Embedded ARM9 Image Acquisition System Based on CMOS Image Sensor

This article is based on CMOS image sensor which is commonly used in image acquisition. It applies 32-bit ARM9 microprocessor S3C2410A as the CPU to control other function modules and designs embedded Arm9 image acquisition system for the realization of machine vision. The main function modules are SDRAM memory cell, image acquisition unit and Ethernet transmission module, UART serial port communication module, Flash module, power supply module, etc. Compared with machine vision systems of the traditional "image acquisition card - PC - terminal control device" model, the image acquisition system has advantages of small volume, low cost, low power consumption, strong real-time performance, etc. And it can be used for practical application in video image monitoring, automatic detection, medical and military detection and so on. In a word, it has a good application prospect

Comparative Analysis of Multiplicative and Additive Noise Based Automated Regularizations in Non-Linear Diffusion Image Reconstruction

Multiplicative and additive noises are often introduced in image signals during the image acquisition process and result into degradation of image features. The work done by Perona and Malik in 1990 and its modified versions revolutionized the way through which noises or speckles are removed. The Perona-Malik model requires tuning of the regularization parameter to control and prevent staircase artifacts in restored images. The current manual tuning is a challenging and time consuming practice when a long queue of images is registered for processing. Attempt to automate the regularization parameter appeared in Perona-Malik model with self-adjusting shape-defining constant. Although both multiplicative and additive noise based automated regularizations were presented, the paper stayed silent on matters concerning the automation method that fits with speckle reduction. This paper therefore, presents a comparative analysis of additive and multiplicative noise based automated regularizations. Simulation results and paired samples T-tests reveal that the multiplicative noise based automation outperforms the additive noise based automation for small speckle variances. However, the two automation methods do not significantly differ when large speckle variances are assumed

UAV Placement for Real-time Video Acquisition: A Tradeoff between Resolution and Delay

Recently, UAVs endowed with high mobility, low cost, and remote control have promoted the development of UAV-assisted real-time video/image acquisition applications, which have a high demand for both transmission rate and image resolution. However, in conventional vertical photography model, the UAV should fly to the top of ground targets (GTs) to capture images, thus enlarge the transmission delay. In this paper, we propose an oblique photography model, which allows the UAV to capture images of GTs from a far distance while still satisfying the predetermined resolution requirement. Based on the proposed oblique photography model, we further study the UAV placement problem in the cellular-connected UAV-assisted image acquisition system, which aims at minimizing the data transmission delay under the condition of satisfying the predetermined image resolution requirement. Firstly, the proposed scheme is first formulated as an intractable non-convex optimization problem. Then, the original problem is simplified to obtain a tractable suboptimal solution with the help of the block coordinate descent and the successive convex approximation techniques. Finally, the numerical results are presented to show the effectiveness of the proposed scheme. The numerical results have shown that the proposed scheme can largely save the transmission time as compared to the conventional vertical photography model.Comment: submitted to ieee for possible publication. arXiv admin note: text overlap with arXiv:2006.14438 by other author

PANORAMA IMAGE SETS FOR TERRESTRIAL PHOTOGRAMMETRIC SURVEYS

High resolution 3D models produced from photographs acquired with consumer-grade cameras are becoming increasingly common in the fields of geosciences. However, the quality of an image-based 3D model depends on the planning of the photogrammetric surveys. This means that the geometric configuration of the multi-view camera network and the control data have to be designed in accordance with the required accuracy, resolution and completeness. From a practical application point of view, a proper planning (of both photos and control data) of the photogrammetric survey especially for terrestrial acquisition, is not always ensured due to limited accessibility of the target object and the presence of occlusions. To solve these problems, we propose a different image acquisition strategy and we test different geo-referencing scenarios to deal with the practical issues of a terrestrial photogrammetric survey. The proposed photogrammetric survey procedure is based on the acquisition of a sequence of images in panorama mode by rotating the camera on a standard tripod. The offset of the pivot point from the projection center prevents the stitching of these images into a panorama. We demonstrate how to still take advantage of this capturing mode. The geo-referencing investigation consists of testing the use of directly observed coordinates of the camera positions, different ground control point (GCP) configurations, and GCPs with different accuracies, i.e. artificial targets vs. natural features. Images of the test field in a low-slope hill were acquired from the ground using an SLR camera. To validate the photogrammetric results a terrestrial laser scanner survey is used as benchmark

IV-FMC: an automated vision based part modeling and reconstruction system for flexible manufacturing cells

The use of computer vision system in manufacturing industry can eliminate the visual faults due to the limitation of human vision and increase productivity. The aim of the current study is to develop an automated vision system (IV-FMC) to reconstruct manufacturing parts in three-dimensional (3D) model. In the designed system, laser stripes are projected onto an object to be scanned. A charge-coupled device (CCD) camera captures the two dimensional (2D) image from the reflected stripes. Based of the principle of optical triangulation, the distance between the object and the camera is calculated in which the third dimension of the image is obtained. These processes iterate each time the object is rotated in different angles, letting the system to capture the whole view of the object being scanned. A 3D model of the object is then reconstructed by merging multiple range images obtained from the range scanning. A PC-based data acquisition board is designed to control the switching of the laser module. The reconstruction process is automated to form a single 3D surface model of the object being scanned

Analysis of the inspection of mechanical parts using dense range data

More than ever, efficiency and quality are key words in modern industry. This situation enhances the importance of quality control and creates a great demand for cheap and reliable automatic inspection systems. Taking into account these facts and the demand for systems able to inspect the final shape of machined parts, we decided to investigate the viability of automatic model-based inspection of mechanical parts using the dense range data produced by laser stripers. Given a part to be inspected and a corresponding model of the part stored in the model data base, the first step of inspecting the part is the acquisition of data corresponding to the part, in our case this means the acquisition of a range image of it. In order to be able to compare the part image and its stored model, it is necessary to align the model with the range image of the part. This process, called registration, corresponds to finding the rigid transformation that superposes model and image. After the image and model are registered, the actual inspection uses the range image to verify if all the features predicted in the model are present and have the right pose and dimensions. Therefore, besides the acquisition of range images, the inspection of machined parts involves three main issues: modelling, registration and inspection diagnosis. The application, for inspection purposes, of the main representational schemes for modelling solid objects is discussed and it is suggested the use of EDT models (see [Zeid 91]). A particular implementation of EDT models is presented. A novel approach for the verification of tolerances during the inspection is proposed. The approach allows not only the inspection of the most common tolerances described in the tolerancing standards, but also the inspection of tolerances defined according to Requicha's theory of tolerancing (see [Requicha 83]). A model of the sensitivity and reliability of the inspection process based on the modelling of the errors during the inspection process is also proposed. The importance of the accuracy of the registration in different inspections tasks is discussed. A modified version of the ICP algorithm (see [Besl &; McKay 92]) for the registration of sculptured surfaces is proposed. The maximum accuracy of the ICP algorithm, as a function of the sensor errors and the number of matched points, is determined. A novel method for the measurement and reconstruction of waviness errors on sculp¬ tured surfaces is proposed. The method makes use of the 2D Discrete Fourier Transform for the detection and reconstruction of the waviness error. A model of the sensitivity and reliability of the method is proposed. The application of the methods proposed is illustrated using synthetic and real range image

Magnetic Resonance Image segmentation using Pulse Coupled Neural Networks

The Pulse Couple Neural Network (PCNN) was developed by Eckhorn to model the observed synchronization of neural assemblies in the visual cortex of small mammals such as a cat. In this dissertation, three novel PCNN based automatic segmentation algorithms were developed to segment Magnetic Resonance Imaging (MRI) data: (a) PCNN image \u27signature\u27 based single region cropping; (b) PCNN - Kittler Illingworth minimum error thresholding and (c) PCNN -Gaussian Mixture Model - Expectation Maximization (GMM-EM) based multiple material segmentation. Among other control tests, the proposed algorithms were tested on three T2 weighted acquisition configurations comprising a total of 42 rat brain volumes, 20 T1 weighted MR human brain volumes from Harvard\u27s Internet Brain Segmentation Repository and 5 human MR breast volumes. The results were compared against manually segmented gold standards, Brain Extraction Tool (BET) V2.1 results, published results and single threshold methods. The Jaccard similarity index was used for numerical evaluation of the proposed algorithms. Our quantitative results demonstrate conclusively that PCNN based multiple material segmentation strategies can approach a human eye\u27s intensity delineation capability in grayscale image segmentation tasks

Combined system identification and robust control of a gimbal platform

Gimbaled imaging systems require very high performance inertial stabilization loops to achieve clear image acquisition, precise pointing, and tracking performance. Therefore, higher bandwidths become essential to meet recent increased performance demands. However, such systems often posses flexible dynamics around target bandwidth and time delay of gyroscope sensors which put certain limit to achievable bandwidths. For inertial stabilization loops, widely used design techniques have difficulty in achieving large bandwidth and satisfying required robustness simultaneously. Clearly, high performance control design hinges on accurate control-relevant model set. For that reason, combined system identification and robust control method is preferred. In the system identification step, accurate nominal model is obtained, which is suitable for subsequent robust control synthesis. Model validation based uncertainty modeling procedure constructs the robust-control-relevant uncertain model set, which facilitates the high performance controller design. Later, with skewed-mu synthesis, controller is designed which satisfies large bandwidth and robustness requirements. Finally, the experimental results show that significant performance improvement is achieved compared to common manual loop shaping methods. In addition, increased performance demands for new imaging systems are fulfilled

Towards real-time 6D pose estimation of objects in single-view cone-beam X-ray

Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images. 6D Object pose estimation based on deep learning models for X-ray images often use custom architectures that employ extensive CAD models and simulated data for training purposes. Recent RGB-based methods opt to solve pose estimation problems using small datasets, making them more attractive for the X-ray domain where medical data is scarcely available. We refine an existing RGB-based model (SingleShotPose) to estimate the 6D pose of a marked cube from grayscale X-ray images by creating a generic solution trained on only real X-ray data and adjusted for X-ray acquisition geometry. The model regresses 2D control points and calculates the pose through 2D/3D correspondences using Perspective-n-Point(PnP), allowing a single trained model to be used across all supporting cone-beam-based X-ray geometries. Since modern X-ray systems continuously adjust acquisition parameters during a procedure, it is essential for such a pose estimation network to consider these parameters in order to be deployed successfully and find a real use case. With a 5-cm/5-degree accuracy of 93% and an average 3D rotation error of 2.2 degrees, the results of the proposed approach are comparable with state-of-the-art alternatives, while requiring significantly less real training examples and being applicable in real-time applications.Comment: Published at SPIE Medical Imaging 202