Binary Descriptors Research Articles

Pit slope unmanned aerial vehicle image matching based on hybrid scale space and boosted efficient binary local image descriptor

Pit slope unmanned aerial vehicle image matching based on hybrid scale space and boosted efficient binary local image descriptor

RBD-EVIO: Optimized Event-based Visual-Inertial Odometry for Large Field-of-View with Rotated Binary DART Descriptor

RBD-EVIO: Optimized Event-based Visual-Inertial Odometry for Large Field-of-View with Rotated Binary DART Descriptor

Advanced facial recognition with LBP-URIGL hybrid descriptors

AbstractFacial recognition technology is transformative in security and human-machine interaction, reshaping societal interactions. Robust descriptors, essential for high precision in machine learning tasks like recognition and recall, are integral to this transformation. This paper presents a hybrid model enhancing local binary pattern descriptors for facial representation. By integrating rotation-invariant local binary pattern with uniform rotation-invariant grey-level co-occurrence, employing linear discriminant analysis for feature space optimization, and utilizing an artificial neural network for classification, the model achieves exceptional accuracy rates of 100% for Olivetti Research Laboratory, 99.98% for Maastricht University Computer Vision Test, and 99.17% for Extended Yale B, surpassing traditional methods significantly.

Thermal Infrared Orthophoto Geometry Correction Using RGB Orthophoto for Unmanned Aerial Vehicle

The geometric correction of thermal infrared (TIR) orthophotos generated by unmanned aerial vehicles (UAVs) presents significant challenges due to low resolution and the difficulty of identifying ground control points (GCPs). This study addresses the limitations of real-time kinematic (RTK) UAV data acquisition, such as network instability and the inability to detect GCPs in TIR images, by proposing a method that utilizes RGB orthophotos as a reference for geometric correction. The accelerated-KAZE (AKAZE) method was applied to extract feature points between RGB and TIR orthophotos, integrating binary descriptors and absolute coordinate-based matching techniques. Geometric correction results demonstrated a significant improvement in regions with stable and changing environmental conditions. Invariant regions exhibited an accuracy of 0.7~2 px (0.01~0.04), while areas with temporal and spatial changes saw corrections within 5~7 px (0.10~0.14 m). This method reduces reliance on GCP measurements and provides an effective supplementary technique for cases where GCP detection is limited or unavailable. Additionally, this approach enhances time and economic efficiency, offering a reliable alternative for precise orthophoto generation across various sensor data.

Steering sulfur reduction kinetics of lithium-sulfur batteries by interfacial microenvironment modulation

Catalytic conversion of lithium polysulfides (LiPSs) is considered as an effective avenue to suppress the shuttle effect of lithium-sulfur (Li-S) batteries, for which the interfacial microenvironment constructed by the interaction between electrocatalysts and LiPSs plays a pivotal role in modulating the sulfur reduction kinetics. However, most of previous reports mainly focused on modulating the band structure of electrocatalysts or LiPSs alone to enhance the catalytic activity rather than considering the interfacial microenvironment as a whole. Herein, we propose a binary descriptor composed of the energy difference between d-band of electrocatalysts and p-band of LiPSs (ΔεM-S) and the antibonding filling degree (εABF), which capture the energy band contributions from both electrocatalysts and LiPSs, to reveal the influence of interfacial microenvironment on sulfur reduction kinetics. Among different designed electrocatalysts, NiO presents a moderate LiPSs anchoring capacity and rapid electron transfer kinetics owing to the optimal ΔεM-S and decreased εABF after interacting with LiPSs, which lead to a robust interfacial microenvironment and thus guarantee a continuous catalytic conversion of LiPSs in the long-term cycling. Such a fundamental understanding of catalytic activity toward LiPSs from the viewpoint of interfacial microenvironment provides an alternative avenue for designing highly efficient electrocatalysts for Li-S batteries.

MEDOIDS AS A PACKING OF ORB IMAGE DESCRIPTORS

The aim of the research. The paper presents the research about the feasibility to use matching medoids obtained from the set of ORB descriptors instead matching the full set of binary descriptors for image classification problem. Research results. Different methods that include direct brute force medoids matching, grouping of medoids for separate classes, and grouping of descriptors followed by calculation of medoids amongst them were proposed. Numerical experiments were performed for all these methods in order to compare the classification accuracy and inference time. It has been shown that using of medoids allowed us to redistribute processing time in order to perform more calculations during preprocessing rather than during classification. According to modelling performed on the Leeds Butterly dataset matching images based on medoids could have the same accuracy as matching of descriptors (0.69–0.88 for different number of features). Medoids require additional time for the calculation during preprocessing stage but classification time becomes faster: in our experiments we have obtained about 9–10 times faster classification and same 9–10 times increasing preprocessing time for the models that have comparable accuracies. Finally, the efficiency of the proposed ideas was compared to the CNN trained and evaluated on the same data. As expected, CNN required much more preprocessing (training) time but the result is worth it: this approach provides the best classification accuracy and inference time. Conclusion. Medoids matching could have the same accuracy as direct descriptors matching, but the usage of medoids allows us to redistribute the overall modeling time with the increasing preprocessing time and making inference faster.

Early detection of Alzheimer’s disease using squeeze and excitation network with local binary pattern descriptor

Early detection of Alzheimer’s disease using squeeze and excitation network with local binary pattern descriptor

Lightweight and Stable Multi-Feature Databases for Efficient Geometric Localization of Remote Sensing Images

The surge in remote sensing satellites and diverse imaging modes poses substantial challenges for ground systems. Swift and high-precision geolocation is the foundational requirement for subsequent remote sensing image applications. Breakthroughs in intelligent on-orbit processing now enable on-orbit geometric processing. In the absence of control data on board, a recent trend is to introduce reference data onto satellites. However, the pre-storage of traditional reference images or control point databases presents a significant challenge to the limited on-board data storage capacity. Therefore, oriented to the demand for control information acquisition during on-orbit geometry processing, we propose the construction of lightweight and stable feature databases. Initially, stable feature classes are obtained through iterative matching filtering, followed by re-extracting feature descriptors for each stable feature point location on the training images. Subsequently, the descriptors of each point location are clustered and fused using affinity propagation (AP) to eliminate redundancy. Finally, LDAHash is utilized to quantize floating-point descriptors into binary descriptors, further reducing the storage space. In our experiments, we utilize a variety of feature algorithms to assess the generality of our proposed method, thus extending the scope of the feature database and its applicability to various scenarios. This work plays a crucial role in advancing the technology of on-orbit geometry processing for remote sensing satellites.

B3D-EAR: Binarized 3D descriptors for ear-based human recognition

Traditional 3D feature descriptors often utilize real-valued vectors, posing challenges in terms of computational complexity and space constraints during matching. This study introduces a novel approach for generating binary 3D feature descriptors using correntropy, an online estimate of Renyi’s quadratic entropy. By employing this technique, the efficiency of state-of-the-art 3D descriptors is significantly enhanced. The effectiveness of the proposed methodology is demonstrated through evaluations on two datasets, UND-J2 and an in-house dataset, comprising 3D scans of human ears. Efficient algorithms for matching keypoints and their binary descriptors are employed, addressing both space and time complexities. The matching process is assessed using distance metrics and iterative closest point (ICP) alignment. Experimental results reveal that the method achieves a recognition performance of 98.62% with an equal error rate (EER) of 1.54%, comparable to state-of-the-art techniques. This highlights the efficacy of the method in generating binary descriptors without compromising accuracy.

A Partitioned CAM Architecture with FPGA Acceleration for Binary Descriptor Matching

An efficient architecture for image descriptor matching that uses a partitioned content-addressable memory (CAM)-based approach is proposed. CAM is frequently used in high-speed content-matching applications. However, due to its lack of functionality to support approximate matching, conventional CAM is not directly useful for image descriptor matching. Our modifications improve the CAM architecture to support approximate content matching for selecting image matches with local binary descriptors. Matches are based on Hamming distances computed for all possible pairs of binary descriptors extracted from two images. We demonstrate an FPGA-based implementation of our CAM-based descriptor-matching unit to illustrate the high matching speed of our design. The time complexity of our modified CAM method for binary descriptor matching is O(n). Our method performs binary descriptor matching at a rate of one descriptor per clock cycle at a frequency of 102 MHz. The resource utilization and timing metrics of several experiments are reported to demonstrate the efficacy and scalability of our design.

Completed Robust Local Binary Pattern Texture Descriptor for Classification of Facial Expression

Completed Robust Local Binary Pattern Texture Descriptor for Classification of Facial Expression

Comparison Between State-of-the-Art Color Local Binary Pattern-Based Descriptors for Image Retrieval

Comparison Between State-of-the-Art Color Local Binary Pattern-Based Descriptors for Image Retrieval

Data Augmentation for Genetic Programming-Driven Late Merging of HOG and Uniform LBP Features for Texture Classification

The categorization of texture images requires the identification and extraction of meaningful keypoints, a crucial step in ensuring the precise representation of textured images. The literature has introduced numerous descriptors in order to detect and capture both local and global texture characteristics. These descriptors vary in their effectiveness depending on the specific application. However, it is generally accepted that they complement each other by compensating for their strengths and weaknesses. Therefore, many approaches focused on combining different types of image descriptors generating robust features that are invariant to image transformations. These solutions mostly focused on one way to deal with information and faced more problems as they need human intervention and a large set of data. The acknowledged benefits of combining multiple types of descriptors are accompanied by challenges specially. These challenges arise due to differences in properties, such as locality and sparsity, as well as the heterogeneity exhibited by generated features. To address this issue comprehensively, we present an approach that benefits from genetic programming techniques to generate and combine two distinct texture classifiers. It incorporates histograms of oriented gradients and local binary patterns descriptors, which capture different textures. To effectively fuse the results of both classifiers, the proposed approach employs a late fusion process along with data augmentation methods while using a limited amount of training data. To evaluate the performance of the proposed approach in texture image classification tasks, we have conducted extensive experiments on six challenging datasets encompassing various variations. We have also investigated its performance in a cross dataset problem where the model has been trained on instances of a dataset before being tested on samples of another dataset. The obtained results clearly demonstrate that the proposed approach surpasses other relevant low-level approaches, as well as existing GP-based and CNN methods specifically designed for describing and classifying textures. Thanks to its ability to simultaneously leverage multiple descriptors, the suggested solution shows a high potential for real-world applications, particularly in handling various image changes with robustness.

Unraveling the Coupling Effect between Cathode and Anode toward Practical Lithium-Sulfur Batteries.

The localized reaction heterogeneity of the sulfur cathode and the uneven Li deposition on the Li anode are intractable issues for lithium-sulfur (Li-S) batteries under practical operation. Despite impressive progress in separately optimizing the sulfur cathode or Li anode, a comprehensive understanding of the highly coupled relationship between the cathode and anode is still lacking. In this work, inspired by the Butler-Volmer equation, a binary descriptor (IBD ) assisting the rational structural design of sulfur cathode by simultaneously considering the mass-transport index (Imass ) and the charge-transfer index (Icharge ) is identified, and subsequently the relationship between IBD and the morphological evolution of Li anode is established. Guided by the IBD , a scalable electrode providing interpenetrated flow channels for efficient mass/charge transfer, full utilization of active sulfur, and mechanically elastic support for aggressive electrochemical reactions under practical conditions is reported. These characteristics induce a homogenous distribution of local current densities and reduced reaction heterogeneity on both sides of the cathode and anode. Impressive energy density of 318Wh kg-1 and 473Wh L-1 in an Ah-level pouch cell can be achieved by the design concept. This work offers a promising paradigm for unlocking the interaction between cathode and anode and designing high-energy practical Li-S batteries.

Machine-learning-assisted design of a binary descriptor to decipher electronic and structural effects on sulfur reduction kinetics

Machine-learning-assisted design of a binary descriptor to decipher electronic and structural effects on sulfur reduction kinetics

Deep‐sea image stitching: Using multi‐channel fusion and improved AKAZE

AbstractDeep‐sea image is of great significance for exploring seabed resources. However, the information of a single image is limited. Besides, deep‐sea image with low contrast and colour distortion further restricts useful feature extraction. To address the issues above, this paper presents a multi‐channel fusion and accelerated‐KAZE (AKAZE) feature detection algorithm for deep‐sea image stitching. First, the authors restore deep‐sea image in LAB colour space and RGB colour space, respectively; in LAB space, the authors use homomorphic filtering in L colour channel, and in RGB space, the authors adopt multi‐scale Retinex with chromaticity preservation algorithm to adjust the colour information. Then, the authors blend two pre‐processed images with dark channel prior weighted coefficient. After that, the authors detect feature points with the AKAZE algorithm and obtain feature descriptors with Boosted Efficient Binary Local Image Descriptor. Finally, the authors match the feature points and warp deep‐sea images to obtain the stitched image. Experimental results demonstrate that the authors’ method generates high‐quality stitched image with minimized seam. Compared with state‐of‐the‐art algorithms, the proposed method has better quantitative evaluation, visual stitching results, and robustness.

Multiple object localization and tracking based on boosted efficient binary local image descriptor

Tracking multiple objects being an important problem in video surveillance. In the research, we provide a BEBLID (Boosted Efficient Binary Local Image Descriptor feature)- based tracking system. BEBLID being quick binary descriptor that works similarly to ORB, SIFT, or SURF and requires little processing. BEBLID key points and their related descriptions for the objects are first generated from two neighboring frames. The best match is then found by computing the Hamming distance between these two-point sets. The following localization of the objects may then be deduced using the key points that match. At the same, the object detection being facilitated by YOLOv3. Combined efforts from the two i.e., BEBLID and YOLOv3 being utilized for precise localization of the multiple objects. Identification of the localization of objects over time leads to the tracking mechanism. The effectiveness of our tracking technology is evaluated using datasets representing actual surveillance scenarios. The outcomes of the experiments demonstrate the suggested approach being capable of accurately and successfully tracking objects.•We proposed a multiple object tracking algorithm based on Boosted Efficient Binary Local Image Descriptor feature.•This algorithm utilizes the competencies of BEBLID and YOLOv3 to effectively detect and track multiple objects.•We validated this algorithm by comparing the results with other state-of-art algorithms presented in the literature.

An Optimal BDCNN ML Architecture for Car Make Model Prediction

We take on the challenge of classifying car photos, from the most general car type to the precise make, model, and year of the vehicle for a given input. Analyzing pre-existing datasets, we find that the CompCars-SV are a great place to begin our classification project. We demonstrate that convolutional neural networks can obtain a classification accuracy of more than 90% on the most difficult task. Due to a skewed mix between training and testing, this impressive result isn't really typical of how people do in the actual world. Using an ML system for car detection, we automatically generate a vehicle-tight bounding box for each picture, which we disseminate to the full dataset together with the existing (but limited) type-level annotation. We have designed and implemented car classification algorithms to analyze this car dataset, two of which take advantage of the hierarchical nature of car annotations. According to our research, a more precise classification of car type at a finer resolution now achieves an accuracy of 99.25%. It serves as a baseline benchmark for future research. Focusing on "vehicle" tasks, this work intends to bring attention to the vision community's lack of attention to these tasks compared to other objects. The important reason getting higher accuracy is extraction of binary descriptor (BD) feature using edge detection before training the CNN. This step reduced the size of the car dataset; hence network took less time to get trained. From the result outcomes shown it is clear that the presented network architecture having 31 layers of 2d convolutional layer, batch normalization, maxpool, ReLU, fully connected layer and Softmax classifier layer, has given higher accuracy. Numerous relevant car-related issues and solutions have yet to be carefully examined and researched, according to our findings. Car model categorization, model verification, and attribute prognosis are just a few examples of how the dataset might be put to use.

A Fast Image Matching Method Based on Improved SURF

In order to solve the problems of low matching accuracy, slow speed and high system overhead in image matching methods, a rotation binary descriptor construction method based on Speed Up Robust Features (SURF) feature point detection is designed by using different Fast Library for Approximate Nearest Neighbors (FLANN) parameters and the filtering mechanism to screen out wrong matches according to the types of feature descriptors constructed in different feature extraction algorithms. This method ensures scale and rotation invariant while simplifying the representation of feature descriptors and speeding up the calculation speed in the initial stage of matching by combining the binary characteristics of descriptors. Finally, the Hamming distance is used as the filtering mechanism to improve the success rate of the final matching. The experimental results show that the accuracy of image matching is improved by 1.5% and the matching time is improved by 0.116s, while the robustness of the image to noise and rotation is ensured.

Illumination-Insensitive Line Binary Descriptor Based on Hierarchical Band Difference

Similar to points, lines are also the basis for many vision applications. Line segments provide more structured information and are more robust than point features, especially for those scenarios with low textures. There are increasing works to tackle the line matching problem, but few of them are designed to match lines under drastic illumination changes, which is critical for long-term vision tasks. In this brief, an illumination-insensitive line binary (IILB) descriptor is proposed to tackle the above issue based on the band differences among multi-spatial granularities of the line region of support. The proposed IILB descriptor has a low computation cost due to the usage of integral images, which could satisfy the requirements of many realistic platforms with limited computation and storage abilities. Numerical experiments in natural and synthetic datasets show that the proposed IILB descriptor has remarkable performance. The code of the IILB descriptor is publicly available1.