769 research outputs found
Structured Light-Based 3D Reconstruction System for Plants.
Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance
Edge adaptive filtering of depth maps for mobile devices
Abstract. Mobile phone cameras have an almost unlimited depth of field, and therefore the images captured with them have wide areas in focus. When the depth of field is digitally manipulated through image processing, accurate perception of depth in a captured scene is important.
Capturing depth data requires advanced imaging methods. In case a stereo lens system is used, depth information is calculated from the disparities between stereo frames. The resulting depth map is often noisy or doesn’t have information for every pixel. Therefore it has to be filtered before it is used for emphasizing depth. Edges must be taken into account in this process to create natural-looking shallow depth of field images.
In this study five filtering methods are compared with each other. The main focus is the Fast Bilateral Solver, because of its novelty and high reported quality. Mobile imaging requires fast filtering in uncontrolled environments, so optimizing the processing time of the filters is essential.
In the evaluations the depth maps are filtered, and the quality and the speed is determined for every method. The results show that the Fast Bilateral Solver filters the depth maps well, and can handle noisy depth maps better than the other evaluated methods. However, in mobile imaging it is slow and needs further optimization.Reunatietoinen syvyyskarttojen suodatus mobiililaitteilla. Tiivistelmä. Matkapuhelimien kameroissa on lähes rajoittamaton syväterävyysalue, ja siksi niillä otetuissa kuvissa laajat alueet näkyvät tarkennettuina. Digitaalisessa syvyysterävyysalueen muokkauksessa tarvitaan luotettava syvyystieto.
Syvyysdatan hankinta vaatii edistyneitä kuvausmenetelmiä. Käytettäessä stereokameroita syvyystieto lasketaan kuvien välisistä dispariteeteista. Tuloksena syntyvä syvyyskartta on usein kohinainen, tai se ei sisällä syvyystietoa joka pikselille. Tästä syystä se on suodatettava ennen käyttöä syvyyden korostamiseen. Tässä prosessissa reunat ovat otettava huomioon, jotta saadaan luotua luonnollisen näköisiä kapean syväterävyysalueen kuvia.
Tässä tutkimuksessa verrataan viittä suodatusmenetelmää keskenään. Eniten keskitytään nopeaan bilateraaliseen ratkaisijaan, johtuen sen uutuudesta ja korkeasta tuloksen laadusta. Mobiililaitteella kuvantamisen vaatimuksena on nopea suodatus hallitsemattomissa olosuhteissa, joten suodattimien prosessointiajan optimointi on erittäin tärkeää.
Vertailuissa syvyyskuvat suodatetaan ja suodatuksen laatu ja nopeus mitataan jokaiselle menetelmälle. Tulokset osoittavat, että nopea bilateraalinen ratkaisija suodattaa syvyyskarttoja hyvin ja osaa käsitellä kohinaisia syvyyskarttoja paremmin kuin muut tarkastellut menetelmät. Mobiilikuvantamiseen se on kuitenkin hidas ja tarvitsee pidemmälle menevää optimointia
Optimization techniques for computationally expensive rendering algorithms
Realistic rendering in computer graphics simulates the interactions of light and surfaces. While many accurate models for surface reflection and lighting, including solid surfaces and participating media have been described; most of them rely on intensive computation. Common practices such as adding constraints and assumptions can increase performance. However, they may compromise the quality of the resulting images or the variety of phenomena that can be accurately represented. In this thesis, we will focus on rendering methods that require high amounts of computational resources. Our intention is to consider several conceptually different approaches capable of reducing these requirements with only limited implications in the quality of the results. The first part of this work will study rendering of time-ÂÂżvarying participating media. Examples of this type of matter are smoke, optically thick gases and any material that, unlike the vacuum, scatters and absorbs the light that travels through it. We will focus on a subset of algorithms that approximate realistic illumination using images of real world scenes. Starting from the traditional ray marching algorithm, we will suggest and implement different optimizations that will allow performing the computation at interactive frame rates. This thesis will also analyze two different aspects of the generation of anti-ÂÂżaliased images. One targeted to the rendering of screen-ÂÂżspace anti-ÂÂżaliased images and the reduction of the artifacts generated in rasterized lines and edges. We expect to describe an implementation that, working as a post process, it is efficient enough to be added to existing rendering pipelines with reduced performance impact. A third method will take advantage of the limitations of the human visual system (HVS) to reduce the resources required to render temporally antialiased images. While film and digital cameras naturally produce motion blur, rendering pipelines need to explicitly simulate it. This process is known to be one of the most important burdens for every rendering pipeline. Motivated by this, we plan to run a series of psychophysical experiments targeted at identifying groups of motion-ÂÂżblurred images that are perceptually equivalent. A possible outcome is the proposal of criteria that may lead to reductions of the rendering budgets
An LED-Based Structured Illumination Microscope Using A Digital Micromirror Device And GPU Accelerated Image Reconstruction
When combined with computational approaches, fluorescence imaging becomes one of the
most powerful tools in biomedical research. It is possible to achieve resolution figures
beyond the diffraction limit, and improve the performance and flexibility of high-resolution
imaging systems with techniques such as structured illumination microscopy (SIM) reconstruction. In this study, the hardware and software implementation of an LED-based superresolution imaging system using SIM employing GPU accelerated parallel image reconstruction is presented. The sample is illuminated with two-dimensional sinusoidal patterns
with various orientations and lateral phase shifts generated using a digital micromirror
device (DMD). SIM reconstruction is carried out in frequency space using parallel CUDA
kernel functions. Furthermore, a general purpose toolbox for the parallel image reconstruction algorithm and an infrastructure that allows all users to perform parallel operations on
images without developing any CUDA kernel code is presented. The developed image
reconstruction algorithm was run separately on a CPU and a GPU. Two different SIM reconstruction algorithms have been developed for the CPU as mono-thread CPU algorithm and
multi-thread OpenMP CPU algorithm. SIM reconstruction of 1024 Ă— 1024 px images was
achieved in 1.49 s using GPU computation, indicating an enhancement by *28 and *20 in
computation time when compared with mono-thread CPU computation and multi-thread
OpenMP CPU computation, respectively
Large-Scale Light Field Capture and Reconstruction
This thesis discusses approaches and techniques to convert Sparsely-Sampled Light Fields (SSLFs) into Densely-Sampled Light Fields (DSLFs), which can be used for visualization on 3DTV and Virtual Reality (VR) devices. Exemplarily, a movable 1D large-scale light field acquisition system for capturing SSLFs in real-world environments is evaluated. This system consists of 24 sparsely placed RGB cameras and two Kinect V2 sensors. The real-world SSLF data captured with this setup can be leveraged to reconstruct real-world DSLFs. To this end, three challenging problems require to be solved for this system: (i) how to estimate the rigid transformation from the coordinate system of a Kinect V2 to the coordinate system of an RGB camera; (ii) how to register the two Kinect V2 sensors with a large displacement; (iii) how to reconstruct a DSLF from a SSLF with moderate and large disparity ranges. To overcome these three challenges, we propose: (i) a novel self-calibration method, which takes advantage of the geometric constraints from the scene and the cameras, for estimating the rigid transformations from the camera coordinate frame of one Kinect V2 to the camera coordinate frames of 12-nearest RGB cameras; (ii) a novel coarse-to-fine approach for recovering the rigid transformation from the coordinate system of one Kinect to the coordinate system of the other by means of local color and geometry information; (iii) several novel algorithms that can be categorized into two groups for reconstructing a DSLF from an input SSLF, including novel view synthesis methods, which are inspired by the state-of-the-art video frame interpolation algorithms, and Epipolar-Plane Image (EPI) inpainting methods, which are inspired by the Shearlet Transform (ST)-based DSLF reconstruction approaches
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