View Angle Estimation Research Articles

Available Viewing Angle Estimation with Semantic Segment Distribution at Unsignalized Intersections

Available Viewing Angle Estimation with Semantic Segment Distribution at Unsignalized Intersections

Computed tomography with view angle estimation using uncertainty quantification

We consider computed tomography (CT) with uncertain measurement geometry, with a focus on the case where the view angles are uncertain and where estimation of these angles improves the reconstruction. We propose a new reconstruction model and a corresponding algorithm that has an additional view-angle estimation component, allowing us to determine the angles solely from the measured CT data. A key component of our approach is that we quantify the uncertainty of the view angles via a model-discrepancy formulation, allowing us to take the uncertainty into account in the image reconstruction. This approach generalizes in a straightforward way to other cases of uncertain geometry. Our method is computationally efficient since we can utilize a block-structure of the computational problem for estimation of both the CT image and the view angles under the assumption that the view angles are independent. The joint image/angle reconstruction problem is non-convex which leads to difficulties in recently proposed algorithms, and we demonstrate numerically that our method seems to avoid these difficulties. Simulations show that our method, with a total variation prior that reflects our phantoms, is able to achieve reconstructions whose quality is similar to ones obtained with the correct view angles (the ideal scenario).

Towards More Accurate Contactless Fingerprint Minutiae Extraction and Pose-Invariant Matching

Contactless fingerprint identification offers significantly higher user convenience, hygiene and has attracted increasing attention for the deployments. However, the presentation of fingers towards the contactless fingerprint sensors is hard to control and often results in unwanted pose changes that significantly degrade the contactless fingerprint matching accuracy. In order to address such problems and improve the fingerprint matching accuracy, this paper proposes a more precise minutiae extraction and pose-compensation approach. As compared with the conventional minutiae extraction approaches, our deep neural network-based approach does not require any image enhancement and is robust to spurious minutiae. All the minutiae extracted from our network are subjected to a three stage pose compensation framework: a) view angle estimation based on the location of core point, b) ellipsoid model formulation which simulates and compensate finger pose, c) intersection area estimation and alignment between different view angles. The proposed ellipsoid model is adaptive to both the silhouette of 2D contactless fingerprint image and the estimated view angle. The corresponding area between the different view angles can be theoretically estimated using this model and incorporated to align two contactless fingerprints for achieving superior matching accuracy. Our reproducible experimental results presented in this paper using public databases, and a database acquired during this work, validate the effectiveness of the proposed framework over the commercial software and earlier methods.

Hierarchical Sliding Slice Regression for Vehicle Viewing Angle Estimation

We propose a novel hierarchical sliding slice regression which in a coarse-to-fine manner represents global circular target space with a number of ordinally localized and overlapping subspaces. Our method is particularly suitable for visual regression problems where the regression target is circular (e.g., car viewing angle) and visual similarity inconsistent over the target space (e.g., repetitive appearance). A good application example is the camera-based car viewing angle estimation problem, where visual similarity of different views is highly inconsistent—front and back views and left and right side views are pair-wise similar, but appear at the far ends of the circular view angle space. In practice, the problem is even more complicated due to large visual variation of objects (e.g., different car models). We perform extensive experiments on the Lausanne Federal of Institute of Technology Multi-view Car and KITTI Data Sets as well as the Technische Universitat Darmstadt Multi-view Pedestrians Data Set and achieve superior performance as compared to the state-of-the-art algorithms.

A 27 mW Reconfigurable Marker-Less Logarithmic Camera Pose Estimation Engine for Mobile Augmented Reality Processor

A marker-less camera pose estimation engine (CPEE) with reconfigurable logarithmic SIMD processor is proposed for the view angle estimation used in mobile augmented reality (AR) applications. Compared to previous marker-based approach, marker-less camera pose estimation can overlay virtual images directly on natural world without the help of markers. However, they require 150x larger computing costs and large power consuming floating-point operations where such requirement severely restricts real-time operations and low-power implementations in mobile platform, respectively. To overcome the gap in computational costs between marker-based and marker-less method, speculative execution (SE) and reconfigurable data-arrangement layer (RDL) are proposed to reduce computing time by 17% and 27%, respectively. For low-power implementation of floating-point units, logarithmic processing element (LPE) is designed to reduce overall power consumption by 18%. The proposed marker-less CPEE is fabricated in 65 nm Logic CMOS technology, and successfully realizes real-time marker-less camera pose estimation with only 27 mW power consumption.

Three-dimensional simulation method of fish-eye lens distortion for a vehicle backup rear-view camera.

Recently, various cameras have been embedded in vehicles for driver safety and convenience. In this context, a backup rear-view camera has attracted increasing attention in helping drivers' parking convenience. Preinstallation of a rear-view camera requires the calibration of a wide-angle lens, such as a fish-eye lens, and the registration of guidelines to the three-dimensional (3D) scene. The proposed method provides a novel simulation method for the optical distortion of a wide-angle lens in a vehicle rear-view camera. The proposed method consists of three steps: (i) generation of the 3D virtual space, (ii) field number-based viewing angle estimation, and (iii) distorted image generation in the 3D space. The major contribution of this work is the lens specification-based simulation of 3D distortion for accurate and efficient preinstallation of vehicle rear-view cameras. The proposed simulation method can also be used to design a general optical imaging system for intelligent surveillance and medical imaging.

Estimation of view angles for gait using a robust regression method

The performance of most gait recognition methods would drop down if the viewpoint of test data is different from the viewpoint of training data. In this paper, we present an idea of estimating the view angle of a test sample in advance so as to compare it with the corresponding training samples with the same or approximate viewpoint. In order to obtain reliable estimation results, the view-sensitive features should be extracted. We propose a novel and effective feature extraction method to characterize the silhouettes from different views. The discrimination power of this representation is also verified through experiments. Afterwards, the robust regression method is employed to estimate the viewpoint of gait. The view angles of test samples from BUAA-IRIP Gait Database are estimated with the regression models learned from CASIA Gait Database. Compared with the ground truth angles, such estimation is satisfactory with a small error level. Therefore, it can provide necessary help for gait application systems when the view angles of test data are uncertain. This point is verified experimentally through integrating the view angle estimation into a gait based gender classification system.

Learning a Family of Detectors via Multiplicative Kernels

Object detection is challenging when the object class exhibits large within-class variations. In this work, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly learned in a multiplicative form of two kernel functions. Model training is accomplished via standard SVM learning. When the foreground object masks are provided in training, the detectors can also produce object segmentations. A tracking-by-detection framework to recover foreground state in video sequences is also proposed with our model. The advantages of our method are demonstrated on tasks of object detection, view angle estimation, and tracking. Our approach compares favorably to existing methods on hand and vehicle detection tasks. Quantitative tracking results are given on sequences of moving vehicles and human faces.