Spatial layout and surface reconstruction from omnidirectional images

We present a Histogram of Oriented Gradient (HoG) based two-directional Dynamic Time Warping (DTW) matching method for handwritten word spotting. Firstly, we extract HoG descriptors from each cell in the normalized images. Then we connect the HoG descriptors in the same column and get a sequence of feature vectors. We do the same operation for the HoG descriptors in the same row. We then apply the two-directional DTW method to calculate the distance between the feature vectors sequences extracted from the query word and the candidate one. The experimental results show that the two-directional DTW is more robust to word deformation than the traditional DTW. And the local features such as HoG, LBP and SIFT combined with the two-directional DTW method outperform the method using the local feature descriptors directly. The HoG based two-directional DTW get the highest mean average precision on both the George Washington dataset and the CASIA-HWDB 2.1 dataset.

This paper describes new vision-based registration methods utilizing not only cameras on a user's head-mounted display but also a bird's-eye view camera that observes the user from an objective viewpoint. Two new methods, the line constraint method (LCM) and global error minimization method (GEM), are proposed. The former method reduces the number of unknown parameters concerning the user's viewpoint by restricting it to be on the line of sight from the bird's-eye view. The other method minimizes the sum of errors, which is the sum of the distance between the fiducials on the view and the calculated positions of them based on the current viewing parameters, for both the user's view and the bird's-eye view. The methods proposed here reduce the number of points that should be observed from the user's viewpoint for registration, thus improving the stability. In addition to theoretical discussions, this paper demonstrates the effectiveness of our methods by experiments in comparison with methods that use only a user's view camera or a bird's-eye view camera.

Face recognition is widely used in computer vision and in many other biometric applications where security is a major concern. The most common problem in recognizing a face arises due to pose variations, different illumination conditions and so on. The main focus of this paper is to recognize whether a given face input corresponds to a registered person in the database. Face recognition is done using Histogram of Oriented Gradients (HOG) technique in AT & T database with an inclusion of a real time subject to evaluate the performance of the algorithm. The feature vectors generated by HOG descriptor are used to train Support Vector Machines (SVM) and results are verified against a given test input. The proposed method checks whether a test image in different pose and lighting conditions is matched correctly with trained images of the facial database. The results of the proposed approach show minimal false positives and improved detection accuracy.

Maximum Curvature Method (MCM) is one of the promising methods for finger vein verification. MCM scans the curvature of the vein image profiles within a finger for feature extraction. However, the quality of the image can be poor due to variations in illumination and sensor conditions. Furthermore, traditional MCM matching of the vein pattern requires extensive processing time. To address these limitations, we propose an integrated Enhanced Maximum Curvature (EMC) method with Histogram of Oriented Gradient (HOG) descriptor for finger vein verification. Unlike MCM, EMC incorporates an enhancement mechanism to extract small vein delineation that is hardly visible in the extracted vein patterns. Next, HOG is applied instead of image binarization to convert a two-dimensional vein image into a one-dimensional feature vector for efficient matching. The HOG descriptor is able to characterize the local spatial representation of a finger vein by capturing the gradient information effectively. The proposed method is evaluated based on two datasets namely the PKU Finger Vein Database (V4) and SDUMLA-HMT finger vein database. Experiments show promising verification results with equal error rates as low as 0.33 % for DB1 and 0.14 % for DB2 respectively, when EMC+HOG+SVM is applied.

Face Recognition plays a very important role in numerous occasions based on visual security in recent days. The current methods of face recognition are to extract the different features of different faces to distinguish from others, so feature extraction has become a vital step in face recognition. For this reason, this paper presents a new fusion of local difference binary (LDB) and histogram of oriented gradients (HOG) for face recognition. We use LDB descriptor to extract the local pattern features of a face image. At the same time, the edge features of the original image are extracted by using HOG descriptor. The proposed new fusion of features improves the shortcomings of the low accuracy and avoids the problems that the dimension of the general fusion of features is too high. The experimental results on ORL and Yale face database verify the validity of the proposed fusion of features.

The aim of our work is to investigate the use of two powerful feature descriptor known as Zernike moments and histogram of oriented gradient (HOG) for facial images extracted from a video sequence uttered ten times by four speakers where MLP neural network classifier is used to identify the correct speaker. We've demonstrated good recognition rate obtained for both Zernike moments and HOG descriptors.

Semi-autonomous operation of intelligent vehicles may require that such platforms maintain a basic situational awareness with respect to people, other vehicles and their intent. These vehicles should be able to operate safely among people and other vehicles, and be able to perceive threats and respond accordingly. A key requirement is the ability to detect people and vehicles from a moving platform. We have developed one such algorithm using video cameras mounted on the vehicle. Our person detection algorithms model the shape and appearance of the person instead of modeling the background. This algorithm uses histogram of oriented gradients (HOG), which model shape and appearance using image edge histograms. These HOG descriptors are computed on an exhaustive set of image windows, which are then classified as person/non-person using a support vector machine classifier. The image windows are computed using camera calibration, which provides approximate size of people with respect to their location in the imagery. The algorithm is flexible and has been trained for different domains such as urban, rural and wooded scenes. We have designed a sensor platform that can be mounted on a moving vehicle to collect video data of pedestrians. Using manually annotated ground-truth data we have evaluated the person detection algorithm in terms of true positive and false positive rates. This paper provides a detailed overview of the algorithm, describes the experiments conducted and reports on algorithmic performance.

The problem of detecting objects in images is solved by training the classifier by the descriptors constructed based on the local Radon transform of the gradient field of the image. The space of the Radon transform is considered as the Hough space accumulator in which projections are constructed. The set of local projections forms a descriptor of the region related to the object that is a generalization of the known histogram of oriented gradients (HOG) descriptor. The issues of the effect of the approximation of the Radon transform contribution function, the form of local normalization, and the number of directions in the projection histograms on the results of detecting pedestrians are investigated. The results produced by the proposed descriptor are compared with the results obtained using the HOG descriptor and convolutional neural networks (CNN) based on the ResNext and MobileNet architectures on the INRIA and CityScapes databases.

The Medieval Church of St. Andrews. Edited by David McRoberts. Pp. xii + 178 + 24 illustrations + plan and a facsimile of the 16th-century ‘bird's eye view’ of St. Andrews. Glasgow: Burns, 1976. £7. - Volume 29 Issue 3

Our research objective is construction of 3D map and development of its practical applications. As an application, the authors had proposed 3D control interface for an operator who controls remote control robot. It is well know that an operator can control a robot easily using bird's-eye view. Using 3D control interface, an operator can control the robot at bird's-eye view. The bird's-eye view is constructed from 3D map and robot model. The operator can recognize relationship between the robot and the environment from 3D map and robot model following real robot motion. In this paper, we will describe 3D control interface and its implementation for HELIOS carrier. We will show an experimental result of 3D control interface. From result, we confirmed the effectiveness of the proposed method.

Robotics combines a lot of different domains with sophisticated challenges such as computer vision, motion control and search algorithms. Search algorithms can be applied to calculate movements. The A∗ algorithm is a well-known and proved search algorithm to find a path within a graph. This paper presents an extended A∗ algorithm that is optimized for robot navigation using a bird's eye view as a map that is dynamically generated by image stitching. The scenario is a robot that moves to a target in an environment containing obstacles. The robot is controlled by a Xilinx Zynq platform that contains an ARM processor and an FPGA. In order to exploit the flexibility of such an architecture, the FPGA is used to execute the most compute-intensive task of the extended A∗ algorithm. This task is responsible for sorting the accessible nodes in the graph. Several environments with different complexity levels are used to evaluate the extended A∗ algorithm. The environment is captured by a Kinect sensor located directly on the robot. In order to dewarp the robot's view, the frames are transformed to a bird's eye view. In addition, a wider viewing range is achieved by image stitching. The evaluation of the extended A∗ algorithm shows a significant improvement in terms of memory utilization. Accordingly, this algorithm is especially practicable for embedded systems since they have often only limited memory resources. Moreover, the overall execution time for several use cases is reduced up to a speed-up of 2.88x.

Bird's Eye View (BEV) representation learning has greatly improved target detection and tracking in complex environments and autonomous systems, thanks to a multi-view aggregation strategy that enhances spatial awareness, reduces occlusions, and ensures continuous surveillance of targets. This paper presents an Enhanced Global Context Fusion Network for multi-target multi-camera tracking (MTMC), addressing the critical challenge of spatial association across views in large-scale surveillance systems, unlike conventional approaches that directly aggregate Bird's Eye View (BEV) features, our method hierarchically refines individual BEV embedding by integrating global context, generating bootstrap features that better preserve spatial relationships and reduce information loss. In order to improve the problem of poor robustness to real-world issues that may be caused by camera malfunctions, we introduce a data augmentation strategy that replaces the input of lost camera signals with black images during training. The results of extensive experiments on the Wildtrack dataset demonstrate the state-of-the-art performance of our approach, reaching 92.0% for MODA and 94.1% for IDF1, outperforming existing techniques with only a negligible increase in computational cost. These results validate the effectiveness of our approach in terms of improved BEV feature representation and system stability, providing a reliable solution for reliable MTMC detection and tracking in complex environments.

By matching roadway images in encountered scene with bird's eye view, the scope of humans' perception is expanded to satellite-roadway-vehicle network. Based on the geometric consistency of the satellite images with as-is local terrain, in this paper, a computa- tional mechanism is introduced for generation and adaptation of a roadway model transferable through the network. First, the chromatic complexity of the roadway area is represented as a palette of saliency colors via fractal sampling of the scene image. Next, the palette is adapted to the associated area in the bird's eye view. Finally, the palette is transferred to the bird's eye view for anticipatively following the roadway pattern. Experimental results demonstrate that the transferable model can be applied to the extension of the roadway pattern prior to physical access.

This work offers a method for the visual-based navigation of a mobile robot in indoor environments, using a single omni-directional camera. The geometry of the catadioptric sensor and the method used to obtain a bird's eye (orthographic) view of the ground plane are presented. This picture evocatively abridges the solution to steering problems, by eliminating any perspective effects. The nature of each navigation task is taken hooked on account when conniving the compulsory navigation skills and environmental representations. Topological Navigation is cast-off for itinerant long distances plus does not require knowledge of the exact location of the robot but rather, a qualitative position happening the topological map. For indoor robots in structured environments, we consume owed separately with the cases of geometrical and topological models of interplanetary. For shapeless environments, we consume discussed the cases of navigation through optical activities, using methods from the appearance-based paradigm, and by recognition of specific substances in the environment. The navigation process cartels appearance based approaches and visual serving upon some environmental features. Graphic Path Succeeding is obligatory for native, actual precise navigation, for e.g. door traversal, docking. The robot is controlled to follow a pre-specified trail accurately, by tracking visual milestones in bird's eye views of the ground plane. By clearly separating the nature of these triangulation tasks, a simple besides yet powerful navigation scheme is obtained.

Proposes a method for the visual-based navigation of a mobile robot in indoor environments, using a single omnidirectional (catadioptric) camera. The geometry of the catadioptric sensor and the method used to obtain a bird's eye (orthographic) view of the ground plane are presented. This representation significantly simplifies the solution to navigation problems, by eliminating any perspective effects. The nature of each navigation task is taken into account when designing the required navigation skills and environmental representations. We propose two main navigation modalities: topological navigation and visual path following. Topological navigation is used for traveling long distances and does not require knowledge of the exact position of the robot but rather, a qualitative position on the topological map. The navigation process combines appearance based methods and visual servoing upon some environmental features. Visual path following is required for local, very precise navigation, e.g., door traversal, docking. The robot is controlled to follow a prespecified path accurately, by tracking visual landmarks in bird's eye views of the ground plane. By clearly separating the nature of these navigation tasks, a simple and yet powerful navigation system is obtained.