquire the facial image features. The cognition model is deployed which is based on the cerebellum-basal ganglia mechanism and is applied for extraction of features from the face image to achieve greater accuracy for recognition of face images. The experimental results reveal that the enhanced CBGM algorithm can effectively recognize face images with greater accuracy. The recognition rate of 100 AR face images has been found to be 96.9%. The high recognition accuracy rate has been achieved by the proposed CBGM technique.

Vehicle Logo Recognition Based on Local Feature Descriptor

2012 ◽  
Vol 263-266 ◽  
pp. 2418-2421
Author(s):  
Sheng Ke Wang ◽  
Lili Liu ◽  
Xiaowei Xu
Keyword(s):  
Feature Matching ◽  
Recognition Rate ◽  
Recognition System ◽  
Feature Descriptor ◽  
Training Set ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Logo Recognition ◽  
Vehicle Logo Recognition ◽  
Scale Invariant Feature

In this paper, we present a comparison of the scale-invariant feature transforms (SIFT)-based feature-matching scheme and the speeded up robust features (SURF)-based feature-matching scheme in the field of vehicle logo recognition. We capture a set of logo images which are varied in illumination, blur, scale, and rotation. Six kinds of vehicle logo training set are formed using 25 images in average and the rest images are used to form the testing set. The Logo Recognition system that we programmed indicates a high recognition rate of the same kind of query images through adjusting different parameters.

scholarly journals IFRAD: A Fast Feature Descriptor for Remote Sensing Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3774
Author(s):  
Qinping Feng ◽  
Shuping Tao ◽  
Chunyu Liu ◽  
Hongsong Qu ◽  
Wei Xu
Keyword(s):  
Remote Sensing ◽  
Feature Matching ◽  
State Of The Art ◽  
Image Data ◽  
Feature Descriptor ◽  
Feature Description ◽  
Sensing Applications ◽  
Feature Similarity ◽  
Feature Based ◽  
Remote Sensing Applications

Feature description is a necessary process for implementing feature-based remote sensing applications. Due to the limited resources in satellite platforms and the considerable amount of image data, feature description—which is a process before feature matching—has to be fast and reliable. Currently, the state-of-the-art feature description methods are time-consuming as they need to quantitatively describe the detected features according to the surrounding gradients or pixels. Here, we propose a novel feature descriptor called Inter-Feature Relative Azimuth and Distance (IFRAD), which will describe a feature according to its relation to other features in an image. The IFRAD will be utilized after detecting some FAST-alike features: it first selects some stable features according to criteria, then calculates their relationships, such as their relative distances and azimuths, followed by describing the relationships according to some regulations, making them distinguishable while keeping affine-invariance to some extent. Finally, a special feature-similarity evaluator is designed to match features in two images. Compared with other state-of-the-art algorithms, the proposed method has significant improvements in computational efficiency at the expense of reasonable reductions in scale invariance.

Novel Algorithm for Hand Vein Feature Extraction and Recognition with Biometrics Material Based on DEFD-SIFT Method

2012 ◽  
Vol 459 ◽  
pp. 428-431 ◽  
Author(s):  
Hua Bin Wang ◽  
Liang Tao
Keyword(s):  
Feature Extraction ◽  
Near Infrared ◽  
High Efficiency ◽  
Feature Matching ◽  
Recognition Rate ◽  
Feature Analysis ◽  
Feature Descriptor ◽  
Sift Feature ◽  
Hand Vein ◽  
Novel Algorithm

Based on the DEFD-SIFT feature analysis, this paper presents a novel algorithm for hand vein feature extraction and recognition. First of all, the principle of the near-infrared hand vein image acquisition is introduced. Secondly, the SIFT feature analysis algorithm is used to extract the feature of hand vein. We designed a novel neighborhood descriptor, which is called “Double Ellipses Feature Descriptor”. The local texture feature is extracted effectively, while reducing the interference of skin region. Finally, the SIFT feature matching algorithm based on similarity measure is given and the experimental results demonstrate the high efficiency of the proposed algorithm in runtime and correct recognition rate.

A novel 3D feature detection and matching approach for autonomous planetary landing mission

2018 ◽  
Vol 233 (6) ◽  
pp. 2241-2249 ◽  
Author(s):  
Yang Tian ◽  
Meng Yu ◽  
Yingying Zhang
Keyword(s):  
Pose Estimation ◽  
Feature Detection ◽  
Feature Matching ◽  
Feature Descriptor ◽  
Matching Algorithm ◽  
Feature Description ◽  
Natural Feature ◽  
Planetary Landing ◽  
Distance Set ◽  
Simulation Results

In an autonomous planetary/asteroid landing mission, landmark recognition is crucial to the success of the navigation system. The failure of feature detection or matching could lead to evident increase of bias in lander pose estimation. To this end, we propose a novel 3D feature detection and matching algorithm in this paper. The spherical harmonic coefficients are adopted to describe a 3D natural feature, and a relative distance set feature description approach is proposed as a supplement feature descriptor to enhance the distinctiveness of 3D feature. Simulation results demonstrate the effectiveness of our complete feature detection and matching algorithm in terms of feature detection rate and correct feature matching rate.

scholarly journals Image Feature Matching Based on Semantic Fusion Description and Spatial Consistency

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 725
Author(s):  
Wei Zhang ◽  
Guoying Zhang
Keyword(s):  
Feature Matching ◽  
Spatial Information ◽  
Pso Algorithm ◽  
Image Feature ◽  
Distance Constraint ◽  
Feature Points ◽  
Matching Method ◽  
Feature Description ◽  
Spatial Consistency ◽  
Image Patches

Image feature description and matching is widely used in computer vision, such as camera pose estimation. Traditional feature descriptions lack the semantic and spatial information, and give rise to a large number of feature mismatches. In order to improve the accuracy of image feature matching, a feature description and matching method, based on local semantic information fusion and feature spatial consistency, is proposed in this paper. Once object detection is used on images, feature points are then extracted, and image patches with various sizes surrounding these points are clipped. These patches are sent into the Siamese convolution network to get their semantic vectors. Then, semantic fusion description of feature points is obtained by weighted sum of the semantic vectors, and their weights optimized by particle swarm optimization (PSO) algorithm. When matching these feature points using their descriptions, feature spatial consistency is calculated based on the spatial consistency of matched objects, and the orientation and distance constraint of adjacent points within matched objects. With the description and matching method, the feature points are matched accurately and effectively. Our experiment results showed the efficiency of our methods.

scholarly journals 3D Affine: An Embedding of Local Image Features for Viewpoint Invariance Using RGB-D Sensor Data

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 291 ◽  
Author(s):  
Hamdi Sahloul ◽  
Shouhei Shirafuji ◽  
Jun Ota
Keyword(s):  
Low Cost ◽  
Image Features ◽  
Image Feature ◽  
Sensor Data ◽  
Invariant Representation ◽  
Depth Sensors ◽  
Viewpoint Invariance ◽  
Surface Discontinuities ◽  
Local Image ◽  
Local Image Features

Local image features are invariant to in-plane rotations and robust to minor viewpoint changes. However, the current detectors and descriptors for local image features fail to accommodate out-of-plane rotations larger than 25°–30°. Invariance to such viewpoint changes is essential for numerous applications, including wide baseline matching, 6D pose estimation, and object reconstruction. In this study, we present a general embedding that wraps a detector/descriptor pair in order to increase viewpoint invariance by exploiting input depth maps. The proposed embedding locates smooth surfaces within the input RGB-D images and projects them into a viewpoint invariant representation, enabling the detection and description of more viewpoint invariant features. Our embedding can be utilized with different combinations of descriptor/detector pairs, according to the desired application. Using synthetic and real-world objects, we evaluated the viewpoint invariance of various detectors and descriptors, for both standalone and embedded approaches. While standalone local image features fail to accommodate average viewpoint changes beyond 33.3°, our proposed embedding boosted the viewpoint invariance to different levels, depending on the scene geometry. Objects with distinct surface discontinuities were on average invariant up to 52.8°, and the overall average for all evaluated datasets was 45.4°. Similarly, out of a total of 140 combinations involving 20 local image features and various objects with distinct surface discontinuities, only a single standalone local image feature exceeded the goal of 60° viewpoint difference in just two combinations, as compared with 19 different local image features succeeding in 73 combinations when wrapped in the proposed embedding. Furthermore, the proposed approach operates robustly in the presence of input depth noise, even that of low-cost commodity depth sensors, and well beyond.

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