Academic literature on the topic 'RGB-Depth Image'
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Journal articles on the topic "RGB-Depth Image"
Li, Hengyu, Hang Liu, Ning Cao, Yan Peng, Shaorong Xie, Jun Luo, and Yu Sun. "Real-time RGB-D image stitching using multiple Kinects for improved field of view." International Journal of Advanced Robotic Systems 14, no. 2 (March 1, 2017): 172988141769556. http://dx.doi.org/10.1177/1729881417695560.
Full textWu, Yan, Jiqian Li, and Jing Bai. "Multiple Classifiers-Based Feature Fusion for RGB-D Object Recognition." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 05 (February 27, 2017): 1750014. http://dx.doi.org/10.1142/s0218001417500148.
Full textOYAMA, Tadahiro, and Daisuke MATSUZAKI. "Depth Image Generation from monocular RGB image." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2019 (2019): 2P2—H09. http://dx.doi.org/10.1299/jsmermd.2019.2p2-h09.
Full textCao, Hao, Xin Zhao, Ang Li, and Meng Yang. "Depth Image Rectification Based on an Effective RGB–Depth Boundary Inconsistency Model." Electronics 13, no. 16 (August 22, 2024): 3330. http://dx.doi.org/10.3390/electronics13163330.
Full textZhang, Longyu, Hao Xia, and Yanyou Qiao. "Texture Synthesis Repair of RealSense D435i Depth Images with Object-Oriented RGB Image Segmentation." Sensors 20, no. 23 (November 24, 2020): 6725. http://dx.doi.org/10.3390/s20236725.
Full textKwak, Jeonghoon, and Yunsick Sung. "Automatic 3D Landmark Extraction System Based on an Encoder–Decoder Using Fusion of Vision and LiDAR." Remote Sensing 12, no. 7 (April 3, 2020): 1142. http://dx.doi.org/10.3390/rs12071142.
Full textTang, Shengjun, Qing Zhu, Wu Chen, Walid Darwish, Bo Wu, Han Hu, and Min Chen. "ENHANCED RGB-D MAPPING METHOD FOR DETAILED 3D MODELING OF LARGE INDOOR ENVIRONMENTS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-1 (June 2, 2016): 151–58. http://dx.doi.org/10.5194/isprsannals-iii-1-151-2016.
Full textTang, Shengjun, Qing Zhu, Wu Chen, Walid Darwish, Bo Wu, Han Hu, and Min Chen. "ENHANCED RGB-D MAPPING METHOD FOR DETAILED 3D MODELING OF LARGE INDOOR ENVIRONMENTS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-1 (June 2, 2016): 151–58. http://dx.doi.org/10.5194/isprs-annals-iii-1-151-2016.
Full textLee, Ki-Seung. "Improving the Performance of Automatic Lip-Reading Using Image Conversion Techniques." Electronics 13, no. 6 (March 9, 2024): 1032. http://dx.doi.org/10.3390/electronics13061032.
Full textKao, Yueying, Weiming Li, Qiang Wang, Zhouchen Lin, Wooshik Kim, and Sunghoon Hong. "Synthetic Depth Transfer for Monocular 3D Object Pose Estimation in the Wild." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11221–28. http://dx.doi.org/10.1609/aaai.v34i07.6781.
Full textDissertations / Theses on the topic "RGB-Depth Image"
Deng, Zhuo. "RGB-DEPTH IMAGE SEGMENTATION AND OBJECT RECOGNITION FOR INDOOR SCENES." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/427631.
Full textPh.D.
With the advent of Microsoft Kinect, the landscape of various vision-related tasks has been changed. Firstly, using an active infrared structured light sensor, the Kinect can provide directly the depth information that is hard to infer from traditional RGB images. Secondly, RGB and depth information are generated synchronously and can be easily aligned, which makes their direct integration possible. In this thesis, I propose several algorithms or systems that focus on how to integrate depth information with traditional visual appearances for addressing different computer vision applications. Those applications cover both low level (image segmentation, class agnostic object proposals) and high level (object detection, semantic segmentation) computer vision tasks. To firstly understand whether and how depth information is helpful for improving computer vision performances, I start research on the image segmentation field, which is a fundamental problem and has been studied extensively in natural color images. We propose an unsupervised segmentation algorithm that is carefully crafted to balance the contribution of color and depth features in RGB-D images. The segmentation problem is then formulated as solving the Maximum Weight Independence Set (MWIS) problem. Given superpixels obtained from different layers of a hierarchical segmentation, the saliency of each superpixel is estimated based on balanced combination of features originating from depth, gray level intensity, and texture information. We evaluate the segmentation quality based on five standard measures on the commonly used NYU-v2 RGB-Depth dataset. A surprising message indicated from experiments is that unsupervised image segmentation of RGB-D images yields comparable results to supervised segmentation. In image segmentation, an image is partitioned into several groups of pixels (or super-pixels). We take one step further to investigate on the problem of assigning class labels to every pixel, i.e., semantic scene segmentation. We propose a novel image region labeling method which augments CRF formulation with hard mutual exclusion (mutex) constraints. This way our approach can make use of rich and accurate 3D geometric structure coming from Kinect in a principled manner. The final labeling result must satisfy all mutex constraints, which allows us to eliminate configurations that violate common sense physics laws like placing a floor above a night stand. Three classes of mutex constraints are proposed: global object co-occurrence constraint, relative height relationship constraint, and local support relationship constraint. Segments obtained from image segmentation can be either too fine or too coarse. A full object region not only conveys global features but also arguably enriches contextual features as confusing background is separated. We propose a novel unsupervised framework for automatically generating bottom up class independent object candidates for detection and recognition in cluttered indoor environments. Utilizing raw depth map, we propose a novel plane segmentation algorithm for dividing an indoor scene into predominant planar regions and non-planar regions. Based on this partition, we are able to effectively predict object locations and their spatial extensions. Our approach automatically generates object proposals considering five different aspects: Non-planar Regions (NPR), Planar Regions (PR), Detected Planes (DP), Merged Detected Planes (MDP) and Hierarchical Clustering (HC) of 3D point clouds. Object region proposals include both bounding boxes and instance segments. Although 2D computer vision tasks can roughly identify where objects are placed on image planes, their true locations and poses in the physical 3D world are difficult to determine due to multiple factors such as occlusions and the uncertainty arising from perspective projections. However, it is very natural for human beings to understand how far objects are from viewers, object poses and their full extents from still images. These kind of features are extremely desirable for many applications such as robotics navigation, grasp estimation, and Augmented Reality (AR) etc. In order to fill the gap, we addresses the problem of amodal perception of 3D object detection. The task is to not only find object localizations in the 3D world, but also estimate their physical sizes and poses, even if only parts of them are visible in the RGB-D image. Recent approaches have attempted to harness point cloud from depth channel to exploit 3D features directly in the 3D space and demonstrated the superiority over traditional 2D representation approaches. We revisit the amodal 3D detection problem by sticking to the 2D representation framework, and directly relate 2D visual appearance to 3D objects. We propose a novel 3D object detection system that simultaneously predicts objects' 3D locations, physical sizes, and orientations in indoor scenes.
Temple University--Theses
Hasnat, Md Abul. "Unsupervised 3D image clustering and extension to joint color and depth segmentation." Thesis, Saint-Etienne, 2014. http://www.theses.fr/2014STET4013/document.
Full textAccess to the 3D images at a reasonable frame rate is widespread now, thanks to the recent advances in low cost depth sensors as well as the efficient methods to compute 3D from 2D images. As a consequence, it is highly demanding to enhance the capability of existing computer vision applications by incorporating 3D information. Indeed, it has been demonstrated in numerous researches that the accuracy of different tasks increases by including 3D information as an additional feature. However, for the task of indoor scene analysis and segmentation, it remains several important issues, such as: (a) how the 3D information itself can be exploited? and (b) what is the best way to fuse color and 3D in an unsupervised manner? In this thesis, we address these issues and propose novel unsupervised methods for 3D image clustering and joint color and depth image segmentation. To this aim, we consider image normals as the prominent feature from 3D image and cluster them with methods based on finite statistical mixture models. We consider Bregman Soft Clustering method to ensure computationally efficient clustering. Moreover, we exploit several probability distributions from directional statistics, such as the von Mises-Fisher distribution and the Watson distribution. By combining these, we propose novel Model Based Clustering methods. We empirically validate these methods using synthetic data and then demonstrate their application for 3D/depth image analysis. Afterward, we extend these methods to segment synchronized 3D and color image, also called RGB-D image. To this aim, first we propose a statistical image generation model for RGB-D image. Then, we propose novel RGB-D segmentation method using a joint color-spatial-axial clustering and a statistical planar region merging method. Results show that, the proposed method is comparable with the state of the art methods and requires less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner. We believe that the methods proposed in this thesis are equally applicable and extendable for clustering different types of data, such as speech, gene expressions, etc. Moreover, they can be used for complex tasks, such as joint image-speech data analysis
Baban, a. erep Thierry Roland. "Contribution au développement d'un système intelligent de quantification des nutriments dans les repas d'Afrique subsaharienne." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP100.
Full textMalnutrition, including under- and overnutrition, is a global health challenge affecting billions of people. It impacts all organ systems and is a significant risk factor for noncommunicable diseases such as cardiovascular diseases, diabetes, and some cancers. Assessing food intake is crucial for preventing malnutrition but remains challenging. Traditional methods for dietary assessment are labor-intensive and prone to bias. Advancements in AI have made Vision-Based Dietary Assessment (VBDA) a promising solution for automatically analyzing food images to estimate portions and nutrition. However, food image segmentation in VBDA faces challenges due to food's non-rigid structure, high intra-class variation (where the same dish can look very different), inter-class resemblance (where different foods appear similar) and scarcity of publicly available datasets.Almost all food segmentation research has focused on Asian and Western foods, with no datasets for African cuisines. However, African dishes often involve mixed food classes, making accurate segmentation challenging. Additionally, research has largely focus on RGB images, which provides color and texture but may lack geometric detail. To address this, RGB-D segmentation combines depth data with RGB images. Depth images provide crucial geometric details that enhance RGB data, improve object discrimination, and are robust to factors like illumination and fog. Despite its success in other fields, RGB-D segmentation for food is underexplored due to difficulties in collecting food depth images.This thesis makes key contributions by developing new deep learning models for RGB (mid-DeepLabv3+) and RGB-D (ESeNet-D) image segmentation and introducing the first food segmentation datasets focused on African food images. Mid-DeepLabv3+ is based on DeepLabv3+, featuring a simplified ResNet backbone with and added skip layer (middle layer) in the decoder and SimAM attention mechanism. This model offers an optimal balance between performance and efficiency, matching DeepLabv3+'s performance while cutting computational load by half. ESeNet-D consists on two encoder branches using EfficientNetV2 as backbone, with a fusion block for multi-scale integration and a decoder employing self-calibrated convolution and learned interpolation for precise segmentation. ESeNet-D outperforms many RGB and RGB-D benchmark models while having fewer parameters and FLOPs. Our experiments show that, when properly integrated, depth information can significantly improve food segmentation accuracy. We also present two new datasets: AfricaFoodSeg for “food/non-food” segmentation with 3,067 images (2,525 for training, 542 for validation), and CamerFood focusing on Cameroonian cuisine. CamerFood datasets include CamerFood10 with 1,422 images from ten food classes, and CamerFood15, an enhanced version with 15 food classes, 1,684 training images, and 514 validation images. Finally, we address the challenge of scarce depth data in RGB-D food segmentation by demonstrating that Monocular Depth Estimation (MDE) models can aid in generating effective depth maps for RGB-D datasets
Řehánek, Martin. "Detekce objektů pomocí Kinectu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236602.
Full textSANTOS, LEANDRO TAVARES ARAGAO DOS. "GENERATING SUPERRESOLVED DEPTH MAPS USING LOW COST SENSORS AND RGB IMAGES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28673@1.
Full textCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
As aplicações da reconstrução em três dimensões de uma cena real são as mais diversas. O surgimento de sensores de profundidade de baixo custo, tal qual o Kinect, sugere o desenvolvimento de sistemas de reconstrução mais baratos que aqueles já existentes. Contudo, os dados disponibilizados por este dispositivo ainda carecem em muito quando comparados àqueles providos por sistemas mais sofisticados. No mundo acadêmico e comercial, algumas iniciativas, como aquelas de Tong et al. [1] e de Cui et al. [2], se propõem a solucionar tal problema. A partir do estudo das mesmas, este trabalho propôs a modificação do algoritmo de super-resolução descrito por Mitzel et al. [3] no intuito de considerar em seus cálculos as imagens coloridas também fornecidas pelo dispositivo, conforme abordagem de Cui et al. [2]. Tal alteração melhorou os mapas de profundidade super-resolvidos fornecidos, mitigando interferências geradas por movimentações repentinas na cena captada. Os testes realizados comprovam a melhoria dos mapas gerados, bem como analisam o impacto da implementação em CPU e GPU dos algoritmos nesta etapa da super-resolução. O trabalho se restringe a esta etapa. As etapas seguintes da reconstrução 3D não foram implementadas.
There are a lot of three dimensions reconstruction applications of real scenes. The rise of low cost sensors, like the Kinect, suggests the development of systems cheaper than the existing ones. Nevertheless, data provided by this device are worse than that provided by more sophisticated sensors. In the academic and commercial world, some initiatives, described in Tong et al. [1] and in Cui et al. [2], try to solve that problem. Studying that attempts, this work suggests the modification of super-resolution algorithm described for Mitzel et al. [3] in order to consider in its calculations coloured images provided by Kinect, like the approach of Cui et al. [2]. This change improved the super resolved depth maps provided, mitigating interference caused by sudden changes of captured scenes. The tests proved the improvement of generated maps and analysed the impact of CPU and GPU algorithms implementation in the superresolution step. This work is restricted to this step. The next stages of 3D reconstruction have not been implemented.
Thörnberg, Jesper. "Combining RGB and Depth Images for Robust Object Detection using Convolutional Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174137.
Full textMöckelind, Christoffer. "Improving deep monocular depth predictions using dense narrow field of view depth images." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235660.
Full textI det här arbetet studerar vi ett djupapproximationsproblem där vi tillhandahåller en djupbild med smal synvinkel och en RGB-bild med bred synvinkel till ett djupt nätverk med uppgift att förutsäga djupet för hela RGB-bilden. Vi visar att genom att ge djupbilden till nätverket förbättras resultatet för området utanför det tillhandahållna djupet jämfört med en existerande metod som använder en RGB-bild för att förutsäga djupet. Vi undersöker flera arkitekturer och storlekar på djupbildssynfält och studerar effekten av att lägga till brus och sänka upplösningen på djupbilden. Vi visar att större synfält för djupbilden ger en större fördel och även att modellens noggrannhet minskar med avståndet från det angivna djupet. Våra resultat visar också att modellerna som använde sig av det brusiga lågupplösta djupet presterade på samma nivå som de modeller som använde sig av det omodifierade djupet.
Hammond, Patrick Douglas. "Deep Synthetic Noise Generation for RGB-D Data Augmentation." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7516.
Full textTu, Chieh-Min, and 杜介民. "Depth Image Inpainting with RGB-D Camera." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/k4m42a.
Full text義守大學
資訊工程學系
103
Since Microsoft released the cheap Kinect sensors as a new natural user interface, stereo imaging is made from previous multi-view color image synthesis, to now synthesis of color image and depth image. But the captured depth images may lose some depth values so that stereoscopic effect is often poor in general. This thesis is based on Kinect RGB-D camera to develop an object-based depth inpainting method. Firstly, the background differencing, frame differencing and depth thresholding strategies are used as a basis for segmenting foreground objects from a dynamic background image. Then, the task of hole inpainting is divided into background area and foreground area, in which background area is inpainted by background depth image and foreground area is inpainted by a best-fit neighborhood depth value. Experimental results show that such an inpainting method is helpful to fill holes, and to improve the contour edges and image quality.
Lin, Shih-Pi, and 林士筆. "In-air Handwriting Chinese Character Recognition Base on RGB Image without Depth Information." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2mhfzk.
Full text國立中央大學
資訊工程學系
107
As technology changes rapidly, Human-Computer Interaction(HCI) no longer being limited by keyboard. Existing handwriting products are provided sufficient feature to recognize handwriting trajectories on density and stability. For Chinese font, it is relatively difficult for machines to obtain stable trajectory comparing to English and numerals. In the past, in-air hand detection and tracking often used the devices with depth information. For example, Kinect uses two infrared cameras to obtain depth information, which cause higher price on devices. Therefore, the use of RGB information with one camera to achieve object detection and tracking is a trend in recent years. The use of RGB camera as HCI media for in-air handwriting need to deal with accurate hand detection and stability tracking, and the handwriting trajectory has one stroke-finished attribute, which means that it will have both real stroke and virtual stroke, it increases the difficulty of recognition. The hand database uses to build the model contains, self-recorded handwriting videos and the relevant hand data sets collected on the Internet. By adding the Multiple Receptive Field(MRF) in processing data, which scale the ground truth and regard the scaled as a new object, it increases the robustness of detection. This paper uses YOLO v3 as the core neural network model, and adds Convolutional Recurrent Neural Network(CRNN) to convert YOLO into a time-sequential neural network to stabilize tracking. The analysis of the experimental results shows that the hand detection can be more robust after the data processed by the MRF. The converted YOLO improves the stability of hand tracking. Overall, using several Chinese character recognition methods, the accuracy of recognize in-air handwriting trajectory in Chinese characters is about 96.33%.
Book chapters on the topic "RGB-Depth Image"
Pan, Hong, Søren Ingvor Olsen, and Yaping Zhu. "Joint Spatial-Depth Feature Pooling for RGB-D Object Classification." In Image Analysis, 314–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19665-7_26.
Full textLiu, Shirui, Hamid A. Jalab, and Zhen Dai. "Intrinsic Face Image Decomposition from RGB Images with Depth Cues." In Advances in Visual Informatics, 149–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34032-2_14.
Full textGuo, Jinxin, Qingxiang Wang, and Xiaoqiang Ren. "Target Recognition Based on Kinect Combined RGB Image with Depth Image." In Advances in Intelligent Systems and Computing, 726–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25128-4_89.
Full textMechal, Chaymae El, Najiba El Amrani El Idrissi, and Mostefa Mesbah. "CNN-Based Obstacle Avoidance Using RGB-Depth Image Fusion." In Lecture Notes in Electrical Engineering, 867–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6893-4_78.
Full textPetrelli, Alioscia, and Luigi Di Stefano. "Learning to Weight Color and Depth for RGB-D Visual Search." In Image Analysis and Processing - ICIAP 2017, 648–59. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68560-1_58.
Full textChen, Anran, Yao Zhao, and Chunyu Lin. "RGB Image Guided Depth Hole-Filling Using Bidirectional Attention Mechanism." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 173–82. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1053-1_16.
Full textFarahnakian, Fahimeh, and Jukka Heikkonen. "RGB and Depth Image Fusion for Object Detection Using Deep Learning." In Advances in Intelligent Systems and Computing, 73–93. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3357-7_3.
Full textKhaire, Pushpajit, Javed Imran, and Praveen Kumar. "Human Activity Recognition by Fusion of RGB, Depth, and Skeletal Data." In Proceedings of 2nd International Conference on Computer Vision & Image Processing, 409–21. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7895-8_32.
Full textLi, Yijin, Xinyang Liu, Wenqi Dong, Han Zhou, Hujun Bao, Guofeng Zhang, Yinda Zhang, and Zhaopeng Cui. "DELTAR: Depth Estimation from a Light-Weight ToF Sensor and RGB Image." In Lecture Notes in Computer Science, 619–36. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19769-7_36.
Full textKam, Jaewon, Jungeon Kim, Soongjin Kim, Jaesik Park, and Seungyong Lee. "CostDCNet: Cost Volume Based Depth Completion for a Single RGB-D Image." In Lecture Notes in Computer Science, 257–74. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20086-1_15.
Full textConference papers on the topic "RGB-Depth Image"
Qiu, Zhouyan, Shang Zeng, Joaquín Martínez Sánchez, and Pedro Arias. "Comparative analysis of image super-resolution: A concurrent study of RGB and depth images." In 2024 International Workshop on the Theory of Computational Sensing and its Applications to Radar, Multimodal Sensing and Imaging (CoSeRa), 36–41. IEEE, 2024. http://dx.doi.org/10.1109/cosera60846.2024.10720360.
Full textMorisset, Maxime, Marc Donias, and Christian Germain. "Principal Curvatures as Pose-Invariant Features of Depth Maps for RGB-D Object Recognition." In 2024 IEEE Thirteenth International Conference on Image Processing Theory, Tools and Applications (IPTA), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/ipta62886.2024.10755742.
Full textBaban A Erep, Thierry Roland, Lotfi Chaari, Pierre Ele, and Eugene Sobngwi. "ESeNet-D : Efficient Semantic Segmentation for RGB-Depth Food Images." In 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/mlsp58920.2024.10734761.
Full textYu, Yeh-Wei, Tzu-Kai Wang, Chi-Chung Lau, Jia-Ching Wang, Tsung-Hsun Yang, Jann-Long Chern, and Ching-Cherng Sun. "Repairing IR depth image with 2D RGB image." In Current Developments in Lens Design and Optical Engineering XIX, edited by R. Barry Johnson, Virendra N. Mahajan, and Simon Thibault. SPIE, 2018. http://dx.doi.org/10.1117/12.2321205.
Full textIssaranon, Theerasit, Chuhang Zou, and David Forsyth. "Counterfactual Depth from a Single RGB Image." In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019. http://dx.doi.org/10.1109/iccvw.2019.00268.
Full textLi, Wenju, Wenkang Hu, Tianzhen Dong, and Jiantao Qu. "Depth Image Enhancement Algorithm Based on RGB Image Fusion." In 2018 11th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2018. http://dx.doi.org/10.1109/iscid.2018.10126.
Full textHui, Tak-Wai, and King Ngi Ngan. "Depth enhancement using RGB-D guided filtering." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025778.
Full textBai, Jinghui, Jingyu Yang, Xinchen Ye, and Chunping Hou. "Depth refinement for binocular kinect RGB-D cameras." In 2016 Visual Communications and Image Processing (VCIP). IEEE, 2016. http://dx.doi.org/10.1109/vcip.2016.7805545.
Full textWaskitho, Suryo Aji, Ardiansyah Alfarouq, Sritrusta Sukaridhoto, and Dadet Pramadihanto. "FloW vision: Depth image enhancement by combining stereo RGB-depth sensor." In 2016 International Conference on Knowledge Creation and Intelligent Computing (KCIC). IEEE, 2016. http://dx.doi.org/10.1109/kcic.2016.7883644.
Full textYan, Zengqiang, Li Yu, and Zixiang Xiong. "Large-area depth recovery for RGB-D camera." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351032.
Full text