Polar Histogram Research Articles

Quantitative research on the degree of disorder of traditional settlements: a case study of Liangjia Village, Jingxing, Hebei Province

Traditional settlements are vital carriers of Chinese history and culture, reflecting the direct and original needs of generations. However, the modernization of settlements will inevitably disrupt the spontaneous growth patterns inherent in traditional settlements. The degree of disorder of the settlement is not only the embodiment of its form, but also the concrete manifestation of its construction intention. We use the degree of disorder in settlements to assess the construction intentions of the settlements, the results allow us to assess whether new planning aligns with the original construction logic, thereby assessing the extent of interference by modernization in traditional villages. This can provide guidance and assistance for settlement conservation and future planning. By using the Delaunay triangulation network, street polar histogram and direction entropy, and comprehensibility of settlements, this paper quantitatively analyzes the degree of disorder in the three levels of architecture, street and local environment of the settlement. Liangjia Village, the example in this research, is divided into three areas—two new and one old. degree of disorders in architecture, streets, and local environment are calculated and compared with the overall settlement. The study finds that the construction logic for buildings and streets remains stable throughout development. However, notable differences exist in environmental construction principles between new and old sections. The analysis suggests that traditional settlements, guided by inherent principles, maintain stability under external influences, serving as a benchmark for future planning and modernization decisions.

Global principal planes aided LiDAR-based mobile mapping method in artificial environments

3-D mapping of buildings is crucial for urban renewal, but traditional LiDAR-based mapping methods are often less effective for buildings with narrow spaces and limited geometric features. Current methods attempt to overcome this by integrating additional sensors, such as cameras, which increases cost and complexity. This paper proposes a novel LiDAR-based mobile mapping framework using global principal planes (GPPs) to address this challenge without additional sensors. GPPs are defined as unlimited planes characterized by principal normal vectors (PNVs). GPPs can provide stronger constraints than traditional small planes extracted from one or certain LiDAR frames because they are little affected by the accumulative error from point cloud matching. A PNV estimation method is also proposed based on an inertial measurement unit and polar histogram, and PNVs are axes of the natural cartesian XYZ coordinate system. Point clouds are transformed into the PNVs coordinate system to extract robust edge and plane feature points and GPPs. The proposed framework is tested in various environments. It achieves about 3 cm accuracy in corridors and similar accuracy in stairwells. Compared to five state-of-the-art mapping methods (Cartographer, etc.), its accuracy improves by over 76%, increasing at least an order of magnitude. In the outdoor KITTI dataset, it shows a reduction in absolute pose errors by 4% to 20%. Extensive experiments demonstrate its accuracy, robustness, and generalizability. Ablation experiments further validate the efficacy of different components in the framework.

Obstacle avoidance and collision avoidance of UAV swarm based on improved VFH algorithm and information sharing strategy

An obstacle avoidance and collision avoidance approach based on an improved VFH algorithm with Apollonius circle and information sharing strategy is put forward with the goal of guiding UAV swarms in formation flight in an unknown complex environment. For the second-order consensus protocol, a cooperative control law with the virtual leader is presented. For collision avoidance, the vector field histogram algorithm with Apollonius circle is proposed, and the obstacle sharing strategy is proposed to avoid obstacles in advance and solve the formation separation problem. The Apollonius circle is introduced to prevent the entanglement problem brought on by symmetry conflicts, and this approach aims to address the problem of collision avoidance among UAVs by updating the polar coordinate histogram based on the VFH algorithm in accordance with the motion state and relative position of other UAVs in the communication neighborhood. The equivalent detection range of UAVs in the swarm connected topology can be increased by sharing the locations of identified obstacles, improving total obstacle avoidance. Also, to reduce repetitive data interactions and communication loads when sharing obstacle information, a data offset mechanism is proposed, which stores the obstacle information in a chronological queue and sets a data offset in the queue for each UAV in the communication neighborhood to indicate the degree of synchronization with the data in the queue. Finally, simulation results show the effectiveness of the algorithm in collision avoidance and real-time obstacle avoidance of UAV swarm. Additionally, a data offset mechanism is suggested, which stores obstacle information in a chronological queue and sets a data offset in the queue for each UAV in the communication neighborhood to represent the degree of synchronization with the data in the queue, to reduce repetitive data interactions and communication loads when sharing obstacle information. Finally, simulation results demonstrate the algorithm’s effectiveness in real-time obstacle avoidance and collision avoidance for UAV swarms.

An Extended Vector Polar Histogram Method Using Omni-Directional LiDAR Information

This study presents an extended vector polar histogram (EVPH) method for efficient robot navigation using omni-directional LiDAR data. Although the conventional vector polar histogram (VPH) method is a powerful technique suitable for LiDAR sensors, it is limited in its sensing range by the single LiDAR sensor to a semicircle. To address this limitation, the EVPH method incorporates multiple LiDAR sensor’s data for omni-directional sensing. First off, in the EVPH method, the LiDAR sensor coordinate systems are directly transformed into the robot coordinate system to obtain an omni-directional polar histogram. Several techniques are also employed in this process, such as minimum value selection and linear interpolation, to generate a uniform omni-directional polar histogram. The resulting histogram is modified to represent the robot as a single point. Subsequently, consecutive points in the histogram are grouped to construct a symbol function for excluding concave blocks and a threshold function for safety. These functions are combined to determine the maximum cost value that generates the robot’s next heading angle. Robot backward motion is made feasible based on the determined heading angle, enabling the calculation of the velocity vector for time-efficient and collision-free navigation. To assess the efficacy of the proposed EVPH method, experiments were carried out in two environments where humans and obstacles coexist. The results showed that, compared to the conventional method, the robot traveled safely and efficiently in terms of the accumulated amount of rotations, total traveling distance, and time using the EVPH method. In the future, our plan includes enhancing the robustness of the proposed method in congested environments by integrating parameter adaptation and dynamic object estimation methods.

Cover Image

COVER ILLUSTRATION Seeing the invisible – Intraoperative Optical Imaging (IOI) is a technique that gives us unique insights on the human brain and it's function. The method uses subtle changes in the optical properties of the exposed cortical brain tissue to detect and visualize hemodynamic changes which are connected to specific brain functions. Since IOI needs beside an undisturbed view on the cortical surface only a camera system and light within the visible range it is well suited for application during neurosurgical interventions. In our paper “Characterization of Cortical Hemodynamic Changes Following Sensory, Visual, and Speech Activation by Intraoperative Optical Imaging Utilizing Phase-Based Evaluation Methods” we used IOI to characterize the hemodynamic changes on the brain surface following different types of stimulation in patients that underwent brain tumor surgery. The graphics illustrates the several aspects of the methodology and the results of our paper. The three images in the middle are showing phase angle maps, overlaid to the white light image of the exposed brain surfaces. The different maps are representing the response towards visual (left), sensory (right), and speech activation (middle) as shown in the result section of the paper and supported by the text above the whole image composition. The grid in the front is a polar histogram, we used for quantitative analysis and visualization of the results. Overlaid to the grid is the colorbar that we used for the phase angle maps, shaped as vortex. The colors correspond with different phase angles of the polar histogram. To underline the fact that IOI can be used to visualize functional brain areas during neurosurgical interventions, we added in the back an abstract painting of a neurosurgeon, looking through the sterile draped surgical microscope. The light source references to the fact, that IOI is an optical technology and therefore based on light.

Robust sclera recognition based on a local spherical structure

The sclera is the opaque white tissue that covers the sphere of the human eye, and this causes a complex nonlinear deformation of the vessel pattern in the sclera as the eyes move. Over the last decade, although the blood vessels of the sclera have been investigated for use as a biometric modality, very little research has been undertaken on feature extraction and matching based on this anatomical structure. To address this problem, we propose a new local spherical structure (LSS) that incorporates a vessel shape feature (VSF) to represent the geometry and texture feature of the vessel pattern. The key ideas in this work are the designs of the VSF and the LSS, which are based on the fact that the vessels lie below a sphere-shaped sclera. To create the proposed VSF, a Harris corner detector is used to extract interest points, each of which is encoded into a normalized polar histogram that represents the shape of a vessel. These points are then projected onto the surface of a sphere, and we design an LSS to represent the topological relationships between each of the points, derived from the arc distance. In the matching step, we adopt a coarse-to-fine procedure that enables a fast similarity comparison between two point pairs using the VSF and the LSS. The final matching score is obtained by combining the number of matched pairs, the total number of interest points, and the similarity. Various experiments were conducted on the UBIRIS.v1 and UTIRIS databases, the latter of which was more challenging. The proposed method showed promising performance, as it yielded an error rate equal to those of other feature extraction and matching methods. Our findings suggest that the proposed approach not only outperforms the state-of-the-art methods of sclera recognition but also illustrates how to address the issue of nonlinear deformation of the vessel pattern based on the anatomical structure of the human eye.

Computer Vision and Artificial Intelligence Based Navigation on Top-view Virtual Plane

This paper shows a vision-based obstacle detection approach in an outdoor sidewalk environment. There is a major problem of applying single camera in outdoor navigation mission, that is the differentiation of various types of barriers and clutter pavement. To settle this issue, the property that pavement margins are sub-sampled while barrier margins are oversampled when the original image is mapped to the virtual plane of the top view. Morphology filters are applied to strengthen barrier margins as margin-blobs with greater size, while sparse margins from pavement are screened. On basis of the recognized barriers, secure walking areas are predicted by tracking a polar margin-blob histogram. The test of algorithm is made in various sidewalk scenes with complicated pavements with confirmed potency.

Novel Method for Analyzing Flexible Locomotion Patterns of Animals by Using Polar Histogram

In general, legged robots are designed to walk with a fixed rhythmic pattern. However, most animals can adapt their limb movements while walking. It is necessary to understand the mechanism of adaptability during locomotion when designing bio-inspired legged robots. In this paper, we propose an approach to analyze the flexible locomotion pattern of animals using a polar histogram. Field crickets were used to investigate variations in leg movement of insects depending on the environment. Crickets have a tripod gait; however, their leg movement changes depending on the texture of the ground. There was a significant difference between the leg movement when walking and when swimming. Our approach can explain how animals move their legs during locomotion. This study is useful for evaluating the movements of legged robots.

SCHEEPDOG: Programming Electric Cues to Dynamically Herd Large-Scale Cell Migration.

Directed cell migration is critical across biological processes spanning healing to cancer invasion, yet no existing tools allow real-time interactive guidance over such migration. We present a new bioreactor that harnesses electrotaxis-directed cell migration along electric field gradients-by integrating four independent electrodes under computer control to dynamically program electric field patterns, and hence steer cell migration. Using this platform, we programmed and characterized multiple precise, two-dimensional collective migration maneuvers in renal epithelia and primary skin keratinocyte ensembles. First, we demonstrated on-demand, 90-degree collective turning. Next, we developed a universal electrical stimulation scheme capable of programming arbitrary 2D migration maneuvers such as precise angular turns and migration in a complete circle. Our stimulation scheme proves that cells effectively time-average electric field cues, helping to elucidate the transduction timescales in electrotaxis. Together, this work represents an enabling platform for controlling cell migration with broad utility across many cell types.

OCR FOR ENGLISH CHARACTERS BASED ON POLAR HISTOGRAM FEATURE EXTRACTION AND EUCLIDEAN DISTANCE

Optical character recognition is the process of converting characters from image format to text format. The process includes four main stages namely: pre-processing, feature extraction, character recognition, and post-processing. The success is mainly based on the feature extraction method and the character recognition algorithm. When optical character recognition systems are developed for mobile devices, two main constraints must be addressed. They are the system’s size and speed. In this research, both were considered. A small feature dataset was created using polar histogram of x and y projections of the character image. In addition, a simple character recognition algorithm based on Euclidean distance was adapted. An accuracy of over 99.9% was achieved in near microsecond scale recognizing execution time on the development system.

Comparison of Image Duplication Detection Using the Polar Coordinates System and Histogram of Oriented Gradients Methods

Comparison of Image Duplication Detection Using the Polar Coordinates System and Histogram of Oriented Gradients Methods

무인수상선의 단일 카메라를 이용한 VFH+ 기반 장애물 회피 기법

Recently, many unmanned surface vehicles (USVs) have been developed and researched for various fields such as the military, environment, and robotics. In order to perform purpose specific tasks, common autonomous navigation technologies are needed. Obstacle avoidance is important for safe autonomous navigation. This paper describes a vector field histogram+ (VFH+) based obstacle avoidance method that uses the monocular vision of an unmanned surface vehicle. After creating a polar histogram using VFH+, an open space without the histogram is selected in the moving direction. Instead of distance sensor data, monocular vision data are used for make the polar histogram, which includes obstacle information. An object on the water is recognized as an obstacle because this method is for USV. The results of a simulation with sea images showed that we can verify a change in the moving direction according to the position of objects.

Constrained VPH+: a local path planning algorithm for a bio-inspired crawling robot with customized ultrasonic scanning sensor.

This paper aims to develop a local path planning algorithm for a bio-inspired, reconfigurable crawling robot. A detailed description of the robotic platform is first provided, and the suitability for deployment of each of the current state-of-the-art local path planners is analyzed after an extensive literature review. The Enhanced Vector Polar Histogram algorithm is described and reformulated to better fit the requirements of the platform. The algorithm is deployed on the robotic platform in crawling configuration and favorably compared with other state-of-the-art local path planning algorithms.

Classifying GABAergic interneurons with semi-supervised projected model-based clustering

A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names. We sought to automatically classify digitally reconstructed interneuronal morphologies according to this scheme. Simultaneously, we sought to discover possible subtypes of these types that might emerge during automatic classification (clustering). We also investigated which morphometric properties were most relevant for this classification. A set of 118 digitally reconstructed interneuronal morphologies classified into the common basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of the world's leading neuroscientists, quantified by five simple morphometric properties of the axon and four of the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. We then removed this class information for each type separately, and applied semi-supervised clustering to those cells (keeping the others' cluster membership fixed), to assess separation from other types and look for the formation of new groups (subtypes). We performed this same experiment unlabeling the cells of two types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixture of Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performed the described experiments on three different subsets of the data, formed according to how many experts agreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least 26 (47 neurons). Interneurons with more reliable type labels were classified more accurately. We classified HT cells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy, respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, and no subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette width and ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively, confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a single type also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometric properties were more relevant that dendritic ones, with the axonal polar histogram length in the [π, 2π) angle interval being particularly useful. The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heterogeneous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones for distinguishing among the CB, HT, LB, and MA interneuron types.

Traversability based obstacle avoidance path-planning and path-following control for lunar rover

In this paper, an obstacle avoidance path-planning algorithm and a path-following controller are developed for a lunar rover in simulated 3-D lunar terrains. Firstly, traversability analysis of the lunar surface is studied based on the weighted least squares method and fuzzy computing. Secondly, a polar coordinates histogram is built around the rover to measure the traversability to enable selection of the optimal driving direction. Thirdly, a longitudinal traction coordinating controller based on sliding mode theory and a latitudinal trajectory tracking controller based on optimal theory are proposed to follow the desired path. Finally, a six-wheel lunar rover with Rocker Bogie model is established in ADAMS software, and co-simulation between ADAMS and MATLAB/Simulink are carried out to verify the effectiveness of the proposed algorithm.

Robust Robot Navigation using Polar Coordinates in Dynamic Environments

This paper presents robust robot navigation using polar coordinates in dynamic environments based on our previous work which was to move toward a target while avoiding static and dynamic obstacles. It utilized vector polar histogram (VPH) with velocity obstacle (VO) for the navigation and obstacle avoidance but had difficulties in detecting dynamic obstacles and reaching the target accurately. To overcome those problems, polar scan matching (PSM) is combined with our previous work. It allows the robot to localize its pose more correctly, which causes an improvement of dynamic obstacle detection. Simulations are performed to verify that our approach outperforms other conventional navigation methods in terms of obstacle avoidance and reaching the target accurately.

Top-View-Based Guidance for Blind People Using Directional Ellipse Model

The guidance system proposed in this paper aims to complement the white cane by monitoring road conditions in a medium range for blind pedestrians in real time. The system prototype employs only one webcam fixed at the waist of user. One of the main difficulties of using a single camera in outdoor obstacle detection is the discrimination of obstacles from a complex background. To solve this problem, this paper re-formulates top-view mapping as an inhomogeneous re-sampling process, so that background edges are sub-sampled while obstacle edges are oversampled in the top-view domain. Morphology filters are then used to enhance obstacle edges as edge-blobs, which are further represented using a directional ellipse as a new model for obstacle classification. Based on the identified obstacles, safe walking area is estimated by tracking a polar edge-blob histogram. To transfer the information obtained from image domain to language domain, this paper proposes a verbal message generation scheme based on fuzzy logic. The efficiency of the system is confirmed by testing the system with visually impaired people on outdoor pedestrian paths.

An improvement to the SIFT descriptor for image representation and matching

Constructing proper descriptors for interest points in images is a critical aspect for local features related tasks in computer vision and pattern recognition. Although the SIFT descriptor has been proven to perform better than the other existing local descriptors, it does not gain sufficient distinctiveness and robustness in image match especially in the case of affine and mirror transformations, in which many mismatches could occur. This paper presents an improvement to the SIFT descriptor for image matching and retrieval. The framework of the proposed descriptor consists of the following steps: normalizing elliptical neighboring region, transforming to affine scale-space, improving the SIFT descriptor with polar histogram orientation bin, as well as integrating the mirror reflection invariant. A comparative evaluation of different descriptors is carried out showing that the present approach provides better results than the existing methods.

A Contour Representation and Retrieval Algorithm based on Polar-histogram

This paper presents a novel method for contour-based shape retrieval. First, they come from contour points which can represent the shape very well as the object contour points got by contour tracing and denoising. Corner points, sampled points and two vector sets are obtained by leading vectors from each point to the contour centroid point. Then, the polar histogram of directions and distance in the vector sets are used to describe the shape. Finally, it combines the similarity measurement criterion of two contours with the polar histogram of the two feature points. The experiment result shows that our algorithm is efficient and is with better performance in the robustness to the scaling, rotation and translation, compared with the traditional algorithms.

영상디지털도어록용 단일 사람 검출 알고리즘 구현

영상디지털도어록(Video digital door lock, VDDL) 시스템은 문으로 출입하는 사람을 검출하고 사람 영상을 획득한다. 도어록 설계 시 고려할 사항은 배터리 기반으로 동작하므로 속도가 빠른 사람 검출 알고리즘을 적용하여 전류소모를 최소화해야 한다. 그리고 도어록은 고정된 카메라에서 영상을 촬영하므로 배경영상을 이용한 사람 검출이 높은 신뢰성을 얻을 수 있다. 본 논문에서는 이러한 요구조건에 충족하며 VDDL에 적합한 단일 사람검출 알고리즘을 다루고 있는데, 획득한 영상에서 이동하는 물체를 감지하고 영상처리를 통해 물체가 사람인지를 판별한다. 제안된 영상처리 알고리즘은 두 단계로 이루어져 있다. 첫째, 배경영상과 피부색 정보를 통해 사람 이미지 영역을 구한다. 둘째, 인체비례 정보를 기반으로 폴라 히스토그램을 이용하여 사람 유무를 판단한다. 개발된 알고리즘은 도어록에 설치하고 실험을 통해 성능을 확인하였다. Video digital door lock(VDDL) system detects people who access to the door and acquires the human image. Design considerations is that current consumption must be minimized by applying fast human detection algorithm because of battery-based operation. Since the digital door lock takes an image through a fixed camera, detection of a person based on background image leads to high degree of reliability. This paper deals with a single human detection algorithm suitable for VDDL with fulfilling these requirements such that it detects a moving object in an image, then identifies whether the object is a person or not using image processing. The proposed image processing algorithm consists of two steps: Firstly, it detects the human image region using both background image and skin color information. Secondly, it identifies the person using polar histogram based on proportional information of human body. Proposed algorithm is implemented in VDDL and is verified the performance through experiments.