Journal articles: 'RGBZ'

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Richardt, Christian, Carsten Stoll, Neil A. Dodgson, Hans-Peter Seidel, and Christian Theobalt. "Coherent Spatiotemporal Filtering, Upsampling and Rendering of RGBZ Videos." Computer Graphics Forum 31, no. 2pt1 (2012): 247–56. http://dx.doi.org/10.1111/j.1467-8659.2012.03003.x.

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Clouet, Axel, Jérôme Vaillant, and David Alleysson. "The Geometry of Noise in Color and Spectral Image Sensors." Sensors 20, no. 16 (2020): 4487. http://dx.doi.org/10.3390/s20164487.

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Digital images are always affected by noise and the reduction of its impact is an active field of research. Noise due to random photon fall onto the sensor is unavoidable but could be amplified by the camera image processing such as in the color correction step. Color correction is expressed as the combination of a spectral estimation and a computation of color coordinates in a display color space. Then we use geometry to depict raw, spectral and color signals and noise. Geometry is calibrated on the physics of image acquisition and spectral characteristics of the sensor to study the impact of the sensor space metric on noise amplification. Since spectral channels are non-orthogonal, we introduce the contravariant signal to noise ratio for noise evaluation at spectral reconstruction level. Having definitions of signal to noise ratio for each steps of spectral or color reconstruction, we compare performances of different types of sensors (RGB, RGBW, RGBWir, CMY, RYB, RGBC).

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Huang, Chengqiang, Qi Zhang, Hui Wang, and Songlin Feng. "An RGB to RGBG conversion algorithm for AMOLED panel." Journal of the Society for Information Display 25, no. 5 (2017): 302–11. http://dx.doi.org/10.1002/jsid.552.

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Lin, Yi-Tun, and Graham D. Finlayson. "Physically Plausible Spectral Reconstruction." Sensors 20, no. 21 (2020): 6399. http://dx.doi.org/10.3390/s20216399.

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Spectral reconstruction algorithms recover spectra from RGB sensor responses. Recent methods—with the very best algorithms using deep learning—can already solve this problem with good spectral accuracy. However, the recovered spectra are physically incorrect in that they do not induce the RGBs from which they are recovered. Moreover, if the exposure of the RGB image changes then the recovery performance often degrades significantly—i.e., most contemporary methods only work for a fixed exposure. In this paper, we develop a physically accurate recovery method: the spectra we recover provably induce the same RGBs. Key to our approach is the idea that the set of spectra that integrate to the same RGB can be expressed as the sum of a unique fundamental metamer (spanned by the camera’s spectral sensitivities and linearly related to the RGB) and a linear combination of a vector space of metameric blacks (orthogonal to the spectral sensitivities). Physically plausible spectral recovery resorts to finding a spectrum that adheres to the fundamental metamer plus metameric black decomposition. To further ensure spectral recovery that is robust to changes in exposure, we incorporate exposure changes in the training stage of the developed method. In experiments we evaluate how well the methods recover spectra and predict the actual RGBs and RGBs under different viewing conditions (changing illuminations and/or cameras). The results show that our method generally improves the state-of-the-art spectral recovery (with more stabilized performance when exposure varies) and provides zero colorimetric error. Moreover, our method significantly improves the color fidelity under different viewing conditions, with up to a 60% reduction in some cases.

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Murdoch, Michael J., and Michael E. Miller. "52.4: Distinguished Paper: RGB to RGBW Conversion for OLED Displays." SID Symposium Digest of Technical Papers 39, no. 1 (2008): 791. http://dx.doi.org/10.1889/1.3069788.

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Miller, Michael E., and Michael J. Murdoch. "RGB-to-RGBW conversion with current limiting for OLED displays." Journal of the Society for Information Display 17, no. 3 (2009): 195. http://dx.doi.org/10.1889/jsid17.3.195.

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Miller, Michael E., and Michael J. Murdoch. "RGB-to-RGBW conversion with current limiting for OLED displays." Information Display 25, no. 3 (2009): 27. http://dx.doi.org/10.1002/j.2637-496x.2009.tb00067.x.

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Li, Siqi, Changqing Zou, Yipeng Li, Xibin Zhao, and Yue Gao. "Attention-Based Multi-Modal Fusion Network for Semantic Scene Completion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11402–9. http://dx.doi.org/10.1609/aaai.v34i07.6803.

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This paper presents an end-to-end 3D convolutional network named attention-based multi-modal fusion network (AMFNet) for the semantic scene completion (SSC) task of inferring the occupancy and semantic labels of a volumetric 3D scene from single-view RGB-D images. Compared with previous methods which use only the semantic features extracted from RGB-D images, the proposed AMFNet learns to perform effective 3D scene completion and semantic segmentation simultaneously via leveraging the experience of inferring 2D semantic segmentation from RGB-D images as well as the reliable depth cues in spatial dimension. It is achieved by employing a multi-modal fusion architecture boosted from 2D semantic segmentation and a 3D semantic completion network empowered by residual attention blocks. We validate our method on both the synthetic SUNCG-RGBD dataset and the real NYUv2 dataset and the results show that our method respectively achieves the gains of 2.5% and 2.6% on the synthetic SUNCG-RGBD dataset and the real NYUv2 dataset against the state-of-the-art method.

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Huang, Chengqiang, Ming Xia, Guangjun Xu, Hui Wang, and Songlin Feng. "RGB-to-RGBG conversion by direct assignment with accurate feedback correction." Australian Journal of Electrical and Electronics Engineering 17, no. 4 (2020): 291–302. http://dx.doi.org/10.1080/1448837x.2020.1862388.

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Cong, Runmin, Jianjun Lei, Huazhu Fu, Junhui Hou, Qingming Huang, and Sam Kwong. "Going From RGB to RGBD Saliency: A Depth-Guided Transformation Model." IEEE Transactions on Cybernetics 50, no. 8 (2020): 3627–39. http://dx.doi.org/10.1109/tcyb.2019.2932005.

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Chung, Kuo-Liang, Tzu-Hsien Chan, and Szu-Ni Chen. "Effective Three-Stage Demosaicking Method for RGBW CFA Images Using The Iterative Error-Compensation Based Approach." Sensors 20, no. 14 (2020): 3908. http://dx.doi.org/10.3390/s20143908.

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As the color filter array (CFA)2.0, the RGBW CFA pattern, in which each CFA pixel contains only one R, G, B, or W color value, provides more luminance information than the Bayer CFA pattern. Demosaicking RGBW CFA images I R G B W is necessary in order to provide high-quality RGB full-color images as the target images for human perception. In this letter, we propose a three-stage demosaicking method for I R G B W . In the first-stage, a cross shape-based color difference approach is proposed in order to interpolate the missing W color pixels in the W color plane of I R G B W . In the second stage, an iterative error compensation-based demosaicking process is proposed to improve the quality of the demosaiced RGB full-color image. In the third stage, taking the input image I R G B W as the ground truth RGBW CFA image, an I R G B W -based refinement process is proposed to refine the quality of the demosaiced image obtained by the second stage. Based on the testing RGBW images that were collected from the Kodak and IMAX datasets, the comprehensive experimental results illustrated that the proposed three-stage demosaicking method achieves substantial quality and perceptual effect improvement relative to the previous method by Hamilton and Compton and the two state-of-the-art methods, Kwan et al.’s pansharpening-based method, and Kwan and Chou’s deep learning-based method.

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Hach, Thomas, and Tamara Seybold. "Spatio-Temporal Denoising for Depth Map Sequences." International Journal of Multimedia Data Engineering and Management 7, no. 2 (2016): 21–35. http://dx.doi.org/10.4018/ijmdem.2016040102.

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This paper proposes a novel strategy for depth video denoising in RGBD camera systems. Depth map sequences obtained by state-of-the-art Time-of-Flight sensors suffer from high temporal noise. Hence, all high-level RGB video renderings based on the accompanied depth maps' 3D geometry like augmented reality applications will have severe temporal flickering artifacts. The authors approached this limitation by decoupling depth map upscaling from the temporal denoising step. Thereby, denoising is processed on raw pixels including uncorrelated pixel-wise noise distributions. The authors' denoising methodology utilizes joint sparse 3D transform-domain collaborative filtering. Therein, they extract RGB texture information to yield a more stable and accurate highly sparse 3D depth block representation for the consecutive shrinkage operation. They show the effectiveness of our method on real RGBD camera data and on a publicly available synthetic data set. The evaluation reveals that the authors' method is superior to state-of-the-art methods. Their method delivers flicker-free depth video streams for future applications.

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Li, Liming, Shuguang Zhao, Rui Sun, et al. "AFI-Net: Attention-Guided Feature Integration Network for RGBD Saliency Detection." Computational Intelligence and Neuroscience 2021 (March 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/8861446.

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This article proposes an innovative RGBD saliency model, that is, attention-guided feature integration network, which can extract and fuse features and perform saliency inference. Specifically, the model first extracts multimodal and level deep features. Then, a series of attention modules are deployed to the multilevel RGB and depth features, yielding enhanced deep features. Next, the enhanced multimodal deep features are hierarchically fused. Lastly, the RGB and depth boundary features, that is, low-level spatial details, are added to the integrated feature to perform saliency inference. The key points of the AFI-Net are the attention-guided feature enhancement and the boundary-aware saliency inference, where the attention module indicates salient objects coarsely, and the boundary information is used to equip the deep feature with more spatial details. Therefore, salient objects are well characterized, that is, well highlighted. The comprehensive experiments on five challenging public RGBD datasets clearly exhibit the superiority and effectiveness of the proposed AFI-Net.

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Kwak, 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 (2020): 1142. http://dx.doi.org/10.3390/rs12071142.

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To provide a realistic environment for remote sensing applications, point clouds are used to realize a three-dimensional (3D) digital world for the user. Motion recognition of objects, e.g., humans, is required to provide realistic experiences in the 3D digital world. To recognize a user’s motions, 3D landmarks are provided by analyzing a 3D point cloud collected through a light detection and ranging (LiDAR) system or a red green blue (RGB) image collected visually. However, manual supervision is required to extract 3D landmarks as to whether they originate from the RGB image or the 3D point cloud. Thus, there is a need for a method for extracting 3D landmarks without manual supervision. Herein, an RGB image and a 3D point cloud are used to extract 3D landmarks. The 3D point cloud is utilized as the relative distance between a LiDAR and a user. Because it cannot contain all information the user’s entire body due to disparities, it cannot generate a dense depth image that provides the boundary of user’s body. Therefore, up-sampling is performed to increase the density of the depth image generated based on the 3D point cloud; the density depends on the 3D point cloud. This paper proposes a system for extracting 3D landmarks using 3D point clouds and RGB images without manual supervision. A depth image provides the boundary of a user’s motion and is generated by using 3D point cloud and RGB image collected by a LiDAR and an RGB camera, respectively. To extract 3D landmarks automatically, an encoder–decoder model is trained with the generated depth images, and the RGB images and 3D landmarks are extracted from these images with the trained encoder model. The method of extracting 3D landmarks using RGB depth (RGBD) images was verified experimentally, and 3D landmarks were extracted to evaluate the user’s motions with RGBD images. In this manner, landmarks could be extracted according to the user’s motions, rather than by extracting them using the RGB images. The depth images generated by the proposed method were 1.832 times denser than the up-sampling-based depth images generated with bilateral filtering.

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Wang, Liangju, Yunhong Duan, Libo Zhang, Tanzeel U. Rehman, Dongdong Ma, and Jian Jin. "Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants." Sensors 20, no. 11 (2020): 3208. http://dx.doi.org/10.3390/s20113208.

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The normalized difference vegetation index (NDVI) is widely used in remote sensing to monitor plant growth and chlorophyll levels. Usually, a multispectral camera (MSC) or hyperspectral camera (HSC) is required to obtain the near-infrared (NIR) and red bands for calculating NDVI. However, these cameras are expensive, heavy, difficult to geo-reference, and require professional training in imaging and data processing. On the other hand, the RGBN camera (NIR sensitive RGB camera, simply modified from standard RGB cameras by removing the NIR rejection filter) have also been explored to measure NDVI, but the results did not exactly match the NDVI from the MSC or HSC solutions. This study demonstrates an improved NDVI estimation method with an RGBN camera-based imaging system (Ncam) and machine learning algorithms. The Ncam consisted of an RGBN camera, a filter, and a microcontroller with a total cost of only $70 ~ 85. This new NDVI estimation solution was compared with a high-end hyperspectral camera in an experiment with corn plants under different nitrogen and water treatments. The results showed that the Ncam with two-band-pass filter achieved high performance (R2 = 0.96, RMSE = 0.0079) at estimating NDVI with the machine learning model. Additional tests showed that besides NDVI, this low-cost Ncam was also capable of predicting corn plant nitrogen contents precisely. Thus, Ncam is a potential option for MSC and HSC in plant phenotyping projects.

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Kyung Joon Kwon and Young Hwan Kim. "Scene-Adaptive RGB-to-RGBW Conversion Using Retinex Theory-Based Color Preservation." Journal of Display Technology 8, no. 12 (2012): 684–94. http://dx.doi.org/10.1109/jdt.2012.2215954.

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Lee, Chul, and Vishal Monga. "Power-Constrained RGB-to-RGBW Conversion for Emissive Displays: Optimization-Based Approaches." IEEE Transactions on Circuits and Systems for Video Technology 26, no. 10 (2016): 1821–34. http://dx.doi.org/10.1109/tcsvt.2015.2475915.

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Deng, Fei, and Yan Xiong. "Analysis of Novel OLED Based Four-Primary Displays." Applied Mechanics and Materials 541-542 (March 2014): 482–86. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.482.

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Novel four-primary displays utilizing organic light-emitting devices (OLEDs) was analyzed theoretically and experimentally. Using the fitting curves of the OLEDs electroluminescent (EL) characteristics, RGBW four-primary display is clearly shown to be more energy efficient than traditional RGB three-primary display, especially as the efficiency of the OLEDs is getting higher.

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Mohajerani, Sorour, Mark S. Drew, and Parvaneh Saeedi. "Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal." London Imaging Meeting 2020, no. 1 (2020): 82–86. http://dx.doi.org/10.2352/issn.2694-118x.2020.lim-06.

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Removing the effect of illumination variation in images has been proved to be beneficial in many computer vision applications such as object recognition and semantic segmentation. Although generating illumination-invariant images has been studied in the literature before, it has not been investigated on real 4-channel (4D) data. In this study, we examine the quality of illumination-invariant images generated from red, green, blue, and near-infrared (RGBN) data. Our experiments show that the near-infrared channel substantively contributes toward removing illumination. As shown in our numerical and visual results, the illumination-invariant image obtained by RGBN data is superior compared to that obtained by RGB alone.

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Sun, Xinyan, Zhenye Li, Tingting Zhu, and Chao Ni. "Four-Dimension Deep Learning Method for Flower Quality Grading with Depth Information." Electronics 10, no. 19 (2021): 2353. http://dx.doi.org/10.3390/electronics10192353.

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Grading the quality of fresh cut flowers is an important practice in the flower industry. Based on the flower maturing status, a classification method based on deep learning and depth information was proposed for the grading of flower quality. Firstly, the RGB image and the depth image of a flower bud were collected and transformed into fused RGBD information. Then, the RGBD information of a flower was set as inputs of a convolutional neural network to determine the flower bud maturing status. Four convolutional neural network models (VGG16, ResNet18, MobileNetV2, and InceptionV3) were adjusted for a four-dimensional (4D) RGBD input to classify flowers, and their classification performances were compared with and without depth information. The experimental results show that the classification accuracy was improved with depth information, and the improved InceptionV3 network with RGBD achieved the highest classification accuracy (up to 98%), which means that the depth information can effectively reflect the characteristics of the flower bud and is helpful for the classification of the maturing status. These results have a certain significance for the intelligent classification and sorting of fresh flowers.

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Kim, Hansol, Sukho Lee, and Moon Gi Kang. "Demosaicing of RGBW Color Filter Array Based on Rank Minimization with Colorization Constraint." Sensors 20, no. 16 (2020): 4458. http://dx.doi.org/10.3390/s20164458.

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Recently, the white (w) channel has been incorporated in various forms into color filter arrays (CFAs). The advantage of using theWchannel is thatWpixels have less noise than red (R), green (G), or blue (B) (RGB) pixels; therefore, under low-light conditions, pixels with high fidelity can be obtained. However, RGBW CFAs normally suffer from spatial resolution degradation due to a smaller number of color pixels than in RGB CFAs. Therefore, even though the reconstructed colors have higher sensitivity, which results in larger Color Peak Signal-to-Noise Ratio (CPSNR) values, there are some color aliasing artifacts due to a low resolution. In this paper, we propose a rank minimization-based color interpolation method with a colorization constraint for the RGBW format with a large number ofWpixels. The rank minimization can achieve a broad interpolation and preserve the structure in the image, and it thereby eliminates the color artifacts. However, the colors fade from this global process. Therefore, we further incorporate a colorization constraint into the rank minimization process for the better reproduction of the colors. The experimental results show that the images can be reconstructed well, even from noisy pattern images obtained under low-light conditions.

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HUANG Cheng-qiang, 黄成强, 杨友昌 YOU You-chang, 贺娟 LI Tian-hua, 李天华 HE Juan, and 罗德莲 LUO De-lian. "RGB to RGBG conversion algorithm based on weighting factors and related FPGA realization." Chinese Journal of Liquid Crystals and Displays 32, no. 7 (2017): 572–79. http://dx.doi.org/10.3788/yjyxs20173207.0572.

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Kim, Sunwoong, and Hyuk-Jae Lee. "Optimized Interpolation and Cached Data Access in LUT-Based RGB-to-RGBW Conversion." IEEE Transactions on Circuits and Systems II: Express Briefs 65, no. 7 (2018): 943–47. http://dx.doi.org/10.1109/tcsii.2017.2740358.

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Lian, Dongze, Ziheng Zhang, Weixin Luo, et al. "RGBD Based Gaze Estimation via Multi-Task CNN." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2488–95. http://dx.doi.org/10.1609/aaai.v33i01.33012488.

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This paper tackles RGBD based gaze estimation with Convolutional Neural Networks (CNNs). Specifically, we propose to decompose gaze point estimation into eyeball pose, head pose, and 3D eye position estimation. Compared with RGB image-based gaze tracking, having depth modality helps to facilitate head pose estimation and 3D eye position estimation. The captured depth image, however, usually contains noise and black holes which noticeably hamper gaze tracking. Thus we propose a CNN-based multi-task learning framework to simultaneously refine depth images and predict gaze points. We utilize a generator network for depth image generation with a Generative Neural Network (GAN), where the generator network is partially shared by both the gaze tracking network and GAN-based depth synthesizing. By optimizing the whole network simultaneously, depth image synthesis improves gaze point estimation and vice versa. Since the only existing RGBD dataset (EYEDIAP) is too small, we build a large-scale RGBD gaze tracking dataset for performance evaluation. As far as we know, it is the largest RGBD gaze dataset in terms of the number of participants. Comprehensive experiments demonstrate that our method outperforms existing methods by a large margin on both our dataset and the EYEDIAP dataset.

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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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Kanda, Takuya, Kazuya Miyakawa, Jeonghwang Hayashi, et al. "Locating Mechanical Switches Using RGB-D Sensor Mounted on a Disaster Response Robot." Electronic Imaging 2020, no. 6 (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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Zhang, Wenli, Xiang Guo, Jiaqi Wang, Ning Wang, and Kaizhen Chen. "Asymmetric Adaptive Fusion in a Two-Stream Network for RGB-D Human Detection." Sensors 21, no. 3 (2021): 916. http://dx.doi.org/10.3390/s21030916.

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In recent years, human detection in indoor scenes has been widely applied in smart buildings and smart security, but many related challenges can still be difficult to address, such as frequent occlusion, low illumination and multiple poses. This paper proposes an asymmetric adaptive fusion two-stream network (AAFTS-net) for RGB-D human detection. This network can fully extract person-specific depth features and RGB features while reducing the typical complexity of a two-stream network. A depth feature pyramid is constructed by combining contextual information, with the motivation of combining multiscale depth features to improve the adaptability for targets of different sizes. An adaptive channel weighting (ACW) module weights the RGB-D feature channels to achieve efficient feature selection and information complementation. This paper also introduces a novel RGB-D dataset for human detection called RGBD-human, on which we verify the performance of the proposed algorithm. The experimental results show that AAFTS-net outperforms existing state-of-the-art methods and can maintain stable performance under conditions of frequent occlusion, low illumination and multiple poses.

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Iwaszczuk, D., Z. Koppanyi, N. A. Gard, B. Zha, C. Toth, and A. Yilmaz. "SEMANTIC LABELING OF STRUCTURAL ELEMENTS IN BUILDINGS BY FUSING RGB AND DEPTH IMAGES IN AN ENCODER-DECODER CNN FRAMEWORK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 225–32. http://dx.doi.org/10.5194/isprs-archives-xlii-1-225-2018.

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Abstract. In the last decade, we have observed an increasing demand for indoor scene modeling in various applications, such as mobility inside buildings, emergency and rescue operations, and maintenance. Automatically distinguishing between structural elements of buildings, such as walls, ceilings, floors, windows, doors etc., and typical objects in buildings, such as chairs, tables and shelves, is particularly important for many reasons, such as 3D building modeling or navigation. This information can be generally retrieved through semantic labeling. In the past few years, convolutional neural networks (CNN) have become the preferred method for semantic labeling. Furthermore, there is ongoing research on fusing RGB and depth images in CNN frameworks. For pixel-level labeling, encoder-decoder CNN frameworks have been shown to be the most effective. In this study, we adopt an encoder-decoder CNN architecture to label structural elements in buildings and investigate the influence of using depth information on the detection of typical objects in buildings. For this purpose, we have introduced an approach to combine depth map with RGB images by changing the color space of the original image to HSV and then substitute the V channel with the depth information (D) and use it utilize it in the CNN architecture. As further variation of this approach, we also transform back the HSD images to RGB color space and use them within the CNN. This approach allows for using a CNN, designed for three-channel image input, and directly comparing our results with RGB-based labeling within the same network. We perform our tests using the Stanford 2D-3D-Semantics Dataset (2D-3D-S), a widely used indoor dataset. Furthermore, we compare our approach with results when using four-channel input created by stacking RGB and depth (RGBD). Our investigation shows that fusing RGB and depth improves results on semantic labeling; particularly, on structural elements of buildings. On the 2D- 3D-S dataset, we achieve up to 92.1% global accuracy, compared to 90.9% using RGB only and 93.6% using RGBD. Moreover, the scores of Intersection over Union metric have improved using depth, which shows that it gives better labeling results at the boundaries.

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Salaris, Maurizio, Chris Usher, Silvia Martocchia, et al. "Photometric characterization of multiple populations in star clusters: the impact of the first dredge-up." Monthly Notices of the Royal Astronomical Society 492, no. 3 (2020): 3459–64. http://dx.doi.org/10.1093/mnras/staa089.

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ABSTRACT The existence of star-to-star light-element abundance variations (multiple populations, MPs) in massive Galactic and extragalactic star clusters older than about 2 Gyr is by now well established. Photometry of red giant branch (RGB) stars has been and still is instrumental in enabling the detection and characterization of cluster MPs, through the appropriate choices of filters, colours, and colour combinations that are mainly sensitive to N and – to a lesser degree – C stellar surface abundances. An important issue not yet properly addressed is that the translation of the observed widths of the cluster RGBs to abundance spreads must account for the effect of the first dredge-up on the surface chemical patterns, hence on the spectral energy distributions of stars belonging to the various MPs. We have filled this gap by studying theoretically the impact of the dredge-up on the predicted widths of RGBs in clusters hosting MPs. We find that for a given initial range of N abundances, the first dredge-up reduces the predicted RGB widths in N-sensitive filters compared to the case when its effect on the stellar spectral energy distributions is not accounted for. This reduction is a strong function of age and has also a dependence on metallicity. The net effect is an underestimate of the initial N-abundance ranges from RGB photometry if the first dredge-up is not accounted for in the modelling, and also the potential determination of spurious trends of N-abundance spreads with age.

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Zhao, Long, Meng Zhu, Honge Ren, and Lingjixuan Xue. "Channel Exchanging for RGB-T Tracking." Sensors 21, no. 17 (2021): 5800. http://dx.doi.org/10.3390/s21175800.

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It is difficult to achieve all-weather visual object tracking in an open environment only utilizing single modality data input. Due to the complementarity of RGB and thermal infrared (TIR) data in various complex environments, a more robust object tracking framework can be obtained using video data of these two modalities. The fusion methods of RGB and TIR data are the core elements to determine the performance of the RGB-T object tracking method, and the existing RGB-T trackers have not solved this problem well. In order to solve the current low utilization of information intra single modality in aggregation-based methods and between two modalities in alignment-based methods, we used DiMP as the baseline tracker to design an RGB-T object tracking framework channel exchanging DiMP (CEDiMP) based on channel exchanging. CEDiMP achieves dynamic channel exchanging between sub-networks of different modes hardly adding any parameters during the feature fusion process. The expression ability of the deep features generated by our data fusion method based on channel exchanging is stronger. At the same time, in order to solve the poor generalization ability of the existing RGB-T object tracking methods and the poor ability in the long-term object tracking, more training of CEDiMP on the synthetic dataset LaSOT-RGBT is added. A large number of experiments demonstrate the effectiveness of the proposed model. CEDiMP achieves the best performance on two RGB-T object tracking benchmark datasets, GTOT and RGBT234, and performs outstandingly in the generalization testing.

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Kim, Ella L., Anton Buzdin, Maxim Sorokin, et al. "RNA-sequencing and bioinformatic analysis to pre-assess sensitivity to targeted therapeutics in recurrent glioblastoma." Journal of Clinical Oncology 37, no. 15_suppl (2019): e13533-e13533. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e13533.

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e13533 Background: This study developed molecular guided tools for individualized selection of chemotherapeutics for recurrent glioblastoma (rGB). A consortium involving clinical neurooncologists, molecular biologists and bioinformaticians identified gene expression patterns in rGB and quantitatively analyzed pathways involved in response to FDA approved oncodrugs. Methods: From2016 to 2018 biopsies from GB were collected using a multisampling approach. Biopsy material was used to isolate glioma stem-like cells and examined by RNA-sequencing. RNA-seq results were subjected to differential expression (DE) analysis and Oncobox analysis – a bioinformatic tool for quantitative pathway activation analysis. Results for newly diagnosed (nGB) and rGB (tissue samples and cell cultures) were compared. Oncobox analysis was further used to examine differential activation of pathways involved in response to existing chemotherapeutics. Results: 128 tissue samples and 28 cell cultures from a total of 44 GBs including 23 nGB, 19 rGB and 2 second-recurrent GBs were analyzed. 14 patient-matched pairs of nGB and rGB were obtained. DE analysis of nGB and rGB, showed a distinct “signature” associated with rGB. Oncobox analysis found down regulation of pathways related to cell cycle and DNA repair and upregulation of immune response pathways in rGB vs corresponding nGB. Specifically, pathways targeted by temozolomide, which is the first line chemotherapy for GB, were found down regulated in rGB. Among the top pathways upregulated in rGB were the pathways targeted by durvalumab and pomalidomide currently under investigation in phase II or III trials for GB. Conclusions: Specific pathway analysis revealed regional and clinical stage-associated differences in the transcriptional landscapes of nGB and rGB. Our results support a concept of treatment-induced resistance to cytotoxic therapeutics and indicate that temozolomide and radiation treatment have important impacts on gene expression changes associated with GB recurrence. Systematic molecular profiling of rGB is a promising avenue towards predicting sensitivity to targeted therapeutics in rGBs on an individual basis.

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Li, Wenbin, Yaxin Li, Walid Darwish, Shengjun Tang, Yuling Hu, and Wu Chen. "A Range-Independent Disparity-Based Calibration Model for Structured Light Pattern-Based RGBD Sensor." Sensors 20, no. 3 (2020): 639. http://dx.doi.org/10.3390/s20030639.

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Consumer-grade RGBD sensors that provide both colour and depth information have many potential applications, such as robotics control, localization, and mapping, due to their low cost and simple operation. However, the depth measurement provided by consumer-grade RGBD sensors is still inadequate for many high-precision applications, such as rich 3D reconstruction, accurate object recognition and precise localization, due to the fact that the systematic errors of RGB sensors increase exponentially with the ranging distance. Most existing calibration models for depth measurement must be carried out with different distances. In this paper, we reveal the mechanism of how an infrared (IR) camera and IR projector contribute to the overall non-centrosymmetric distortion of a structured light pattern-based RGBD sensor. Then, a new two-step calibration method for RGBD sensors based on the disparity measurement is proposed, which is range-independent and has full frame coverage. Three independent calibration models are used for the calibration for the three main components of the RGBD sensor errors: the infrared camera distortion, the infrared projection distortion, and the infrared cone-caused bias. Experiments show the proposed calibration method can provide precise calibration results in full-range and full-frame coverage of depth measurement. The offset in the edge area of long-range depth (8 m) is reduced from 86 cm to 30 cm, and the relative error is reduced from 11% to 3% of the range distance. Overall, at far range the proposed calibration method can improve the depth accuracy by 70% in the central region of depth frame and 65% in the edge region.

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Li, Guoqing, Guoping Zhang, Chanchan Qin, and Anqin Lu. "Automatic RGBD Object Segmentation Based on MSRM Framework Integrating Depth Value." International Journal on Artificial Intelligence Tools 29, no. 07n08 (2020): 2040009. http://dx.doi.org/10.1142/s0218213020400096.

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In this paper, an automatic RGBD object segmentation method is described. The method integrates depth feature with the cues from RGB images and then uses maximal similarity based region merging (MSRM) method to obtain the segmentation results. Firstly, the depth information is fused to the simple linear iterative clustering (SLIC) method so as to produce superpixels whose boundaries are well adhered to the edges of the natural image. Meanwhile, the depth prior is also incorporated into the saliency estimation, which helps a more accurate localization of representative object and background seeds. By introducing the depth cue into the region merging rule, the maximal geometry weighted similarity (MGWS) is considered, and the resulting segmentation framework has the ability to handle the complex image with similar colour appearance between object and background. Extensive experiments on public RGBD image datasets show that our proposed approach can reliably and automatically provide very promising segmentation results.

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Wen, Senfar. "P-34: Color Gamut and Power Consumption of a RGBW LCD Using RGB LED Backlight." SID Symposium Digest of Technical Papers 40, no. 1 (2009): 1216. http://dx.doi.org/10.1889/1.3256510.

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Can, Chi, and Ian Underwood. "Compact and efficient RGB to RGBW data conversion method and its application in OLED microdisplays." Journal of the Society for Information Display 21, no. 3 (2013): 109–19. http://dx.doi.org/10.1002/jsid.158.

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Chen, Liang Chia, and Nguyen Van Thai. "Real-Time 3-D Mapping for Indoor Environments Using RGB-D Cameras." Advanced Materials Research 579 (October 2012): 435–44. http://dx.doi.org/10.4028/www.scientific.net/amr.579.435.

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For three-dimensional (3-D) mapping, so far, 3-D laser scanners and stereo camera systems are used widely due to their high measurement range and accuracy. For stereo camera systems, establishing corresponding point pairs between two images is one crucial step for reconstructing depth information. However, mapping approaches using laser scanners are still restricted by a serious constraint by accurate image registration and mapping. In recent years, time-of-flight (ToF) cameras have been used for mapping tasks in providing high frame rates while preserving a compact size, but lack in measurement precision and robustness. To address the current technological bottleneck, this article presents a 3-D mapping method which employs an RGB-D camera for 3-D data acquisition and then applies the RGB-D features alignment (RGBD-FA) for data registration. Experimental results show the feasibility and robustness of applying the proposed approach for real-time 3-D mapping for large-scale indoor environments.

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Liu, Xu, Abdelouahed Gherbi, Wubin Li, Zhenzhou Wei, and Mohamed Cheriet. "TaijiGNN: A New Cycle-Consistent Generative Neural Network for High-Quality Bidirectional Transformation between RGB and Multispectral Domains." Sensors 21, no. 16 (2021): 5394. http://dx.doi.org/10.3390/s21165394.

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Since multispectral images (MSIs) and RGB images (RGBs) have significantly different definitions and severely imbalanced information entropies, the spectrum transformation between them, especially reconstructing MSIs from RGBs, is a big challenge. We propose a new approach, the Taiji Generative Neural Network (TaijiGNN), to address the above-mentioned problems. TaijiGNN consists of two generators, G_MSI, and G_RGB. These two generators establish two cycles by connecting one generator’s output with the other’s input. One cycle translates the RGBs into the MSIs and converts the MSIs back to the RGBs. The other cycle does the reverse. The cycles can turn the problem of comparing two different domain images into comparing the same domain images. In the same domain, there are neither different domain definition problems nor severely underconstrained challenges, such as reconstructing MSIs from RGBs. Moreover, according to several investigations and validations, we effectively designed a multilayer perceptron neural network (MLP) to substitute the convolutional neural network (CNN) when implementing the generators to make them simple and high performance. Furthermore, we cut off the two traditional CycleGAN’s identity losses to fit the spectral image translation. We also added two consistent losses of comparing paired images to improve the two generators’ training effectiveness. In addition, during the training process, similar to the ancient Chinese philosophy Taiji’s polarity Yang and polarity Yin, the two generators update their neural network parameters by interacting with and complementing each other until they all converge and the system reaches a dynamic balance. Furthermore, several qualitative and quantitative experiments were conducted on the two classical datasets, CAVE and ICVL, to evaluate the performance of our proposed approach. Promising results were obtained with a well-designed simplistic MLP requiring a minimal amount of training data. Specifically, in the CAVE dataset, to achieve comparable state-of-the-art results, we only need half of the dataset for training; for the ICVL dataset, we used only one-fifth of the dataset to train the model, but obtained state-of-the-art results.

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Huang, Chengqiang, Youchang Yang, Bo Wu, and Weize Yu. "RGB-to-RGBG conversion algorithm with adaptive weighting factors based on edge detection and minimal square error." Journal of the Optical Society of America A 35, no. 6 (2018): 969. http://dx.doi.org/10.1364/josaa.35.000969.

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Nobile, Luca, Marco Randazzo, Michele Colledanchise, et al. "Active Exploration for Obstacle Detection on a Mobile Humanoid Robot." Actuators 10, no. 9 (2021): 205. http://dx.doi.org/10.3390/act10090205.

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Conventional approaches to robot navigation in unstructured environments rely on information acquired from the LiDAR mounted on the robot base to detect and avoid obstacles. This approach fails to detect obstacles that are too small, or that are invisible because they are outside the LiDAR’s field of view. A possible strategy is to integrate information from other sensors. In this paper, we explore the possibility of using depth information from a movable RGB-D camera mounted on the head of the robot, and investigate, in particular, active control strategies to effectively scan the environment. Existing works combine RGBD-D and 2D LiDAR data passively by fusing the current point-cloud from the RGB-D camera with the occupancy grid computed from the 2D LiDAR data, while the robot follows a given path. In contrast, we propose an optimization strategy that actively changes the position of the robot’s head, where the camera is mounted, at each point of the given navigation path; thus, we can fully exploit the RGB-D camera to detect, and hence avoid, obstacles undetected by the 2D LiDAR, such as overhanging obstacles or obstacles in blind spots. We validate our approach in both simulation environments to gather statistically significant data and real environments to show the applicability of our method to real robots. The platform used is the humanoid robot R1.

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Zhang, Wen-Li, Kun Yang, Yi-Tao Xin, and Ting-Song Zhao. "Multi-Object Tracking Algorithm for RGB-D Images Based on Asymmetric Dual Siamese Networks." Sensors 20, no. 23 (2020): 6745. http://dx.doi.org/10.3390/s20236745.

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Currently, intelligent security systems are widely deployed in indoor buildings to ensure the safety of people in shopping malls, banks, train stations, and other indoor buildings. Multi-Object Tracking (MOT), as an important component of intelligent security systems, has received much attention from many researchers in recent years. However, existing multi-objective tracking algorithms still suffer from trajectory drift and interruption problems in crowded scenes, which cannot provide valuable data for managers. In order to solve the above problems, this paper proposes a Multi-Object Tracking algorithm for RGB-D images based on Asymmetric Dual Siamese networks (ADSiamMOT-RGBD). This algorithm combines appearance information from RGB images and target contour information from depth images. Furthermore, the attention module is applied to repress the redundant information in the combined features to overcome the trajectory drift problem. We also propose a trajectory analysis module, which analyzes whether the head movement trajectory is correct in combination with time-context information. It reduces the number of human error trajectories. The experimental results show that the proposed method in this paper has better tracking quality on the MICC, EPFL, and UMdatasets than the previous work.

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Regev, Nir, and Dov Wulich. "Multi-Modal, Remote Breathing Monitor." Sensors 20, no. 4 (2020): 1229. http://dx.doi.org/10.3390/s20041229.

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Monitoring breathing is important for a plethora of applications including, but not limited to, baby monitoring, sleep monitoring, and elderly care. This paper presents a way to fuse both vision-based and RF-based modalities for the task of estimating the breathing rate of a human. The modalities used are the F200 Intel® RealSenseTM RGB and depth (RGBD) sensor, and an ultra-wideband (UWB) radar. RGB image-based features and their corresponding image coordinates are detected on the human body and are tracked using the famous optical flow algorithm of Lucas and Kanade. The depth at these coordinates is also tracked. The synced-radar received signal is processed to extract the breathing pattern. All of these signals are then passed to a harmonic signal detector which is based on a generalized likelihood ratio test. Finally, a spectral estimation algorithm based on the reformed Pisarenko algorithm tracks the breathing fundamental frequencies in real-time, which are then fused into a one optimal breathing rate in a maximum likelihood fashion. We tested this multimodal set-up on 14 human subjects and we report a maximum error of 0.5 BPM compared to the true breathing rate.

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Wen, Senfar. "Color saturation improvement through the use of unequal‐area color filters for the RGB‐LED‐Backlight RGBW LCD." Journal of Information Display 10, no. 4 (2009): 164–70. http://dx.doi.org/10.1080/15980316.2009.9652101.

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Otte, Karen, Tobias Ellermeyer, Tim-Sebastian Vater, et al. "Instrumental Assessment of Stepping in Place Captures Clinically Relevant Motor Symptoms of Parkinson’s Disease." Sensors 20, no. 19 (2020): 5465. http://dx.doi.org/10.3390/s20195465.

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Fluctuations of motor symptoms make clinical assessment in Parkinson’s disease a complex task. New technologies aim to quantify motor symptoms, and their remote application holds potential for a closer monitoring of treatment effects. The focus of this study was to explore the potential of a stepping in place task using RGB-Depth (RGBD) camera technology to assess motor symptoms of people with Parkinson’s disease. In total, 25 persons performed a 40 s stepping in place task in front of a single RGBD camera (Kinect for Xbox One) in up to two different therapeutic states. Eight kinematic parameters were derived from knee movements to describe features of hypokinesia, asymmetry, and arrhythmicity of stepping. To explore their potential clinical utility, these parameters were analyzed for their Spearman’s Rho rank correlation to clinical ratings, and for intraindividual changes between treatment conditions using standard response mean and paired t-test. Test performance not only differed between ON and OFF treatment conditions, but showed moderate correlations to clinical ratings, specifically ratings of postural instability (pull test). Furthermore, the test elicited freezing in some subjects. Results suggest that this single standardized motor task is a promising candidate to assess an array of relevant motor symptoms of Parkinson’s disease. The simple technical test setup would allow future use by patients themselves.

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Kapse, Shalmalee, Richard de Grijs, and Daniel B. Zucker. "Searching for chemical abundance variations in young star clusters in the Magellanic Clouds: NGC 411, NGC 1718, and NGC 2213." Monthly Notices of the Royal Astronomical Society 503, no. 4 (2021): 6016–25. http://dx.doi.org/10.1093/mnras/stab813.

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ABSTRACT The conventional picture of coeval, chemically homogeneous, populous star clusters – known as ‘simple’ stellar populations (SSPs) – is a view of the past. Photometric and spectroscopic studies reveal that almost all ancient globular clusters in the Milky Way and our neighbouring galaxies exhibit star-to-star light-element abundance variations, typically known as ‘multiple populations’ (MPs). Here, we analyse photometric Hubble Space Telescope observations of three young (&lt;2-Gyr old) Large and Small Magellanic Cloud clusters, NGC 411, NGC 1718, and NGC 2213. We measure the widths of their red giant branches (RGBs). For NGC 411, we also use a pseudo-colour–magnitude diagram (pseudo-CMD) to assess its RGB for evidence of MPs. We compare the morphologies of the clusters’ RGBs with artificially generated SSPs. We conclude that their RGBs do not show evidence of significant broadening beyond intrinsic photometric scatter, suggesting an absence of significant chemical abundance variations in our sample clusters. Specifically, for NGC 411, NGC 1718, and NGC 2213 we derive maximum helium-abundance variations of δY = 0.003 ± 0.001(Y = 0.300), 0.002 ± 0.001(Y = 0.350), and 0.004 ± 0.002(Y = 0.300), respectively. We determined an upper limit to the NGC 411 nitrogen-abundance variation of Δ[N/Fe] = 0.3 dex; the available data for our other clusters do not allow us to determine useful upper limits. It thus appears that the transition from SSPs to MPs occurs at an age of ∼2 Gyr, implying that age might play an important role in this transition. This raises the question as to whether this is indeed a fundamental minimum age limit for the formation of MPs.

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Lee, Junwoo, and Bummo Ahn. "Real-Time Human Action Recognition with a Low-Cost RGB Camera and Mobile Robot Platform." Sensors 20, no. 10 (2020): 2886. http://dx.doi.org/10.3390/s20102886.

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Human action recognition is an important research area in the field of computer vision that can be applied in surveillance, assisted living, and robotic systems interacting with people. Although various approaches have been widely used, recent studies have mainly focused on deep-learning networks using Kinect camera that can easily generate data on skeleton joints using depth data, and have achieved satisfactory performances. However, their models are deep and complex to achieve a higher recognition score; therefore, they cannot be applied to a mobile robot platform using a Kinect camera. To overcome these limitations, we suggest a method to classify human actions in real-time using a single RGB camera, which can be applied to the mobile robot platform as well. We integrated two open-source libraries, i.e., OpenPose and 3D-baseline, to extract skeleton joints on RGB images, and classified the actions using convolutional neural networks. Finally, we set up the mobile robot platform including an NVIDIA JETSON XAVIER embedded board and tracking algorithm to monitor a person continuously. We achieved an accuracy of 70% on the NTU-RGBD training dataset, and the whole process was performed on an average of 15 frames per second (FPS) on an embedded board system.

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Atiyah, Hanan A., and Mohammed Y. Hassan. "Outdoor Localization in Mobile Robot with 3D LiDAR Based on Principal Component Analysis and K-Nearest Neighbors Algorithm." Engineering and Technology Journal 39, no. 6 (2021): 965–76. http://dx.doi.org/10.30684/etj.v39i6.2032.

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Localization is one of the potential challenges for a mobile robot. Due to the inaccuracy of GPS systems in determining the location of the moving robot alongside weathering effects on sensors such as RGBs (e.g. rain and light-sensitivity(. This paper aims to improve the localization of mobile robots by combining the 3D LiDAR data with RGB-D images using deep learning algorithms. The proposed approach is to design an outdoor localization system. It is divided into three stages. The first stage is the training stage where 3D LiDAR scans the city and then reduces the dimensions of 3D LiDAR data to 2.5D image. This is based on PCA method where these data are used as training data. The second stage is the testing data stage. RGB and depth image in IHS method are combined to generate 2.5D fusion image. The training and testing of these datasets are based on using Convolution Neural Network. The third stage consists of using the K-Nearest Neighbor algorithm. This is the classification stage to get high accuracy and reduces the training time. The experimental results obtained prove the superiorly of the proposed approach with accuracy up to 97.52%, Mean Square of Error of 0.057568, and Mean error in distance equals 0.804 meters.

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Suwarsono, NFn, Indah Prasasti, Jalu Tejo Nugroho, Jansen Sitorus, and Djoko Triyono. "DETECTING THE LAVA FLOW DEPOSITS FROM 2018 ANAK KRAKATAU ERUPTION USING DATA FUSION LANDSAT-8 OPTIC AND SENTINEL-1 SAR." International Journal of Remote Sensing and Earth Sciences (IJReSES) 15, no. 2 (2019): 157. http://dx.doi.org/10.30536/j.ijreses.2018.v15.a3078.

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The increasing volcanic activity of Anak Krakatau volcano has raised concerns about a major disaster in the area around the Sunda Strait. The objective of the research is to fuse Landsat-8 OLI (Operational Land Imager) and Sentinel-1 TOPS (Terrain Observation with Progressive Scans), an integration of SAR and optic remote sensing data, in observing the lava flow deposits resulted from Anak Krakatau eruption during the middle 2018 eruption. RGBI and the Brovey transformation were conducted to merge (fuse) the optical and SAR data. The results showed that optical and SAR data fusion sharpened the appearance of volcano morphology and lava flow deposits. The regions are often constrained by cloud cover and volcanic ash, which occurs at the time of the volcanic eruption. The RGBI-VV and Brovey RGB-VV methods provide better display quality results in revealing the morphology of volcanic cone and lava deposits. The entire slopes of Anak Krakatau Volcano, with a radius of about 1 km from the crater is an area prone to incandescent lava and pyroclastic falls. The direction of the lava flow has the potential to spread in all directions. The fusion method of optical Landsat-8 and Sentinel-1 SAR data can be used continuously in monitoring the activity of Anak Krakatau volcano and other volcanoes in Indonesia both in cloudy and clear weather conditions.

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Hong, Sung-Jin, and Oh-Kyong Kwon. "An RGB to RGBY Color Conversion Algorithm for Liquid Crystal Display Using RGW Pixel with Two-Field Sequential Driving Method." Journal of the Optical Society of Korea 18, no. 6 (2014): 777–82. http://dx.doi.org/10.3807/josk.2014.18.6.777.

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Mentari, Mustika, Yuita Arum Sari, and Ratih Kartika Dewi. "Deteksi Kanker Kulit Melanoma dengan Linear Discriminant Analysis-Fuzzy k-Nearest Neigbhour Lp-Norm." Register: Jurnal Ilmiah Teknologi Sistem Informasi 2, no. 1 (2016): 34. http://dx.doi.org/10.26594/r.v2i1.443.

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Abstrak Seiring perkembangan teknologi dilakukan otomatisasi deteksi kanker kulit melalui citra dermoscopy. Pengambilan informasi fitur citra dermoscopy terganggu dengan outlier dan overfitting, karena faktor jenis kulit, penyebaran kanker yang tidak merata atau kesalahan sampling. Penelitian ini mengusulkan deteksi kanker kulit melanoma dengan mengintegrasikan metode fuzzy K-Nearest Neighbour (FuzzykNN), Lp-norm dan Linear Discriminant Analysis (LDA) untuk mengurangi outlier dan overfitting. Masukan berupa citra warna RGB yang dinormalisasi menjadi RGBr. Reduksi dimensi dengan LDA menghasilkan fitur dengan nilai eigen paling menonjol. LDA pada penelitian ini menghasilkan dua fitur paling menonjol dari 141 jenis fitur, yaitu wilayah tumor dan minimum wilayah tumor channel R. Kemudian dilakukan klasifikasi FuzzykNN dan metode pengukur jarak Lp-norm. Penggunaan metode LDA dan Lp-norm dalam proses klasifikasi ini mengatasi terjadinya overfitting. Akurasi yang dihasilkan metode LDA-fuzzykNN Lp Norm, yaitu 72% saat masing-masing nilai p dan k = 25. Metode gabungan ini terbukti cukup baik dari pada metode yang dijalankan terpisah. Kata kunci: melanoma, fuzzy, KNN, Lp-norm, LDA.  Abstract As the advancement of technology skin cancer detection need to be automated with the use of dermoscopy image. Outlier and overfitting are the problem in feature extraction of dermoscopy image, this can be caused by skin type, uneven cancer distribution or sampling error. This study proposed melanoma skin cancer detection by fuzzy K-Nearest Neighbour (FuzzykNN) with Lp-norm integrated with Linear Discriminant Analysis (LDA) to reduce the problem of outlier and overfitting. Input used in this study are images with RGB channel, then it adapted to RGBr. Dimensional reduction with LDA result in features with highest eigen value. LDA in this research select 2 discriminant, they are tumor area and minimum tumor area in R channel. This features then classified by fuzzykNN with Lp-Norm. Integration of LDA and Lp-norm in classification can reduce the problem of overfitting. This study results in 72% accuracy when the value of p and k are 25. Integration of LDA and fuzzykNN with Lp-norm has better result than unintegrated method. Key word: melanoma, fuzzy, KNN, Lp-norm, LDA.

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Cheng, Lidan, Tianyi Li, Shijia Zha, Wei Wei, and Jihua Gu. "Multichannel Saliency Detection Based on Visual Bionics." Applied Bionics and Biomechanics 2020 (November 23, 2020): 1–8. http://dx.doi.org/10.1155/2020/8886923.

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Inspired by the visual properties of the human eyes, the depth information of visual attention is integrated into the saliency detection to effectively solve problems such as low accuracy and poor stability under similar or complex background interference. Firstly, the improved SLIC algorithm was used to segment and cluster the RGBD image. Secondly, the depth saliency of the image region was obtained according to the anisotropic center-surround difference method. Then, the global feature saliency of RGB image was calculated according to the colour perception rule of human vision. The obtained multichannel saliency maps were weighted and fused based on information entropy to highlighting the target area and get the final detection results. The proposed method works within a complexity of O(N), and the experimental results show that our algorithm based on visual bionics effectively suppress the interference of similar or complex background and has high accuracy and stability.

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Journal articles on the topic 'RGBZ'

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