- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features
摘要： The detection of individual trees in a larch plantation could improve the management efficiency and production prediction. This study introduced a two-stage individual tree crown (ITC) segmentation method for airborne light detection and ranging (LiDAR) point clouds, focusing on larch plantation forests with different stem densities. The two-stage segmentation method consists of the region growing and morphology segmentation, which combines advantages of the region growing characteristics and the detailed morphology structures of tree crowns. The framework comprises five steps: (1) determination of the initial dominant segments using a region growing algorithm, (2) identification of segments to be redefined based on the 2D hull convex area of each segment, (3) establishment and selection of profiles based on the tree structures, (4) determination of the number of trees using the correlation coefficient of residuals between Gaussian fitting and the tree canopy shape described in each profile, and (5) k-means segmentation to obtain the point cloud of a single tree. The accuracy was evaluated in terms of correct matching, recall, precision, and F-score in eight plots with different stem densities. Results showed that the proposed method significantly increased ITC detections compared with that of using only the region growing algorithm, where the correct matching rate increased from 73.5% to 86.1%, and the recall value increased from 0.78 to 0.89.
关键词： airborne laser scanning (ALS),individual tree crown (ITC) segmentation,light detection and ranging (LiDAR),region growing,canopy morphology,larch plantation
更新于2025-09-19 17:13:59
-
An electrodeposited amorphous cobalt sulphide nanobowl array with secondary nanosheets as a multifunctional counter electrode for enhancing the efficiency in a dye-sensitized solar cell
摘要： Parsing sketches via semantic segmentation is attractive but challenging, because (i) free-hand drawings are abstract with large variances in depicting objects due to different drawing styles and skills; (ii) distorting lines drawn on the touchpad make sketches more difficult to be recognized; (iii) the high-performance image segmentation via deep learning technologies needs enormous annotated sketch datasets during the training stage. In this paper, we propose a Sketch-target deep FCN Segmentation Network(SFSegNet) for automatic free-hand sketch segmentation, labeling each sketch in a single object with multiple parts. SFSegNet has an end-to-end network process between the input sketches and the segmentation results, composed of 2 parts: (i) a modified deep Fully Convolutional Network(FCN) using a reweighting strategy to ignore background pixels and classify which part each pixel belongs to; (ii) affine transform encoders that attempt to canonicalize the shaking strokes. We train our network with the dataset that consists of 10,000 annotated sketches, to find an extensively applicable model to segment stokes semantically in one ground truth. Extensive experiments are carried out and segmentation results show that our method outperforms other state-of-the-art networks.
关键词： deep learning,FCN,sketch segmentation,object segmentation
更新于2025-09-16 10:30:52
-
[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - A Fast Point Cloud Segmentation Algorithm Based on Region Growth
摘要： Point cloud segmentation is a key prerequisite for object classification recognition. We propose a fast region growing algorithm by using the neighborhood search, filter sampling, Euclidean clustering and region growth. Segmentation experiment on point cloud data in indoor environment demonstrated that segmentation accuracy and efficiency were improved by the proposed algorithm.
关键词： regional growth,Euclidean clustering,point cloud segmentation
更新于2025-09-16 10:30:52
-
An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled with the Probabilistic Patch Based Denoiser
摘要： Vessel segmentation has come a long way in terms of matching the experts at detection accuracy, yet there is potential for further improvement. In this regard, the accurate detection of vessels is generally more challenging due to the high variations in vessel contrast, width, and the observed noise level. Most vessel segmentation strategies utilize contrast enhancement as a preprocessing step, which has an inherent tendency to aggravate the noise and therefore, impede accurate vessel detection. To alleviate this problem, we propose to use the state-of-the-art Probabilistic Patch-Based (PPB) denoiser within the framework of an unsupervised retinal vessel segmentation strategy based on the Frangi filter. The PPB denoiser helps preserve vascular structure while effectively dealing with the amplified noise. Also, the modified Frangi filter is evaluated separately for tiny and large vessels, followed by individual segmentation and linear recombination of the binarized outputs. This way, the performance of the modified Frangi filter is significantly enhanced. The performance evaluation of the proposed method is evaluated on two recognized open-access datasets, viz: DRIVE and STARE. The proposed strategy yields competitive results for both preprocessing modalities, i.e., Contrast Limited Adaptive Histogram Equalization (CLAHE) and Generalized Linear Model (GLM). The performance observed for CLAHE over DRIVE and STARE datasets is (Sn = 0.8027, Acc = 0.9561) and (Sn = 0.798, Acc = 0.9561), respectively. For GLM, it is observed to be (Sn = 0.7907, Acc = 0.9603) and (Sn = 0.7860, Acc = 0.9583) over DRIVE and STARE datasets, respectively. Furthermore, based on the conducted comparative study, it is established that the proposed method outperforms various notable vessel segmentation methods available in the existing literature.
关键词： Image segmentation,Modified Frangi filter,Probabilistic patch-based denoiser,Image denoising,Retinal vessels
更新于2025-09-16 10:30:52
-
Automatic Detection and Segmentation of Laser Stripes for Industrial Measurement
摘要： Laser stripe plays an important role in industrial vision measurement as the major auxiliary feature. Existing researches mainly focus on the application of small size parts. However, with the increase of field of view, it is difficult to extract laser stripes robustly in varying field measurement situations for the complex background, low proportion and uneven characteristic of laser stripes. To increase the measurement adaptability in complex environment, an automatic laser stripe detection and segmentation algorithm is proposed. First, the dataset is constructed by a large number of image patches collected in the field and laboratory, and laser stripe patches in the imbalanced dataset are expanded by data augmentation method. Next, the detection of the laser stripe is initially realized based on the training results of the convolutional neural network (CNN), and then the laser stripe is accurately detected by non-feature filtering criteria based on area constraints. Finally, a sub-regional feature clustering method is proposed to realize effective segmentation of uneven laser stripes. A large number of verification experiments have been carried out in both laboratory and field, and the results show that the proposed method can achieve automatic and accurate extraction of laser strips, which has strong adaptability to both the complex background in the field and the uneven brightness characteristic of laser stripes, satisfying the engineering requirements of large-scale parts field measurement.
关键词： detection and segmentation,stereo-vision measurement,CNN,laser stripe,large industrial part
更新于2025-09-16 10:30:52
-
[IEEE 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE) - London, United Kingdom (2019.8.22-2019.8.23)] 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE) - Detailed Analysis of IRIS Recognition Performance
摘要： Iris recognition is a well-known biometric identification system which distinguishes authentic and imposter individuals based on the features of their irides. It employs stringent statistical analyses of the features of irides due to the fact that each person has a unique iris, just like a fingerprint. In this work, the approach adopted towards the iris recognition problem is through an exhaustive and careful analysis of the statistical properties of the iris images and the randomness of spurious noise effects. The ability to differentiate two different templates from each other improves with the increase in the number of the degrees of freedom (DOF). The DOF depends on the encoding schemes utilized and moreover, it is hypothesized that the encoding schemes used in themselves could influence the recognition performance. The CASIA (Chinese Academy of Sciences Institute of Automation) version 1 database of iris images used in this study has been modified by the addition of artificial noise in order to simulate practical real life in situ noisy iris capture environments. The classical and state-of-the-art segmentation techniques have been compared, determining whether they are superior to the others under several conditions. The 1D, 2D Gabor filters and the short window implementation were all tested. The conclusion was that the 2D Gabor Filters produce a lower equal error rate (EER), higher accuracy and decidability than by using the one-dimensional log Gabor filter. After modifying the one-dimensional log Gabor filters, a lower EER and higher accuracy was found as the noise level increased. This makes the modified 1D log Gabor Filters a better proposition in noisy conditions. The generated iris templates have a predetermined theoretical value of DOF and from the statistical analysis, an experimental value can be determined. The relation between these values can be used as a metric to compare different databases.
关键词： CASIA iris image database,decidability,equal error rate,degrees of freedom,recognition,accuracy,iris encoding,low-resolution images,CASIA-iris segmentation
更新于2025-09-16 10:30:52
-
[IEEE 2019 IEEE International Conference on Image Processing (ICIP) - Taipei, Taiwan (2019.9.22-2019.9.25)] 2019 IEEE International Conference on Image Processing (ICIP) - Lip Image Segmentation in Mobile Devices Based on Alternative Knowledge Distillation
摘要： Lip image segmentation, as the first step in many lip-related tasks (e.g. automatic lipreading), is of vital significance for the subsequent procedures. Nowadays, with the increasing computational power of the mobile devices, mobile applications become more and more popular. In this paper, a new approach is proposed, which is able to segment the lip region in natural scenes and is of acceptable computational complexity to be implemented in mobile devices. Two networks including a complex teacher network and a compact student network with the same structure are employed. With the proposed remedy loss and the alternative knowledge distillation scheme, the student network can learn useful knowledge from the teacher network effectively and efficiently, and even rectify some of its segmentation errors. A dataset containing 49 people captured under natural scenes by various cellphone cameras is adopted for evaluation and the experiment results have demonstrated that the proposed student network even outperforms the teacher network with much less computational cost.
关键词： Lip image segmentation,Deep neural network,Knowledge distillation
更新于2025-09-16 10:30:52
-
Degradation Mechanism Detection in Photovoltaic Backsheets by Fully Convolutional Neural Network
摘要： Materials and devices age with time. Material aging and degradation has important implications for lifetime performance of materials and systems. While consensus exists that materials should be studied and designed for degradation, materials inspection during operation is typically performed manually by technicians. the manual inspection makes studies prone to errors and uncertainties due to human subjectivity. in this work, we focus on automating the process of degradation mechanism detection through the use of a fully convolutional deep neural network architecture (f-cnn). We demonstrate that f-cnn architecture allows for automated inspection of cracks in polymer backsheets from photovoltaic (pV) modules. the developed f-cnn architecture enabled an end-to-end semantic inspection of the pV module backsheets by applying a contracting path of convolutional blocks (encoders) followed by an expansive path of decoding blocks (decoders). first, the hierarchy of contextual features is learned from the input images by encoders. next, these features are reconstructed to the pixel-level prediction of the input by decoders. the structure of the encoder and the decoder networks are thoroughly investigated for the multi-class pixel-level degradation type prediction for pV module backsheets. the developed f-cnn framework is validated by reporting degradation type prediction accuracy for the pixel level prediction at the level of 92.8%.
关键词： photovoltaic backsheets,automated inspection,degradation mechanism,fully convolutional neural network,semantic segmentation
更新于2025-09-12 10:27:22
-
[IEEE 2019 IEEE International Conference on BioPhotonics (BioPhotonics) - Taipei, Taiwan (2019.9.15-2019.9.18)] 2019 IEEE International Conference on BioPhotonics (BioPhotonics) - Deep Learning Approach for Red Blood Cell Segmentation from Full-Field OCT Data of Human Skin
摘要： The purpose of this paper is to segment red blood cells from the Full-Field OCT data of human skin, using deep learning technique. Test results show the developed technique is very promising for real time detection and counting of red blood cells.
关键词： deep learning,Segmentation,Red blood cells
更新于2025-09-12 10:27:22
-
Leveraging Domain Knowledge to Improve Microscopy Image Segmentation With Lifted Multicuts
摘要： The throughput of electron microscopes has increased significantly in recent years, enabling detailed analysis of cell morphology and ultrastructure in fairly large tissue volumes. Analysis of neural circuits at single-synapse resolution remains the flagship target of this technique, but applications to cell and developmental biology are also starting to emerge at scale. On the light microscopy side, continuous development of light-sheet microscopes has led to a rapid increase in imaged volume dimensions, making Terabyte-scale acquisitions routine in the field. The amount of data acquired in such studies makes manual instance segmentation, a fundamental step in many analysis pipelines, impossible. While automatic segmentation approaches have improved significantly thanks to the adoption of convolutional neural networks, their accuracy lags behind human annotations and requires additional manual proof-reading. A still major hindrance to further improvements is the limited field of view of the segmentation networks preventing them from learning to exploit the expected cell morphology or other prior biological knowledge which humans use to inform their segmentation decisions. In this contribution, we show how such domain-specific information can be leveraged by expressing it as long-range interactions in a graph partitioning problem known as the lifted multicut problem. Using this formulation, we demonstrate significant improvement in segmentation accuracy for four challenging boundary-based segmentation problems from neuroscience and developmental biology.
关键词： biomedical image analysis,biological priors,instance segmentation,LM segmentation,connectomics,EM segmentation
更新于2025-09-11 14:15:04