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Добірка наукової літератури з теми "RGB-D Image"

Автор: Grafiati

Опубліковано: 11 січня 2025

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Статті в журналах з теми "RGB-D Image"

Uddin, Md Kamal, Amran Bhuiyan, and Mahmudul Hasan. "Fusion in Dissimilarity Space Between RGB D and Skeleton for Person Re Identification." International Journal of Innovative Technology and Exploring Engineering 10, no. 12 (October 30, 2021): 69–75. http://dx.doi.org/10.35940/ijitee.l9566.10101221.

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Person re-identification (Re-id) is one of the important tools of video surveillance systems, which aims to recognize an individual across the multiple disjoint sensors of a camera network. Despite the recent advances on RGB camera-based person re-identification methods under normal lighting conditions, Re-id researchers fail to take advantages of modern RGB-D sensor-based additional information (e.g. depth and skeleton information). When traditional RGB-based cameras fail to capture the video under poor illumination conditions, RGB-D sensor-based additional information can be advantageous to tackle these constraints. This work takes depth images and skeleton joint points as additional information along with RGB appearance cues and proposes a person re-identification method. We combine 4-channel RGB-D image features with skeleton information using score-level fusion strategy in dissimilarity space to increase re-identification accuracy. Moreover, our propose method overcomes the illumination problem because we use illumination invariant depth image and skeleton information. We carried out rigorous experiments on two publicly available RGBD-ID re-identification datasets and proved the use of combined features of 4-channel RGB-D images and skeleton information boost up the rank 1 recognition accuracy.

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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.

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This article concerns the problems of a defective depth map and limited field of view of Kinect-style RGB-D sensors. An anisotropic diffusion based hole-filling method is proposed to recover invalid depth data in the depth map. The field of view of the Kinect-style RGB-D sensor is extended by stitching depth and color images from several RGB-D sensors. By aligning the depth map with the color image, the registration data calculated by registering color images can be used to stitch depth and color images into a depth and color panoramic image concurrently in real time. Experiments show that the proposed stitching method can generate a RGB-D panorama with no invalid depth data and little distortion in real time and can be extended to incorporate more RGB-D sensors to construct even a 360° field of view panoramic RGB-D image.

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Wu, 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.

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RGB-D-based object recognition has been enthusiastically investigated in the past few years. RGB and depth images provide useful and complementary information. Fusing RGB and depth features can significantly increase the accuracy of object recognition. However, previous works just simply take the depth image as the fourth channel of the RGB image and concatenate the RGB and depth features, ignoring the different power of RGB and depth information for different objects. In this paper, a new method which contains three different classifiers is proposed to fuse features extracted from RGB image and depth image for RGB-D-based object recognition. Firstly, a RGB classifier and a depth classifier are trained by cross-validation to get the accuracy difference between RGB and depth features for each object. Then a variant RGB-D classifier is trained with different initialization parameters for each class according to the accuracy difference. The variant RGB-D-classifier can result in a more robust classification performance. The proposed method is evaluated on two benchmark RGB-D datasets. Compared with previous methods, ours achieves comparable performance with the state-of-the-art method.

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Kitzler, Florian, Norbert Barta, Reinhard W. Neugschwandtner, Andreas Gronauer, and Viktoria Motsch. "WE3DS: An RGB-D Image Dataset for Semantic Segmentation in Agriculture." Sensors 23, no. 5 (March 1, 2023): 2713. http://dx.doi.org/10.3390/s23052713.

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Smart farming (SF) applications rely on robust and accurate computer vision systems. An important computer vision task in agriculture is semantic segmentation, which aims to classify each pixel of an image and can be used for selective weed removal. State-of-the-art implementations use convolutional neural networks (CNN) that are trained on large image datasets. In agriculture, publicly available RGB image datasets are scarce and often lack detailed ground-truth information. In contrast to agriculture, other research areas feature RGB-D datasets that combine color (RGB) with additional distance (D) information. Such results show that including distance as an additional modality can improve model performance further. Therefore, we introduce WE3DS as the first RGB-D image dataset for multi-class plant species semantic segmentation in crop farming. It contains 2568 RGB-D images (color image and distance map) and corresponding hand-annotated ground-truth masks. Images were taken under natural light conditions using an RGB-D sensor consisting of two RGB cameras in a stereo setup. Further, we provide a benchmark for RGB-D semantic segmentation on the WE3DS dataset and compare it with a solely RGB-based model. Our trained models achieve up to 70.7% mean Intersection over Union (mIoU) for discriminating between soil, seven crop species, and ten weed species. Finally, our work confirms the finding that additional distance information improves segmentation quality.

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Zheng, Huiming, and Wei Gao. "End-to-End RGB-D Image Compression via Exploiting Channel-Modality Redundancy." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (March 24, 2024): 7562–70. http://dx.doi.org/10.1609/aaai.v38i7.28588.

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As a kind of 3D data, RGB-D images have been extensively used in object tracking, 3D reconstruction, remote sensing mapping, and other tasks. In the realm of computer vision, the significance of RGB-D images is progressively growing. However, the existing learning-based image compression methods usually process RGB images and depth images separately, which cannot entirely exploit the redundant information between the modalities, limiting the further improvement of the Rate-Distortion performance. With the goal of overcoming the defect, in this paper, we propose a learning-based dual-branch RGB-D image compression framework. Compared with traditional RGB domain compression scheme, a YUV domain compression scheme is presented for spatial redundancy removal. In addition, Intra-Modality Attention (IMA) and Cross-Modality Attention (CMA) are introduced for modal redundancy removal. For the sake of benefiting from cross-modal prior information, Context Prediction Module (CPM) and Context Fusion Module (CFM) are raised in the conditional entropy model which makes the context probability prediction more accurate. The experimental results demonstrate our method outperforms existing image compression methods in two RGB-D image datasets. Compared with BPG, our proposed framework can achieve up to 15% bit rate saving for RGB images.

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Peroš, Josip, Rinaldo Paar, Vladimir Divić, and Boštjan Kovačić. "Fusion of Laser Scans and Image Data—RGB+D for Structural Health Monitoring of Engineering Structures." Applied Sciences 12, no. 22 (November 19, 2022): 11763. http://dx.doi.org/10.3390/app122211763.

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A novel method for structural health monitoring (SHM) by using RGB+D data has been recently proposed. RGB+D data are created by fusing image and laser scan data, where the D channel represents the distance, interpolated from laser scanner data. RGB channel represents image data obtained by an image sensor integrated in robotic total station (RTS) telescope, or on top of the telescope i.e., image assisted total station (IATS). Images can also be obtained by conventional cameras, or cameras integrated with RTS (different kind of prototypes). RGB+D image combines the advantages of the two measuring methods. Laser scans are used for distance changes in the line of sight and image data are used for displacements determination in two axes perpendicular to the viewing direction of the camera. Image feature detection and matching algorithms detect and match discrete points within RGB+D images obtained from different epochs. These way 3D coordinates of the points can be easily calculated from RGB+D images. In this study, the implementation of this method was proposed for measuring displacements and monitoring the behavior of structural elements under constant load in field conditions. For the precision analysis of the proposed method, displacements obtained from a numerical model in combination with measurements from a high precision linear variable differential transformer (LVDT) sensor was used as a reference for the analysis of determined displacements from RGB+D images. Based on the achieved results, we calculated that in this study, the precision of the image matching and fusion part of the RGB+D is ±1 mm while using the ORB algorithm. The ORB algorithm was determined as the optimal algorithm for this study, with good computing performance, lowest processing times and the highest number of usable features detected. The calculated achievable precision for determining height displacement while monitoring the behavior of structural element wooden beam under different loads is ±2.7 mm.

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Yan, Zhiqiang, Hongyuan Wang, Qianhao Ning, and Yinxi Lu. "Robust Image Matching Based on Image Feature and Depth Information Fusion." Machines 10, no. 6 (June 8, 2022): 456. http://dx.doi.org/10.3390/machines10060456.

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In this paper, we propose a robust image feature extraction and fusion method to effectively fuse image feature and depth information and improve the registration accuracy of RGB-D images. The proposed method directly splices the image feature point descriptors with the corresponding point cloud feature descriptors to obtain the fusion descriptor of the feature points. The fusion feature descriptor is constructed based on the SIFT, SURF, and ORB feature descriptors and the PFH and FPFH point cloud feature descriptors. Furthermore, the registration performance based on fusion features is tested through the RGB-D datasets of YCB and KITTI. ORBPFH reduces the false-matching rate by 4.66~16.66%, and ORBFPFH reduces the false-matching rate by 9~20%. The experimental results show that the RGB-D robust feature extraction and fusion method proposed in this paper is suitable for the fusion of ORB with PFH and FPFH, which can improve feature representation and registration, representing a novel approach for RGB-D image matching.

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Yuan, Yuan, Zhitong Xiong, and Qi Wang. "ACM: Adaptive Cross-Modal Graph Convolutional Neural Networks for RGB-D Scene Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9176–84. http://dx.doi.org/10.1609/aaai.v33i01.33019176.

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RGB image classification has achieved significant performance improvement with the resurge of deep convolutional neural networks. However, mono-modal deep models for RGB image still have several limitations when applied to RGB-D scene recognition. 1) Images for scene classification usually contain more than one typical object with flexible spatial distribution, so the object-level local features should also be considered in addition to global scene representation. 2) Multi-modal features in RGB-D scene classification are still under-utilized. Simply combining these modal-specific features suffers from the semantic gaps between different modalities. 3) Most existing methods neglect the complex relationships among multiple modality features. Considering these limitations, this paper proposes an adaptive crossmodal (ACM) feature learning framework based on graph convolutional neural networks for RGB-D scene recognition. In order to make better use of the modal-specific cues, this approach mines the intra-modality relationships among the selected local features from one modality. To leverage the multi-modal knowledge more effectively, the proposed approach models the inter-modality relationships between two modalities through the cross-modal graph (CMG). We evaluate the proposed method on two public RGB-D scene classification datasets: SUN-RGBD and NYUD V2, and the proposed method achieves state-of-the-art performance.

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Wang, Z., T. Li, L. Pan, and Z. Kang. "SCENE SEMANTIC SEGMENTATION FROM INDOOR RGB-D IMAGES USING ENCODE-DECODER FULLY CONVOLUTIONAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 397–404. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-397-2017.

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Анотація:

With increasing attention for the indoor environment and the development of low-cost RGB-D sensors, indoor RGB-D images are easily acquired. However, scene semantic segmentation is still an open area, which restricts indoor applications. The depth information can help to distinguish the regions which are difficult to be segmented out from the RGB images with similar color or texture in the indoor scenes. How to utilize the depth information is the key problem of semantic segmentation for RGB-D images. In this paper, we propose an Encode-Decoder Fully Convolutional Networks for RGB-D image classification. We use Multiple Kernel Maximum Mean Discrepancy (MK-MMD) as a distance measure to find common and special features of RGB and D images in the network to enhance performance of classification automatically. To explore better methods of applying MMD, we designed two strategies; the first calculates MMD for each feature map, and the other calculates MMD for whole batch features. Based on the result of classification, we use the full connect CRFs for the semantic segmentation. The experimental results show that our method can achieve a good performance on indoor RGB-D image semantic segmentation.

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Kanda, Takuya, Kazuya Miyakawa, Jeonghwang Hayashi, Jun Ohya, Hiroyuki Ogata, Kenji Hashimoto, Xiao Sun, Takashi Matsuzawa, Hiroshi Naito, and Atsuo Takanishi. "Locating Mechanical Switches Using RGB-D Sensor Mounted on a Disaster Response Robot." Electronic Imaging 2020, no. 6 (January 26, 2020): 16–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.6.iriacv-016.

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To achieve one of the tasks required for disaster response robots, this paper proposes a method for locating 3D structured switches’ points to be pressed by the robot in disaster sites using RGBD images acquired by Kinect sensor attached to our disaster response robot. Our method consists of the following five steps: 1)Obtain RGB and depth images using an RGB-D sensor. 2) Detect the bounding box of switch area from the RGB image using YOLOv3. 3)Generate 3D point cloud data of the target switch by combining the bounding box and the depth image.4)Detect the center position of the switch button from the RGB image in the bounding box using Convolutional Neural Network (CNN). 5)Estimate the center of the button’s face in real space from the detection result in step 4) and the 3D point cloud data generated in step3) In the experiment, the proposed method is applied to two types of 3D structured switch boxes to evaluate the effectiveness. The results show that our proposed method can locate the switch button accurately enough for the robot operation.

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Більше джерел

Дисертації з теми "RGB-D Image"

Murgia, Julian. "Segmentation d'objets mobiles par fusion RGB-D et invariance colorimétrique." Thesis, Belfort-Montbéliard, 2016. http://www.theses.fr/2016BELF0289/document.

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Cette thèse s'inscrit dans un cadre de vidéo-surveillance, et s'intéresse plus précisément à la détection robustesd'objets mobiles dans une séquence d'images. Une bonne détection d'objets mobiles est un prérequis indispensableà tout traitement appliqué à ces objets dans de nombreuses applications telles que le suivi de voitures ou depersonnes, le comptage des passagers de transports en commun, la détection de situations dangereuses dans desenvironnements spécifiques (passages à niveau, passages piéton, carrefours, etc.), ou encore le contrôle devéhicules autonomes. Un très grand nombre de ces applications utilise un système de vision par ordinateur. Lafiabilité de ces systèmes demande une robustesse importante face à des conditions parfois difficiles souventcausées par les conditions d'illumination (jour/nuit, ombres portées), les conditions météorologiques (pluie, vent,neige) ainsi que la topologie même de la scène observée (occultations). Les travaux présentés dans cette thèsevisent à améliorer la qualité de détection d'objets mobiles en milieu intérieur ou extérieur, et à tout moment de lajournée.Pour ce faire, nous avons proposé trois stratégies combinables :i) l'utilisation d'invariants colorimétriques et/ou d'espaces de représentation couleur présentant des propriétésinvariantes ;ii) l'utilisation d'une caméra stéréoscopique et d'une caméra active Microsoft Kinect en plus de la caméra couleurafin de reconstruire l'environnement 3D partiel de la scène, et de fournir une dimension supplémentaire, à savoirune information de profondeur, à l'algorithme de détection d'objets mobiles pour la caractérisation des pixels ;iii) la proposition d'un nouvel algorithme de fusion basé sur la logique floue permettant de combiner les informationsde couleur et de profondeur tout en accordant une certaine marge d'incertitude quant à l'appartenance du pixel aufond ou à un objet mobile
This PhD thesis falls within the scope of video-surveillance, and more precisely focuses on the detection of movingobjects in image sequences. In many applications, good detection of moving objects is an indispensable prerequisiteto any treatment applied to these objects such as people or cars tracking, passengers counting, detection ofdangerous situations in specific environments (level crossings, pedestrian crossings, intersections, etc.), or controlof autonomous vehicles. The reliability of computer vision based systems require robustness against difficultconditions often caused by lighting conditions (day/night, shadows), weather conditions (rain, wind, snow...) and thetopology of the observed scene (occultation...).Works detailed in this PhD thesis aim at reducing the impact of illumination conditions by improving the quality of thedetection of mobile objects in indoor or outdoor environments and at any time of the day. Thus, we propose threestrategies working as a combination to improve the detection of moving objects:i) using colorimetric invariants and/or color spaces that provide invariant properties ;ii) using passive stereoscopic camera (in outdoor environments) and Microsoft Kinect active camera (in outdoorenvironments) in order to partially reconstruct the 3D environment, providing an additional dimension (a depthinformation) to the background/foreground subtraction algorithm ;iii) a new fusion algorithm based on fuzzy logic in order to combine color and depth information with a certain level ofuncertainty for the pixels classification

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Tykkälä, Tommi. "Suivi de caméra image en temps réel base et cartographie de l'environnement." Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00933813.

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Dans ce travail, méthodes d'estimation basées sur des images, également connu sous le nom de méthodes directes, sont étudiées qui permettent d'éviter l'extraction de caractéristiques et l'appariement complètement. L'objectif est de produire pose 3D précis et des estimations de la structure. Les fonctions de coût présenté minimiser l'erreur du capteur, car les mesures ne sont pas transformés ou modifiés. Dans la caméra photométrique estimation de la pose, rotation 3D et les paramètres de traduction sont estimées en minimisant une séquence de fonctions de coûts à base d'image, qui sont des non-linéaires en raison de la perspective projection et la distorsion de l'objectif. Dans l'image la structure basée sur le raffinement, d'autre part, de la structure 3D est affinée en utilisant un certain nombre de vues supplémentaires et un coût basé sur l'image métrique. Les principaux domaines d'application dans ce travail sont des reconstitutions d'intérieur, la robotique et la réalité augmentée. L'objectif global du projet est d'améliorer l'image des méthodes d'estimation fondées, et pour produire des méthodes de calcul efficaces qui peuvent être accueillis dans des applications réelles. Les principales questions pour ce travail sont : Qu'est-ce qu'une formulation efficace pour une image 3D basé estimation de la pose et de la structure tâche de raffinement ? Comment organiser calcul afin de permettre une mise en œuvre efficace en temps réel ? Quelles sont les considérations pratiques utilisant l'image des méthodes d'estimation basées sur des applications telles que la réalité augmentée et la reconstruction 3D ?

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Lai, Po Kong. "Immersive Dynamic Scenes for Virtual Reality from a Single RGB-D Camera." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39663.

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In this thesis we explore the concepts and components which can be used as individual building blocks for producing immersive virtual reality (VR) content from a single RGB-D sensor. We identify the properties of immersive VR videos and propose a system composed of a foreground/background separator, a dynamic scene re-constructor and a shape completer. We initially explore the foreground/background separator component in the context of video summarization. More specifically, we examined how to extract trajectories of moving objects from video sequences captured with a static camera. We then present a new approach for video summarization via minimization of the spatial-temporal projections of the extracted object trajectories. New evaluation criterion are also presented for video summarization. These concepts of foreground/background separation can then be applied towards VR scene creation by extracting relative objects of interest. We present an approach for the dynamic scene re-constructor component using a single moving RGB-D sensor. By tracking the foreground objects and removing them from the input RGB-D frames we can feed the background only data into existing RGB-D SLAM systems. The result is a static 3D background model where the foreground frames are then super-imposed to produce a coherent scene with dynamic moving foreground objects. We also present a specific method for extracting moving foreground objects from a moving RGB-D camera along with an evaluation dataset with benchmarks. Lastly, the shape completer component takes in a single view depth map of an object as input and "fills in" the occluded portions to produce a complete 3D shape. We present an approach that utilizes a new data minimal representation, the additive depth map, which allows traditional 2D convolutional neural networks to accomplish the task. The additive depth map represents the amount of depth required to transform the input into the "back depth map" which would exist if there was a sensor exactly opposite of the input. We train and benchmark our approach using existing synthetic datasets and also show that it can perform shape completion on real world data without fine-tuning. Our experiments show that our data minimal representation can achieve comparable results to existing state-of-the-art 3D networks while also being able to produce higher resolution outputs.

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Kadkhodamohammadi, Abdolrahim. "3D detection and pose estimation of medical staff in operating rooms using RGB-D images." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAD047/document.

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Анотація:

Dans cette thèse, nous traitons des problèmes de la détection des personnes et de l'estimation de leurs poses dans la Salle Opératoire (SO), deux éléments clés pour le développement d'applications d'assistance chirurgicale. Nous percevons la salle grâce à des caméras RGB-D qui fournissent des informations visuelles complémentaires sur la scène. Ces informations permettent de développer des méthodes mieux adaptées aux difficultés propres aux SO, comme l'encombrement, les surfaces sans texture et les occlusions. Nous présentons des nouvelles approches qui tirent profit des informations temporelles, de profondeur et des vues multiples afin de construire des modèles robustes pour la détection des personnes et de leurs poses. Une évaluation est effectuée sur plusieurs jeux de données complexes enregistrés dans des salles opératoires avec une ou plusieurs caméras. Les résultats obtenus sont très prometteurs et montrent que nos approches surpassent les méthodes de l'état de l'art sur ces données cliniques
In this thesis, we address the two problems of person detection and pose estimation in Operating Rooms (ORs), which are key ingredients in the development of surgical assistance applications. We perceive the OR using compact RGB-D cameras that can be conveniently integrated in the room. These sensors provide complementary information about the scene, which enables us to develop methods that can cope with numerous challenges present in the OR, e.g. clutter, textureless surfaces and occlusions. We present novel part-based approaches that take advantage of depth, multi-view and temporal information to construct robust human detection and pose estimation models. Evaluation is performed on new single- and multi-view datasets recorded in operating rooms. We demonstrate very promising results and show that our approaches outperform state-of-the-art methods on this challenging data acquired during real surgeries

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Meilland, Maxime. "Cartographie RGB-D dense pour la localisation visuelle temps-réel et la navigation autonome." Phd thesis, Ecole Nationale Supérieure des Mines de Paris, 2012. http://tel.archives-ouvertes.fr/tel-00686803.

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Анотація:

Dans le contexte de la navigation autonome en environnement urbain, une localisation précise du véhicule est importante pour une navigation sure et fiable. La faible précision des capteurs bas coût existants tels que le système GPS, nécessite l'utilisation d'autres capteurs eux aussi à faible coût. Les caméras mesurent une information photométrique riche et précise sur l'environnement, mais nécessitent l'utilisation d'algorithmes de traitement avancés pour obtenir une information sur la géométrie et sur la position de la caméra dans l'environnement. Cette problématique est connue sous le terme de Cartographie et Localisation Simultanées (SLAM visuel). En général, les techniques de SLAM sont incrémentales et dérivent sur de longues trajectoires. Pour simplifier l'étape de localisation, il est proposé de découpler la partie cartographie et la partie localisation en deux phases: la carte est construite hors-ligne lors d'une phase d'apprentissage, et la localisation est effectuée efficacement en ligne à partir de la carte 3D de l'environnement. Contrairement aux approches classiques, qui utilisent un modèle 3D global approximatif, une nouvelle représentation égo-centrée dense est proposée. Cette représentation est composée d'un graphe d'images sphériques augmentées par l'information dense de profondeur (RGB+D), et permet de cartographier de larges environnements. Lors de la localisation en ligne, ce type de modèle apporte toute l'information nécessaire pour une localisation précise dans le voisinage du graphe, et permet de recaler en temps-réel l'image perçue par une caméra embarquée sur un véhicule, avec les images du graphe, en utilisant une technique d'alignement d'images directe. La méthode de localisation proposée, est précise, robuste aux aberrations et prend en compte les changements d'illumination entre le modèle de la base de données et les images perçues par la caméra. Finalement, la précision et la robustesse de la localisation permettent à un véhicule autonome, équipé d'une caméra, de naviguer de façon sure en environnement urbain.

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Villota, Juan Carlos Perafán. "Adaptive registration using 2D and 3D features for indoor scene reconstruction." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-17042017-090901/.

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Анотація:

Pairwise alignment between point clouds is an important task in building 3D maps of indoor environments with partial information. The combination of 2D local features with depth information provided by RGB-D cameras are often used to improve such alignment. However, under varying lighting or low visual texture, indoor pairwise frame registration with sparse 2D local features is not a particularly robust method. In these conditions, features are hard to detect, thus leading to misalignment between consecutive pairs of frames. The use of 3D local features can be a solution as such features come from the 3D points themselves and are resistant to variations in visual texture and illumination. Because varying conditions in real indoor scenes are unavoidable, we propose a new framework to improve the pairwise frame alignment using an adaptive combination of sparse 2D and 3D features based on both the levels of geometric structure and visual texture contained in each scene. Experiments with datasets including unrestricted RGB-D camera motion and natural changes in illumination show that the proposed framework convincingly outperforms methods using 2D or 3D features separately, as reflected in better level of alignment accuracy.
O alinhamento entre pares de nuvens de pontos é uma tarefa importante na construção de mapas de ambientes em 3D. A combinação de características locais 2D com informação de profundidade fornecida por câmeras RGB-D são frequentemente utilizadas para melhorar tais alinhamentos. No entanto, em ambientes internos com baixa iluminação ou pouca textura visual o método usando somente características locais 2D não é particularmente robusto. Nessas condições, as características 2D são difíceis de serem detectadas, conduzindo a um desalinhamento entre pares de quadros consecutivos. A utilização de características 3D locais pode ser uma solução uma vez que tais características são extraídas diretamente de pontos 3D e são resistentes a variações na textura visual e na iluminação. Como situações de variações em cenas reais em ambientes internos são inevitáveis, essa tese apresenta um novo sistema desenvolvido com o objetivo de melhorar o alinhamento entre pares de quadros usando uma combinação adaptativa de características esparsas 2D e 3D. Tal combinação está baseada nos níveis de estrutura geométrica e de textura visual contidos em cada cena. Esse sistema foi testado com conjuntos de dados RGB-D, incluindo vídeos com movimentos irrestritos da câmera e mudanças naturais na iluminação. Os resultados experimentais mostram que a nossa proposta supera aqueles métodos que usam características 2D ou 3D separadamente, obtendo uma melhora da precisão no alinhamento de cenas em ambientes internos reais.

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Shi, Yangyu. "Infrared Imaging Decision Aid Tools for Diagnosis of Necrotizing Enterocolitis." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40714.

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Neonatal necrotizing enterocolitis (NEC) is one of the most severe digestive tract emergencies in neonates, involving bowel edema, hemorrhage, and necrosis, and can lead to serious complications including death. Since it is difficult to diagnose early, the morbidity and mortality rates are high due to severe complications in later stages of NEC and thus early detection is key to the treatment of NEC. In this thesis, a novel automatic image acquisition and analysis system combining a color and depth (RGB-D) sensor with an infrared (IR) camera is proposed for NEC diagnosis. A design for sensors configuration and a data acquisition process are introduced. A calibration method between the three cameras is described which aims to ensure frames synchronization and observation consistency among the color, depth, and IR images. Subsequently, complete segmentation procedures based on the original color, depth, and IR information are proposed to automatically separate the human body from the background, remove other interfering items, identify feature points on the human body joints, distinguish the human torso and limbs, and extract the abdominal region of interest. Finally, first-order statistical analysis is performed on thermal data collected over the entire extracted abdominal region to compare differences in thermal data distribution between different patient groups. Experimental validation in a real clinical environment is reported and shows encouraging results.

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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.

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La malnutrition, qu'elle soit liée à un apport insuffisant ou excessif en nutriments, représente un défi mondial de santé publique touchant des milliards de personnes. Elle affecte tous les systèmes organiques en étant un facteur majeur de risque pour les maladies non transmissibles telles que les maladies cardiovasculaires, le diabète et certains cancers. Évaluer l'apport alimentaire est crucial pour prévenir la malnutrition, mais cela reste un défi. Les méthodes traditionnelles d'évaluation alimentaire sont laborieuses et sujettes aux biais. Les avancées en IA ont permis la conception de VBDA, solution prometteuse pour analyser automatiquement les images alimentaires afin d'estimer les portions et la composition nutritionnelle. Cependant, la segmentation des images alimentaires dans un VBDA rencontre des difficultés en raison de la structure non rigide des aliments, de la variation intra-classe élevée (où le même type d'aliment peut apparaître très différent), de la ressemblance inter-classe (où différents types d'aliments semblent visuellement très similaires) et de la rareté des ensembles de données disponibles publiquement.Presque toutes les recherches sur la segmentation alimentaire se sont concentrées sur les aliments asiatiques et occidentaux, en l'absence de bases de données pour les cuisines africaines. Cependant, les plats africains impliquent souvent des classes alimentaires mélangées, rendant la segmentation précise difficile. De plus, la recherche s'est largement concentrée sur les images RGB, qui fournissent des informations sur la couleur et la texture mais pourraient manquer de suffisamment de détails géométriques. Pour y remédier, la segmentation RGB-D combine des données de profondeur avec des images RGB. Les images de profondeur fournissent des détails géométriques cruciaux qui enrichissent les données RGB, améliorent la discrimination des objets et sont robustes face à des facteurs tels que l'illumination et le brouillard. Malgré son succès dans d'autres domaines, la segmentation RGB-D pour les aliments est peu explorée en raison des difficultés à collecter des images de profondeur des aliments.Cette thèse apporte des contributions clés en développant de nouveaux modèles d'apprentissage profond pour la segmentation d'images RGB (mid-DeepLabv3+) et RGB-D (ESeNet-D) et en introduisant les premiers ensembles de données axés sur les images alimentaires africaines. Mid-DeepLabv3+ est basé sur DeepLabv3+, avec un backbone ResNet simplifié et une couche de saut (middle layer) ajoutée dans le décodeur, ainsi que des couches mécanisme d'attention SimAM. Ce model offre un excellent compromis entre performance et efficacité computationnelle. ESeNet-D est composé de deux branches d'encodeurs utilisant EfficientNetV2 comme backbone, avec un bloc de fusion pour l'intégration multi-échelle et un décodeur employant des convolutions auto-calibrée et interpolations entrainées pour une segmentation précise. ESeNet-D surpasse de nombreux modèles de référence RGB et RGB-D tout en ayant une charge computationnelle plus faible. Nos expériences ont montré que, lorsqu'elles sont correctement intégrées, les informations relatives à la profondeur peuvent améliorer de manière significative la précision de la segmentation des images alimentaires.Nous présentons également deux nouvelles bases de données : AfricaFoodSeg pour la segmentation « aliment/non-aliment » avec 3067 images (2525 pour l'entraînement, 542 pour la validation), et CamerFood, axée sur la cuisine camerounaise. Les ensembles de données CamerFood comprennent CamerFood10 avec 1422 images et dix classes alimentaires, et CamerFood15, une version améliorée avec 15 classes alimentaires, 1684 images d'entraînement et 514 images de validation. Enfin, nous abordons le défi des données de profondeur rares dans la segmentation RGB-D des aliments en démontrant que les modèles MDE peuvent aider à générer des cartes de profondeur efficaces pour les ensembles de données RGB-D
Malnutrition, 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

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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.

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L'accès aux séquences d'images 3D s'est aujourd'hui démocratisé, grâce aux récentes avancées dans le développement des capteurs de profondeur ainsi que des méthodes permettant de manipuler des informations 3D à partir d'images 2D. De ce fait, il y a une attente importante de la part de la communauté scientifique de la vision par ordinateur dans l'intégration de l'information 3D. En effet, des travaux de recherche ont montré que les performances de certaines applications pouvaient être améliorées en intégrant l'information 3D. Cependant, il reste des problèmes à résoudre pour l'analyse et la segmentation de scènes intérieures comme (a) comment l'information 3D peut-elle être exploitée au mieux ? et (b) quelle est la meilleure manière de prendre en compte de manière conjointe les informations couleur et 3D ? Nous abordons ces deux questions dans cette thèse et nous proposons de nouvelles méthodes non supervisées pour la classification d'images 3D et la segmentation prenant en compte de manière conjointe les informations de couleur et de profondeur. A cet effet, nous formulons l'hypothèse que les normales aux surfaces dans les images 3D sont des éléments à prendre en compte pour leur analyse, et leurs distributions sont modélisables à l'aide de lois de mélange. Nous utilisons la méthode dite « Bregman Soft Clustering » afin d'être efficace d'un point de vue calculatoire. De plus, nous étudions plusieurs lois de probabilités permettant de modéliser les distributions de directions : la loi de von Mises-Fisher et la loi de Watson. Les méthodes de classification « basées modèles » proposées sont ensuite validées en utilisant des données de synthèse puis nous montrons leur intérêt pour l'analyse des images 3D (ou de profondeur). Une nouvelle méthode de segmentation d'images couleur et profondeur, appelées aussi images RGB-D, exploitant conjointement la couleur, la position 3D, et la normale locale est alors développée par extension des précédentes méthodes et en introduisant une méthode statistique de fusion de régions « planes » à l'aide d'un graphe. Les résultats montrent que la méthode proposée donne des résultats au moins comparables aux méthodes de l'état de l'art tout en demandant moins de temps de calcul. De plus, elle ouvre des perspectives nouvelles pour la fusion non supervisée des informations de couleur et de géométrie. Nous sommes convaincus que les méthodes proposées dans cette thèse pourront être utilisées pour la classification d'autres types de données comme la parole, les données d'expression en génétique, etc. Elles devraient aussi permettre la réalisation de tâches complexes comme l'analyse conjointe de données contenant des images et de la parole
Access 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

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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.

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With the release of the Kinect device new possibilities appeared, allowing a simple use of image depth in image processing. The aim of this thesis is to propose a method for object detection and recognition in a depth map. Well known method Bag of Words and a descriptor based on Spin Image method are used for the object recognition. The Spin Image method is one of several existing approaches to depth map which are described in this thesis. Detection of object in picture is ensured by the sliding window technique. That is improved and speeded up by utilization of the depth information.

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Книги з теми "RGB-D Image"

Rosin, Paul L., Yu-Kun Lai, Ling Shao, and Yonghuai Liu, eds. RGB-D Image Analysis and Processing. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28603-3.

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Rosin, Paul L., Yonghuai Liu, Ling Shao, and Yu-Kun Lai. RGB-D Image Analysis and Processing. Springer International Publishing AG, 2020.

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RGB-D Image Analysis and Processing. Springer, 2019.

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Kohli, Pushmeet, Zhengyou Zhang, Ling Shao, and Jungong Han. Computer Vision and Machine Learning with RGB-D Sensors. Springer, 2014.

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Kohli, Pushmeet, Zhengyou Zhang, Ling Shao, and Jungong Han. Computer Vision and Machine Learning with RGB-D Sensors. Springer, 2016.

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Computer Vision and Machine Learning with RGB-D Sensors. Springer, 2014.

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Hester, Desirae. Picture Book of dσdge Chαrgєrs: An Album Consist of Compelling Photos of dσdge Chαrgєrs with High Quality Images As a Special Gift for Friends, Family, Lovers, Relative. Independently Published, 2022.

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Частини книг з теми "RGB-D Image"

Civera, Javier, and Seong Hun Lee. "RGB-D Odometry and SLAM." In RGB-D Image Analysis and Processing, 117–44. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28603-3_6.

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Zollhöfer, Michael. "Commodity RGB-D Sensors: Data Acquisition." In RGB-D Image Analysis and Processing, 3–13. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28603-3_1.

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Malleson, Charles, Jean-Yves Guillemaut, and Adrian Hilton. "3D Reconstruction from RGB-D Data." In RGB-D Image Analysis and Processing, 87–115. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28603-3_5.

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Ren, Tongwei, and Ao Zhang. "RGB-D Salient Object Detection: A Review." In RGB-D Image Analysis and Processing, 203–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28603-3_9.

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Cong, Runmin, Hao Chen, Hongyuan Zhu, and Huazhu Fu. "Foreground Detection and Segmentation in RGB-D Images." In RGB-D Image Analysis and Processing, 221–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28603-3_10.

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Sahin, Caner, Guillermo Garcia-Hernando, Juil Sock, and Tae-Kyun Kim. "Instance- and Category-Level 6D Object Pose Estimation." In RGB-D Image Analysis and Processing, 243–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28603-3_11.

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Zhang, Song-Hai, and Yu-Kun Lai. "Geometric and Semantic Modeling from RGB-D Data." In RGB-D Image Analysis and Processing, 267–82. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28603-3_12.

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Schwarz, Max, and Sven Behnke. "Semantic RGB-D Perception for Cognitive Service Robots." In RGB-D Image Analysis and Processing, 285–307. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28603-3_13.

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Spinsante, Susanna. "RGB-D Sensors and Signal Processing for Fall Detection." In RGB-D Image Analysis and Processing, 309–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28603-3_14.

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Moyà-Alcover, Gabriel, Ines Ayed, Javier Varona, and Antoni Jaume-i-Capó. "RGB-D Interactive Systems on Serious Games for Motor Rehabilitation Therapy and Therapeutic Measurements." In RGB-D Image Analysis and Processing, 335–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28603-3_15.

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Тези доповідей конференцій з теми "RGB-D Image"

Teng, Qianqian, and Xianbo He. "RGB-D Image Modeling Method Based on Transformer: RDT." In 2024 3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC), 386–89. IEEE, 2024. http://dx.doi.org/10.1109/aiotc63215.2024.10748282.

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Wang, Kexuan, Chenhua Liu, Huiguang Wei, Li Jing, and Rongfu Zhang. "RFNET: Refined Fusion Three-Branch RGB-D Salient Object Detection Network." In 2024 IEEE International Conference on Image Processing (ICIP), 741–46. IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647308.

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Fouad, Islam I., Sherine Rady, and Mostafa G. M. Mostafa. "Efficient image segmentation of RGB-D images." In 2017 12th International Conference on Computer Engineering and Systems (ICCES). IEEE, 2017. http://dx.doi.org/10.1109/icces.2017.8275331.

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Li, Shijie, Rong Li, and Juergen Gall. "Semantic RGB-D Image Synthesis." In 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2023. http://dx.doi.org/10.1109/iccvw60793.2023.00101.

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Zhang, Xiaoxiong, Sajid Javed, Ahmad Obeid, Jorge Dias, and Naoufel Werghi. "Gender Recognition on RGB-D Image." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9191068.

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Zhang, Shaopeng, Ming Zhong, Gang Zeng, and Rui Gan. "Joining geometric and RGB features for RGB-D semantic segmentation." In The Second International Conference on Image, Video Processing and Artificial Intelligence, edited by Ruidan Su. SPIE, 2019. http://dx.doi.org/10.1117/12.2541645.

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Li, Benchao, Wanhua Li, Yongyi Tang, Jian-Fang Hu, and Wei-Shi Zheng. "GL-PAM RGB-D Gesture Recognition." In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451157.

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Shibata, Toshihiro, Yuji Akai, and Ryo Matsuoka. "Reflection Removal Using RGB-D Images." In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451639.

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Valognes, Julien, Maria A. Amer, and Niloufar Salehi Dastjerdi. "Effective keyframe extraction from RGB and RGB-D video sequences." In 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2017. http://dx.doi.org/10.1109/ipta.2017.8310120.

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Hui, 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.

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