Journal articles on the topic 'Pixel labeling problems'
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Jung, Ho Yub, Kyoung Mu Lee, and Sang Uk Lee. "Window annealing for pixel-labeling problems." Computer Vision and Image Understanding 117, no. 3 (March 2013): 289–303. http://dx.doi.org/10.1016/j.cviu.2012.12.005.
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Shao, Zhenfeng, Ke Yang, and Weixun Zhou. "Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset." Remote Sensing 10, no. 6 (June 16, 2018): 964. http://dx.doi.org/10.3390/rs10060964.
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Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels and sometimes even dense (pixel) labels are required for more complex problems, such as RSIR and semantic segmentation.We therefore extended the existing multi-labeled dataset collected for multi-label RSIR and presented a dense labeling remote sensing dataset termed "DLRSD". DLRSD contained a total of 17 classes, and the pixels of each image were assigned with 17 pre-defined labels. We used DLRSD to evaluate the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep learning-based ones. More specifically, we evaluated the performances of RSIR methods from both single-label and multi-label perspectives. These results demonstrated the advantages of multiple labels over single labels for interpreting complex remote sensing images. DLRSD provided the literature a benchmark for RSIR and other pixel-based problems such as semantic segmentation.
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Shao, Zhenfeng, Ke Yang, and Weixun Zhou. "Correction: Shao, Z.; et al. A Benchmark Dataset for Performance Evaluation of Multi-Label Remote Sensing Image Retrieval. Remote Sens. 2018, 10, 964." Remote Sensing 10, no. 8 (August 3, 2018): 1220. http://dx.doi.org/10.3390/rs10081220.
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In our paper [1], we presented a dense labeling dataset that can be used for not only single-label and multi-label remote sensing image retrieval but also pixel-based problems such as semantic segmentation.[...]
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Macville, Merryn V. E., Jeroen A. W. M. Van der Laak, Ernst J. M. Speel, Nir Katzir, Yuval Garini, Dirk Soenksen, George McNamara, et al. "Spectral Imaging of Multi-Color Chromogenic Dyes in Pathological Specimens." Analytical Cellular Pathology 22, no. 3 (2001): 133–42. http://dx.doi.org/10.1155/2001/740909.
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We have investigated the use of spectral imaging for multi‐color analysis of permanent cytochemical dyes and enzyme precipitates on cytopathological specimens. Spectral imaging is based on Fourier‐transform spectroscopy and digital imaging. A pixel‐by‐pixel spectrum‐based color classification is presented of single‐, double‐, and triple‐color in situ hybridization for centromeric probes in T24 bladder cancer cells, and immunocytochemical staining of nuclear antigens Ki‐67 and TP53 in paraffin‐embedded cervical brush material (AgarCyto). The results demonstrate that spectral imaging unambiguously identifies three chromogenic dyes in a single bright‐field microscopic specimen. Serial microscopic fields from the same specimen can be analyzed using a spectral reference library. We conclude that spectral imaging of multi‐color chromogenic dyes is a reliable and robust method for pixel color recognition and classification. Our data further indicate that the use of spectral imaging (a) may increase the number of parameters studied simultaneously in pathological diagnosis, (b) may provide quantitative data (such as positive labeling indices) more accurately, and (c) may solve segmentation problems currently faced in automated screening of cell‐ and tissue specimens. Figures on http://www.esacp.org/acp/2001/22‐3/macville.htm.
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Chen, Linwei, Ying Fu, Shaodi You, and Hongzhe Liu. "Efficient Hybrid Supervision for Instance Segmentation in Aerial Images." Remote Sensing 13, no. 2 (January 13, 2021): 252. http://dx.doi.org/10.3390/rs13020252.
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Instance segmentation in aerial images is of great significance for remote sensing applications, and it is inherently more challenging because of cluttered background, extremely dense and small objects, and objects with arbitrary orientations. Besides, current mainstream CNN-based methods often suffer from the trade-off between labeling cost and performance. To address these problems, we present a pipeline of hybrid supervision. In the pipeline, we design an ancillary segmentation model with the bounding box attention module and bounding box filter module. It is able to generate accurate pseudo pixel-wise labels from real-world aerial images for training any instance segmentation models. Specifically, bounding box attention module can effectively suppress the noise in cluttered background and improve the capability of segmenting small objects. Bounding box filter module works as a filter which removes the false positives caused by cluttered background and densely distributed objects. Our ancillary segmentation model can locate object pixel-wisely instead of relying on horizontal bounding box prediction, which has better adaptability to arbitrary oriented objects. Furthermore, oriented bounding box labels are utilized for handling arbitrary oriented objects. Experiments on iSAID dataset show that the proposed method can achieve comparable performance (32.1 AP) to fully supervised methods (33.9 AP), which is obviously higher than weakly supervised setting (26.5 AP), when using only 10% pixel-wise labels.
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Chen, Dali, Yingying Ao, and Shixin Liu. "Semi-Supervised Learning Method of U-Net Deep Learning Network for Blood Vessel Segmentation in Retinal Images." Symmetry 12, no. 7 (June 29, 2020): 1067. http://dx.doi.org/10.3390/sym12071067.
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Blood vessel segmentation methods based on deep neural networks have achieved satisfactory results. However, these methods are usually supervised learning methods, which require large numbers of retinal images with high quality pixel-level ground-truth labels. In practice, the task of labeling these retinal images is very costly, financially and in human effort. To deal with these problems, we propose a semi-supervised learning method which can be used in blood vessel segmentation with limited labeled data. In this method, we use the improved U-Net deep learning network to segment the blood vessel tree. On this basis, we implement the U-Net network-based training dataset updating strategy. A large number of experiments are presented to analyze the segmentation performance of the proposed semi-supervised learning method. The experiment results demonstrate that the proposed methodology is able to avoid the problems of insufficient hand-labels, and achieve satisfactory performance.
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Sosnovik, Ivan, and Ivan Oseledets. "Neural networks for topology optimization." Russian Journal of Numerical Analysis and Mathematical Modelling 34, no. 4 (August 27, 2019): 215–23. http://dx.doi.org/10.1515/rnam-2019-0018.
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Abstract In this research, we propose a deep learning based approach for speeding up the topology optimization methods. The problem we seek to solve is the layout problem. The main novelty of this work is to state the problem as an image segmentation task. We leverage the power of deep learning methods as the efficient pixel-wise image labeling technique to perform the topology optimization. We introduce convolutional encoder-decoder architecture and the overall approach of solving the above-described problem with high performance. The conducted experiments demonstrate the significant acceleration of the optimization process. The proposed approach has excellent generalization properties. We demonstrate the ability of the application of the proposed model to other problems. The successful results, as well as the drawbacks of the current method, are discussed.
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Tymchenko, Borys Igorovych. "NEURAL NETWORK METHODS FOR PLANAR IMAGE ANALYSIS IN AUTOMATED SCREENING SYSTEMS." Applied Aspects of Information Technology 4, no. 1 (April 10, 2021): 71–79. http://dx.doi.org/10.15276/aait.01.2021.6.
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Nowadays, means of preventive management in various spheres of human life are actively developing. The task of automated screening is to detect hidden problems at an early stage without human intervention, while the cost of responding to them is low. Visual inspection is often used to perform a screening task. Deep artificial neural networks are especially popular in image processing. One of the main problems when working with them is the need for a large amount of well-labeled data for training. In automated screening systems, available neural network approaches have limitations on the reliability of predictions due to the lack of accurately marked training data, as obtaining quality markup from professionals is very expensive, and sometimes not possible in principle. Therefore, there is a contradiction between increasing the requirements for the precision of predictions of neural network models without increasing the time spent on the one hand, and the need to reduce the cost of obtaining the markup of educational data. In this paper, we propose the parametric model of the segmentation dataset, which can be used to generate training data for model selection and benchmarking; and the multi-task learning method for training and inference of deep neural networks for semantic segmentation. Based on the proposed method, we develop a semi-supervised approach for segmentation of salient regions for classification task. The main advantage of the proposed method is that it uses semantically-similar general tasks, that have better labeling than original one, what allows users to reduce the cost of the labeling process. We propose to use classification task as a more general to the problem of semantic segmentation. As semantic segmentation aims to classify each pixel in the input image, classification aims to assign a class to all of the pixels in the input image. We evaluate our methods using the proposed dataset model, observing the Dice score improvement by seventeen percent. Additionally, we evaluate the robustness of the proposed method to different amount of the noise in labels and observe consistent improvement over baseline version.
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Ling, Feng, Rajamohan Parthasarathy, Ye Wang, and Sokchoo Ng. "Data Augmentation Technology for Improving the Recognition Accuracy of Target Image." Journal of Networking and Telecommunications 2, no. 3 (October 15, 2020): 58. http://dx.doi.org/10.18282/jnt.v2i3.1265.
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<p align="justify">Relevant studies have pointed out that public has paid highly attention on the accuracy of neural network algorithm as it is widely applied in recent years. According to the present practice, it is quite difficult to collect related data when applying neural network algorithm. Besides, problems of trifles and complication exists in data image labeling process, which leads to a bad impact on the recognition accuracy of targets. In this article, analyzes are conducted on the relevant data from the perspective of data image processing with neural network algorithm as the core of this work. Besides, corresponding data augmentation technology is also put forward. Generally speaking, this technology has effectively realized the simulation under different shooting and lighting conditions by flipping, transforming and changing the pixel positions of the related original images, which contributes to the expansion of database types and promotes the robustness of detection work.</p>
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Saglam, A., and N. A. Baykan. "A SATELLITE IMAGE CLASSIFICATION APPROACH BY USING ONE DIMENSIONAL DISCRIMINANT ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W4 (March 6, 2018): 429–35. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w4-429-2018.
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<p><strong>Abstract.</strong> The classification problem in the image processing field is an important challenge, so that in the process image pixels are separated into previously determined classes according to their features. This process provides a meaningful knowledge about an area thanks to the satellite images. Satellite images are digital images obtained from a satellite vehicle by the way scanning the interest areas with some specified sensors. These sensors provide the specific radiometric and spatial information about the surface of the object. This information allows the researchers to obtain reliable classification results to be used to solve some real life problems such as object extraction, mapping, recognition, navigation and disaster management. Linear Discriminant Analysis (LDA) is a supervised method that reduces the dimensions of data in respect to the maximum discrimination of the elements of the data. This method also transfers the data to a new coordinate space in which the discriminant features of the classes are highest using the objection data provided manually. In this work, we consider the classes as if the satellite images have two classes; one is foreground and the other is background. The true classes such as roofs, roads, buildings, spaces and trees are treated sequentially as the foreground. The area outside the foreground class is treated as the background. The one dimensional reduced feature values of pixels, such that each value is reduced according to the binary classification of each class, are considered as membership values to the classes. In this way, each pixel has membership values for each of the classes. Finally, the pixels are classified according to the membership values. We used the ISPRS WG III/4 2D Semantic Labeling Benchmark (Vaihingen) images includes the ground truths and give the accuracy result values for each class.</p>
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Bah, M., Adel Hafiane, and Raphael Canals. "Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images." Remote Sensing 10, no. 11 (October 26, 2018): 1690. http://dx.doi.org/10.3390/rs10111690.
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In recent years, weeds have been responsible for most agricultural yield losses. To deal with this threat, farmers resort to spraying the fields uniformly with herbicides. This method not only requires huge quantities of herbicides but impacts the environment and human health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide to the right place and at the right time (precision agriculture). Nowadays, unmanned aerial vehicles (UAVs) are becoming an interesting acquisition system for weed localization and management due to their ability to obtain images of the entire agricultural field with a very high spatial resolution and at a low cost. However, despite significant advances in UAV acquisition systems, the automatic detection of weeds remains a challenging problem because of their strong similarity to the crops. Recently, a deep learning approach has shown impressive results in different complex classification problems. However, this approach needs a certain amount of training data, and creating large agricultural datasets with pixel-level annotations by an expert is an extremely time-consuming task. In this paper, we propose a novel fully automatic learning method using convolutional neuronal networks (CNNs) with an unsupervised training dataset collection for weed detection from UAV images. The proposed method comprises three main phases. First, we automatically detect the crop rows and use them to identify the inter-row weeds. In the second phase, inter-row weeds are used to constitute the training dataset. Finally, we perform CNNs on this dataset to build a model able to detect the crop and the weeds in the images. The results obtained are comparable to those of traditional supervised training data labeling, with differences in accuracy of 1.5% in the spinach field and 6% in the bean field.
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Liu, Tao, Chunsheng Li, Zongbao Liu, Kejia Zhang, Fang Liu, Dongsheng Li, Yan Zhang, Zhigang Liu, Liyuan Liu, and Jiacheng Huang. "Research on Image Identification Method of Rock Thin Slices in Tight Oil Reservoirs Based on Mask R-CNN." Energies 15, no. 16 (August 10, 2022): 5818. http://dx.doi.org/10.3390/en15165818.
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Terrestrial tight oil has extremely strong diagenesis heterogeneity, so a large number of rock thin slices are needed to reveal the real microscopic pore-throat structure characteristics. In addition, difficult identification, high cost, long time, strong subjectivity and other problems exist in the identification of tight oil rock thin slices, and it is difficult to meet the needs of fine description and quantitative characterization of the reservoir. In this paper, a method for identifying the characteristics of rock thin slices in tight oil reservoirs based on the deep learning technique was proposed. The present work has the following steps: first, the image preprocessing technique was studied. The original image noise was removed by filtering, and the image pixel size was unified by a normalization technique to ensure the quality of samples; second, the self-labeling image data augmentation technique was constructed to solve the problem of sparse samples; third, the Mask R-CNN algorithm was introduced and improved to synchronize the segmentation and recognition of rock thin slice components in tight oil reservoirs; Finally, it was demonstrated through experiments that the SMR method has significant advantages in accuracy, execution speed and migration.
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Giancola, Michael, Randy Paffenroth, and Jacob Whitehill. "Permutation-Invariant Consensus over Crowdsourced Labels." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 6 (June 15, 2018): 21–30. http://dx.doi.org/10.1609/hcomp.v6i1.13326.
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This paper introduces a novel crowdsourcing consensus model and inference algorithm — which we call PICA (Permutation-Invariant Crowdsourcing Aggregation) — that is designed to recover the ground-truth labels of a dataset while being invariant to the class permutations enacted by the different annotators. This is particularly useful for settings in which annotators may have systematic confusions about the meanings of different classes, as well as clustering problems (e.g., dense pixel-wise image segmentation) in which the names/numbers assigned to each cluster have no inherent meaning.The PICA model is constructed by endowing each annotator with a doubly-stochastic matrix (DSM), which models the probabilities that an annotator will perceive one class and transcribe it into another. We conduct simulations and experiments to show the advantage of PICA compared to two baselines (Majority Vote, and an "unpermutation" heuristic) for three different clustering/labeling tasks. We also explore the conditions under which PICA provides better inference accuracy compared to a simpler but related model based on right-stochastic matrices. Finally, we show that PICA can be used to crowdsource responses for dense image segmentation tasks, and provide a proof-of-concept that aggregating responses in this way could improve the accuracy of this labor-intensive task.
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Liu, Tiange, Qiguang Miao, Pengfei Xu, and Shihui Zhang. "Superpixel-Based Shallow Convolutional Neural Network (SSCNN) for Scanned Topographic Map Segmentation." Remote Sensing 12, no. 20 (October 18, 2020): 3421. http://dx.doi.org/10.3390/rs12203421.
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Motivated by applications in topographic map information extraction, our goal was to discover a practical method for scanned topographic map (STM) segmentation. We present an advanced guided watershed transform (AGWT) to generate superpixels on STM. AGWT utilizes the information from both linear and area elements to modify detected boundary maps and sequentially achieve superpixels based on the watershed transform. With achieving an average of 0.06 on under-segmentation error, 0.96 on boundary recall, and 0.95 on boundary precision, it has been proven to have strong ability in boundary adherence, with fewer over-segmentation issues. Based on AGWT, a benchmark for STM segmentation based on superpixels and a shallow convolutional neural network (SCNN), termed SSCNN, is proposed. There are several notable ideas behind the proposed approach. Superpixels are employed to overcome the false color and color aliasing problems that exist in STMs. The unification method of random selection facilitates sufficient training data with little manual labeling while keeping the potential color information of each geographic element. Moreover, with the small number of parameters, SCNN can accurately and efficiently classify those unified pixel sequences. The experiments show that SSCNN achieves an overall F1 score of 0.73 on our STM testing dataset. They also show the quality of the segmentation results and the short run time of this approach, which makes it applicable to full-size maps.
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Xing, Yongfeng, Luo Zhong, and Xian Zhong. "DARSegNet: A Real-Time Semantic Segmentation Method Based on Dual Attention Fusion Module and Encoder-Decoder Network." Mathematical Problems in Engineering 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/6195148.
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The convolutional neural network achieves excellent semantic segmentation results in artificially annotated datasets with complex scenes. However, semantic segmentation methods still suffer from several problems such as low use rate of the features, high computational complexity, and being far from practical real-time application, which bring about challenges for the image semantic segmentation. Two factors are very critical to semantic segmentation task: global context and multilevel semantics. However, generating these two factors will always lead to high complexity. In order to solve this, we propose a novel structure, dual attention fusion module (DAFM), by eliminating structural redundancy. Unlike most of the existing algorithms, we combine the attention mechanism with the depth pyramid pool module (DPPM) to extract accurate dense features for pixel labeling rather than complex expansion convolution. Specifically, we introduce a DPPM to execute the spatial pyramid structure in output and combine the global pool method. The DAFM is introduced in each decoder layer. Finally, the low-level features and high-level features are fused to obtain semantic segmentation result. The experiments and visualization results on Cityscapes and CamVid datasets show that, in real-time semantic segmentation, we have achieved a satisfactory balance between accuracy and speed, which proves the effectiveness of the proposed algorithm. In particular, on a single 1080ti GPU computer, ResNet-18 produces 75.53% MIoU at 70 FPS on Cityscapes and 73.96% MIoU at 109 FPS on CamVid.
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Xing, Yongfeng, Luo Zhong, and Xian Zhong. "DARSegNet: A Real-Time Semantic Segmentation Method Based on Dual Attention Fusion Module and Encoder-Decoder Network." Mathematical Problems in Engineering 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/6195148.
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The convolutional neural network achieves excellent semantic segmentation results in artificially annotated datasets with complex scenes. However, semantic segmentation methods still suffer from several problems such as low use rate of the features, high computational complexity, and being far from practical real-time application, which bring about challenges for the image semantic segmentation. Two factors are very critical to semantic segmentation task: global context and multilevel semantics. However, generating these two factors will always lead to high complexity. In order to solve this, we propose a novel structure, dual attention fusion module (DAFM), by eliminating structural redundancy. Unlike most of the existing algorithms, we combine the attention mechanism with the depth pyramid pool module (DPPM) to extract accurate dense features for pixel labeling rather than complex expansion convolution. Specifically, we introduce a DPPM to execute the spatial pyramid structure in output and combine the global pool method. The DAFM is introduced in each decoder layer. Finally, the low-level features and high-level features are fused to obtain semantic segmentation result. The experiments and visualization results on Cityscapes and CamVid datasets show that, in real-time semantic segmentation, we have achieved a satisfactory balance between accuracy and speed, which proves the effectiveness of the proposed algorithm. In particular, on a single 1080ti GPU computer, ResNet-18 produces 75.53% MIoU at 70 FPS on Cityscapes and 73.96% MIoU at 109 FPS on CamVid.
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Lotte, Rodolfo, Norbert Haala, Mateusz Karpina, Luiz Aragão, and Yosio Shimabukuro. "3D Façade Labeling over Complex Scenarios: A Case Study Using Convolutional Neural Network and Structure-From-Motion." Remote Sensing 10, no. 9 (September 8, 2018): 1435. http://dx.doi.org/10.3390/rs10091435.
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Urban environments are regions in which spectral variability and spatial variability are extremely high, with a huge range of shapes and sizes, and they also demand high resolution images for applications involving their study. Due to the fact that these environments can grow even more over time, applications related to their monitoring tend to turn to autonomous intelligent systems, which together with remote sensing data could help or even predict daily life situations. The task of mapping cities by autonomous operators was usually carried out by aerial optical images due to its scale and resolution; however new scientific questions have arisen, and this has led research into a new era of highly-detailed data extraction. For many years, using artificial neural models to solve complex problems such as automatic image classification was commonplace, owing much of their popularity to their ability to adapt to complex situations without needing human intervention. In spite of that, their popularity declined in the mid-2000s, mostly due to the complex and time-consuming nature of their methods and workflows. However, newer neural network architectures have brought back the interest in their application for autonomous classifiers, especially for image classification purposes. Convolutional Neural Networks (CNN) have been a trend for pixel-wise image segmentation, showing flexibility when detecting and classifying any kind of object, even in situations where humans failed to perceive differences, such as in city scenarios. In this paper, we aim to explore and experiment with state-of-the-art technologies to semantically label 3D urban models over complex scenarios. To achieve these goals, we split the problem into two main processing lines: first, how to correctly label the façade features in the 2D domain, where a supervised CNN is used to segment ground-based façade images into six feature classes, roof, window, wall, door, balcony and shop; second, a Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) workflow is used to extract the geometry of the façade, wherein the segmented images in the previous stage are then used to label the generated mesh by a “reverse” ray-tracing technique. This paper demonstrates that the proposed methodology is robust in complex scenarios. The façade feature inferences have reached up to 93% accuracy over most of the datasets used. Although it still presents some deficiencies in unknown architectural styles and needs some improvements to be made regarding 3D-labeling, we present a consistent and simple methodology to handle the problem.
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Győrfi, Ágnes, László Szilágyi, and Levente Kovács. "A Fully Automatic Procedure for Brain Tumor Segmentation from Multi-Spectral MRI Records Using Ensemble Learning and Atlas-Based Data Enhancement." Applied Sciences 11, no. 2 (January 8, 2021): 564. http://dx.doi.org/10.3390/app11020564.
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The accurate and reliable segmentation of gliomas from magnetic resonance image (MRI) data has an important role in diagnosis, intervention planning, and monitoring the tumor’s evolution during and after therapy. Segmentation has serious anatomical obstacles like the great variety of the tumor’s location, size, shape, and appearance and the modified position of normal tissues. Other phenomena like intensity inhomogeneity and the lack of standard intensity scale in MRI data represent further difficulties. This paper proposes a fully automatic brain tumor segmentation procedure that attempts to handle all the above problems. Having its foundations on the MRI data provided by the MICCAI Brain Tumor Segmentation (BraTS) Challenges, the procedure consists of three main phases. The first pre-processing phase prepares the MRI data to be suitable for supervised classification, by attempting to fix missing data, suppressing the intensity inhomogeneity, normalizing the histogram of observed data channels, generating additional morphological, gradient-based, and Gabor-wavelet features, and optionally applying atlas-based data enhancement. The second phase accomplishes the main classification process using ensembles of binary decision trees and provides an initial, intermediary labeling for each pixel of test records. The last phase reevaluates these intermediary labels using a random forest classifier, then deploys a spatial region growing-based structural validation of suspected tumors, thus achieving a high-quality final segmentation result. The accuracy of the procedure is evaluated using the multi-spectral MRI records of the BraTS 2015 and BraTS 2019 training data sets. The procedure achieves high-quality segmentation results, characterized by average Dice similarity scores of up to 86%.
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Győrfi, Ágnes, László Szilágyi, and Levente Kovács. "A Fully Automatic Procedure for Brain Tumor Segmentation from Multi-Spectral MRI Records Using Ensemble Learning and Atlas-Based Data Enhancement." Applied Sciences 11, no. 2 (January 8, 2021): 564. http://dx.doi.org/10.3390/app11020564.
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The accurate and reliable segmentation of gliomas from magnetic resonance image (MRI) data has an important role in diagnosis, intervention planning, and monitoring the tumor’s evolution during and after therapy. Segmentation has serious anatomical obstacles like the great variety of the tumor’s location, size, shape, and appearance and the modified position of normal tissues. Other phenomena like intensity inhomogeneity and the lack of standard intensity scale in MRI data represent further difficulties. This paper proposes a fully automatic brain tumor segmentation procedure that attempts to handle all the above problems. Having its foundations on the MRI data provided by the MICCAI Brain Tumor Segmentation (BraTS) Challenges, the procedure consists of three main phases. The first pre-processing phase prepares the MRI data to be suitable for supervised classification, by attempting to fix missing data, suppressing the intensity inhomogeneity, normalizing the histogram of observed data channels, generating additional morphological, gradient-based, and Gabor-wavelet features, and optionally applying atlas-based data enhancement. The second phase accomplishes the main classification process using ensembles of binary decision trees and provides an initial, intermediary labeling for each pixel of test records. The last phase reevaluates these intermediary labels using a random forest classifier, then deploys a spatial region growing-based structural validation of suspected tumors, thus achieving a high-quality final segmentation result. The accuracy of the procedure is evaluated using the multi-spectral MRI records of the BraTS 2015 and BraTS 2019 training data sets. The procedure achieves high-quality segmentation results, characterized by average Dice similarity scores of up to 86%.
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Liu, Wang, Xiao Li, and Fengjiao Wu. "Research on Restoration Algorithm of Tomb Murals Based on Sequential Similarity Detection." Scientific Programming 2021 (October 7, 2021): 1–8. http://dx.doi.org/10.1155/2021/6842353.
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Considering the problems of fuzzy repair and low pixel similarity matching in the repair of existing tomb murals, we propose a novel tomb mural repair algorithm based on sequential similarity detection in this paper. First, we determine the gradient value of tomb mural through second-order Gaussian Laplace operator in LOG edge detection and then reduce the noise in the edge of tomb mural to process a smooth edge of tomb mural. Further, we set the mathematical model to obtain the edge features of tomb murals. To calculate the average gray level of foreground and background under a specific threshold, we use the maximum interclass variance method, which considers the influence of small cracks on the edge of tomb murals and separates the cracks through a connected domain labelling algorithm and open and close operations to complete the edge threshold segmentation. In addition, we use the priority calculation function to determine the damaged tomb mural area, calculate the gradient factor of edge information, obtain the information entropy of different angles, determine the priority of tomb mural image repair, detect the similarity of tomb mural repair pixels with the help of sequential similarity, and complete the tomb mural repair. Experimental results show that our model can effectively repair the edges of the tomb murals and can achieve a high pixel similarity matching degree.
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Ackerley, C. A., L. E. Becker, A. Tilups, J. T. Rutka, and J. F. Mancuso. "CCD cameras facilitate the imaging of small gold particles in immunogold-labelled ultrathin cryosections." Proceedings, annual meeting, Electron Microscopy Society of America 54 (August 11, 1996): 904–5. http://dx.doi.org/10.1017/s0424820100166981.
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Immunogold labelled ultrathin cryosections have proven themselves invaluable in subcellular localizations of many epitopes. As there are no embeddents to block an antibody‘s access to an epitope beyond physical size or chemical alteration by primary fixation, immunogold labelling density is increased dramatically with smaller (>5nm) gold-ligand complexes. Takizawa and Robinson have elegantly demonstrated penetrations in excess of 1μm using 1.4nm gold-Fab conjugates, while 5nm IgG gold particles only partially penetrated the surface and l0nm IgG gold particles labelled the surface. In order to visualize the 1.4nm gold-Fab particles silver enhancement was required leading one to doubt the viability of doing any quantitation.Modern CCD cameras provide an effective alternative method of data collection to photographic plates with their ability to record 1000 × 1000 pixel digital images. Using a simple sharpen filter and contrast enhancement, 1.4nm gold Fab particles are easily resolved on thin films. Problems arise when using this small a particle on conventionally stained immunogold labelled cryosections.
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Pan, Lei, Hengchao Li, Xiang Dai, Ying Cui, Xifeng Huang, and Lican Dai. "Latent Low-Rank Projection Learning with Graph Regularization for Feature Extraction of Hyperspectral Images." Remote Sensing 14, no. 13 (June 27, 2022): 3078. http://dx.doi.org/10.3390/rs14133078.
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Due to the great benefit of rich spectral information, hyperspectral images (HSIs) have been successfully applied in many fields. However, some problems of concern also limit their further applications, such as high dimension and expensive labeling. To address these issues, an unsupervised latent low-rank projection learning with graph regularization (LatLRPL) method is presented for feature extraction and classification of HSIs in this paper, in which discriminative features can be extracted from the view of latent space by decomposing the latent low-rank matrix into two different matrices, also benefiting from the preservation of intrinsic subspace structures by the graph regularization. Different from the graph embedding-based methods that need two phases to obtain the low-dimensional projections, one step is enough for LatLRPL by constructing the integrated projection learning model, reducing the complexity and simultaneously improving the robustness. To improve the performance, a simple but effective strategy is exploited by conducting the local weighted average on the pixels in a sliding window for HSIs. Experiments on the Indian Pines and Pavia University datasets demonstrate the superiority of the proposed LatLRPL method.
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Лавренюк, Микола Сергіович. "МЕТОД ОБ’ЄКТНОЇ ПОСТОБРОБКИ КАРТ КЛАСИФІКАЦІЇ З УРАХУВАННЯМ СПЕЦИФІКИ КОЖНОГО КЛАСУ." Aerospace technic and technology, no. 1 (February 25, 2018): 80–91. http://dx.doi.org/10.32620/aktt.2018.1.10.
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Obtaining reliable and accurate crop classification and land cover map based on satellite data, in particular high resolution data, is one of the most important tasks in remote sensing. Such maps provide basic information for many other applied problems and are vital in remote sensing studies. Despite of which machine learning methods were utilized for maps obtaining: traditional (Random Forest, Support Vector Machine, Multi-layer perceptron, logistic regression) or state-of-the-art approaches (autoencoder, convolutional neural network, recurrent neural network) there is some noise (single pixels or groups and clusters of pixels that wrong classify) on such maps. There are traditional methods for noise reduction, however these methods do not take into account image semantics. Therefore, they are not effective for filtration land cover and crop classification maps based on satellite images. The most complicated task in the filtering such maps is to preserve boundaries between different agricultural fields and to remove quite big clusters of incorrect classified pixels (objects) and at the same time save small farmer fields that are right classified. Thus, in this paper we proposed new method for postprocessing crop classification map based on algorithm that takes into account each class specificity and as a result utilizes different thresholds for different classes. We proposed investigate each object in classification map independently and decision should be: “is this whole object a noise or not?”. We consider each class independent and use connected component labeling technique for discriminating objects from classification map. Further different types of conditions based on sharpness and compactness where proposed for the investigated object. Accuracy and efficiency of this method with the proposed filtration method have been tested on the independent set and using the visual comparison with the results of utilizing common filters. Also, McNemar statistical test has been conducted to prove the statistically significant gain of utilizing proposed filtration methodology compare to common voting filter
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Feng, Yuchao, Jianwei Zheng, Mengjie Qin, Cong Bai, and Jinglin Zhang. "3D Octave and 2D Vanilla Mixed Convolutional Neural Network for Hyperspectral Image Classification with Limited Samples." Remote Sensing 13, no. 21 (November 2, 2021): 4407. http://dx.doi.org/10.3390/rs13214407.
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Owing to the outstanding feature extraction capability, convolutional neural networks (CNNs) have been widely applied in hyperspectral image (HSI) classification problems and have achieved an impressive performance. However, it is well known that 2D convolution suffers from the absent consideration of spectral information, while 3D convolution requires a huge amount of computational cost. In addition, the cost of labeling and the limitation of computing resources make it urgent to improve the generalization performance of the model with scarcely labeled samples. To relieve these issues, we design an end-to-end 3D octave and 2D vanilla mixed CNN, namely Oct-MCNN-HS, based on the typical 3D-2D mixed CNN (MCNN). It is worth mentioning that two feature fusion operations are deliberately constructed to climb the top of the discriminative features and practical performance. That is, 2D vanilla convolution merges the feature maps generated by 3D octave convolutions along the channel direction, and homology shifting aggregates the information of the pixels locating at the same spatial position. Extensive experiments are conducted on four publicly available HSI datasets to evaluate the effectiveness and robustness of our model, and the results verify the superiority of Oct-MCNN-HS both in efficacy and efficiency.
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Brandmeier, M., and Y. Chen. "LITHOLOGICAL CLASSIFICATION USING MULTI-SENSOR DATA AND CONVOLUTIONAL NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W16 (September 17, 2019): 55–59. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-55-2019.
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<p><strong>Abstract.</strong> Deep learning has been used successfully in computer vision problems, e.g. image classification, target detection and many more. We use deep learning in conjunction with ArcGIS to implement a model with advanced convolutional neural networks (CNN) for lithological mapping in the Mount Isa region (Australia). The area is ideal for spectral remote sensing as there is only sparse vegetation and besides freely available Sentinel-2 and ASTER data, several geophysical datasets are available from exploration campaigns. By fusing the data and thus covering a wide spectral range as well as capturing geophysical properties of rocks, we aim at improving classification accuracies and support geological mapping. We also evaluate the performance of the sensors on their own compared to a joint use as the Sentinel-2 satellites are relatively new and as of now there exist only few studies for geological applications. We developed an end-to-end deep learning model using Keras and Tensorflow that consists of several convolutional, pooling and deconvolutional layers. Our model was inspired by the family of U-Net architectures, where low-level feature maps (encoders) are concatenated with high-level ones (decoders), which enables precise localization. This type of network architecture was especially designed to effectively solve pixel-wise classification problems, which is appropriate for lithological classification. We spatially resampled and fused the multi-sensor remote sensing data with different bands and geophysical data into image cubes as input for our model. Pre-processing was done in ArcGIS and the final, fine-tuned model was imported into a toolbox to be used on further scenes directly in the GIS environment. The tool classifies each pixel of the multiband imagery into different types of rocks according to a defined probability threshold. Results highlight the power of using Sentinel-2 in conjunction with ASTER data with accuracies of 75% in comparison to only 70% and 73% for ASTER or Sentinel-2 data alone. These results are similar but examining the different classes shows that there are significant improvements for classes such as dolerite or carbonate sediments that are not that widely distributed in the area. Adding geophysical datasets reduced accuracies to 60%, probably due to an order of magnitude difference in spatial resolution. In comparison, Random Forest (RF) and Support Vector Machines (SVMs) that were trained on the same data only achieve accuracies of 46 % and 36 % respectively. Most insecurity is due to labelling errors and labels with mixed lithologies. However, results show that the U-Netmodel is a powerful alternative to other classifiers for medium-resolution multispectral data.</p>
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Padial-Iglesias, Mario, Pere Serra, Miquel Ninyerola, and Xavier Pons. "A Framework of Filtering Rules over Ground Truth Samples to Achieve Higher Accuracy in Land Cover Maps." Remote Sensing 13, no. 14 (July 6, 2021): 2662. http://dx.doi.org/10.3390/rs13142662.
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Remote Sensing (RS) digital classification techniques require sufficient, accurate and ubiquitously distributed ground truth (GT) samples. GT is usually considered “true” per se; however, human errors, or differences in criteria when defining classes, among other reasons, often undermine this veracity. Trusting the GT is so crucial that protocols should be defined for making additional quality checks before passing to the classification stage. Fortunately, the nature of RS imagery allows setting a framework of quality controls to improve the confidence in the GT areas by proposing a set of filtering rules based on data from the images themselves. In our experiment, two pre-existing reference datasets (rDS) were used to obtain GT candidate pixels, over which inconsistencies were identified. This served as a basis for inferring five key filtering rules based on NDVI data, a product available from almost all RS instruments. We evaluated the performance of the rules in four temporal study cases (under backdating and updating scenarios) and two study areas. In each case, a set of GT samples was extracted from the rDS and the set was used both unfiltered (original) and filtered according to the rules. Our proposal shows that the filtered GT samples made it possible to solve usual problems in wilderness and agricultural categories. Indeed, the confusion matrices revealed, on average, an increase in the overall accuracy of 10.9, a decrease in the omission error of 16.8, and a decrease in the commission error of 14.0, all values in percent points. Filtering rules corrected inconsistencies in the GT samples extracted from the rDS by considering inter-annual and intra-annual differences, scale issues, multiple behaviours over time and labelling misassignments. Therefore, although some intrinsic limitations have been detected (as in mixed forests), the protocol allows a much better Land Cover mapping thanks to using more robust GT samples, something particularly important in a multitemporal context in which accounting for phenology is essential.
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Martí i Rabadán, Miquel, Alessandro Pieropan, Hossein Azizpour, and Atsuto Maki. "Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks." Proceedings of the Northern Lights Deep Learning Workshop 4 (January 23, 2023). http://dx.doi.org/10.7557/18.6794.
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We propose Dense FixMatch, a simple method for online semi-supervised learning of dense and structured prediction tasks combining pseudo-labeling and consistency regularization via strong data augmentation. We enable the application of FixMatch in semi-supervised learning problems beyond image classification by adding a matching operation on the pseudo-labels. This allows us to still use the full strength of data augmentation pipelines, including geometric transformations. We evaluate it on semi-supervised semantic segmentation on Cityscapes and Pascal VOC with different percentages of labeled data and ablate design choices and hyper-parameters. Dense FixMatch significantly improves results compared to supervised learning using only labeled data, approaching its performance with 1/4 of the labeled samples.
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Han, Yongqi, Lianglun Cheng, Guoheng Huang, Guo Zhong, Jiahua Li, Xiaochen Yuan, Hongrui Liu, Jiao Li, Jian Zhou, and Muyan Cai. "Weakly supervised semantic segmentation of histological tissue via attention accumulation and pixel-level contrast learning." Physics in Medicine & Biology, December 28, 2022. http://dx.doi.org/10.1088/1361-6560/acaeee.
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Abstract Objective. Histopathology image segmentation can assist medical professionals in identifying and diagnosing diseased tissue more efficiently. Although fully supervised segmentation models have excellent performance, the annotation cost is extremely expensive. Weakly supervised models are widely used in medical image segmentation due to their low annotation cost. Nevertheless, these weakly supervised models have difficulty in accurately locating the boundaries between different classes of regions in pathological images, resulting in a high rate of false alarms. Our objective is to design a weakly supervised segmentation model to resolve the above problems. Approach. The segmentation model is divided into two main stages, the generation of pseudo labels based on Class Residual Attention Accumulation Network (CRAANet) and the semantic segmentation based on Pixel Feature Space Construction Network (PFSCNet). CRAANet provides attention scores for each class through the Class Residual Attention (CRA) module, while the Attention Accumulation (AA) module overlays the attention feature maps generated in each training epoch. PFSCNet employs a network model containing an inflated convolutional residual neural network and a multi-scale feature-aware module as the segmentation backbone, and proposes Dense Energy Loss (DE Loss) and Pixel Clustering (PCL) are modules based on contrast learning to solve the pseudo-labeling-inaccuracy problem. Main results. We validate our method using the lung adenocarcinoma (LUAD-HistoSeg) dataset and the breast cancer (BCSS) dataset. The results of the experiments show that our proposed method outperforms other state-of-the-art methods on both datasets in several metrics. This suggests that it is capable of performing well in a wide variety of histopathological image segmentation tasks. Significance. We propose a weakly supervised semantic segmentation network that achieves approximate fully supervised segmentation performance even in the case of incomplete labels. The proposed attention accumulation and pixel-level contrast learning also make the edges more accurate and can well assist pathologists in their research.