Добірка наукової літератури з теми "Pixel labeling problems"

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.

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.[...]

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.

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.

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.

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.

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.

<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>

<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>

Recently, higher-order Markov random field (MRF) models have been successfully applied to problems in computer vision, especially scene understanding problems. One successful higher-order MRF model for scene understanding is the consistency model [Kohli and Kumar, 2010; Kohli et al., 2009] and earlier work by Ladicky et al. [2009, 2013] which contain higher-order potentials composed of lower linear envelope functions. In semantic image segmentation problems, which seek to identify the pixels of images with pre-defined labels of objects and backgrounds, this model encourages consistent label assignments over segmented regions of images. However, solving this MRF problem exactly is generally NP-hard; instead, efficient approximate inference algorithms are used. Furthermore, the lower linear envelope functions involve a number of parameters to learn. But, the typical cross-validation used for pairwise MRF models is not a practical method for estimating such a large number of parameters. Nevertheless, few works have proposed efficient learning methods to deal with the large number of parameters in these consistency models. In this thesis, we propose a unified inference and learning framework for the consistency model. We investigate various issues and present solutions for inference and learning with this higher-order MRF model as follows. First, we derive two variants of the consistency model for multi-class pixel labeling tasks. Our model defines an energy function scoring any given label assignments over an image. In order to perform Maximum a posteriori (MAP) inference in this model, we minimize the energy function using move-making algorithms in which the higher-order problems are transformed into tractable pairwise problems. Then, we employ a max-margin framework for learning optimal parameters. This learning framework provides a generalized approach for searching the large parameter space. Second, we propose a novel use of the Gaussian mixture model (GMM) for encoding consistency constraints over a large set of pixels. Here, we use various oversegmentation methods to define coherent regions for the consistency potentials. In general, Mean shift (MS) produces locally coherent regions, and GMM provides globally coherent regions, which do not need to be contiguous. Our model exploits both local and global information together and improves the labeling accuracy on real data sets. Accordingly, we use multiple higher-order terms associated with each over-segmentation method. Our learning framework allows us to deal with the large number of parameters involved with multiple higher-order terms. Next, we explore a dual decomposition (DD) method for our multi-class consistency model. The dual decomposition MRF (DD-MRF) is an alternative method for optimizing the energy function. In dual decomposition, a complex MRF problem is decomposed into many easy subproblems and we optimize the relaxed dual problem using a projected subgradient method. At convergence, we expect a global optimum in the dual space because it is a concave maximization problem. To optimize our higher-order DD-MRF exactly, we propose an exact minimization algorithm for solving the higher-order subproblems. Moreover, the minimization algorithm is much more efficient than graph-cuts. The dual decomposition approach also solves the max-margin learning problem by minimizing the dual losses derived from DD-MRF. Here, our minimization algorithm allows us to optimize the DD learning exactly and efficiently, which in most cases finds better parameters than the previous learning approach. Last, we focus on improving labeling accuracies of our higher-order model by combining mid-level features, which we call region features. The region features help customize the general envelope functions for individual segmented regions. By assigning specified weights to the envelope functions, we can choose subsets of highly likely labels for each segmented region. We train multiple classifiers with region features and aggregate them to increase prediction performance of possible labels for each region. Importantly, introducing these region features does not change the previous inference and learning algorithms.

Conventional convolutional neural networks (CNNs) have achieved great success in image semantic segmentation. Existing methods mainly focus on learning pixel-wise labels from an image directly. In this paper, we advocate tackling the pixel-wise segmentation problem by considering the image-level classification labels. Theoretically, we analyze and discuss the effects of image-level labels on pixel-wise segmentation from the perspective of information theory. In practice, an end-to-end segmentation model is built by fusing the image-level and pixel-wise labeling networks. A generative network is included to reconstruct the input image and further boost the segmentation model training with an auxiliary loss. Extensive experimental results on benchmark dataset demonstrate the effectiveness of the proposed method, where good image-level labels can significantly improve the pixel-wise segmentation accuracy.

There is a vast body of theoretical research on lifted inference in probabilistic graphical models (PGMs). However, few demonstrations exist where lifting is applied in conjunction with top of the line applied algorithms. We pursue the applicability of lifted inference for computer vision (CV), with the insight that a globally optimal (MAP) labeling will likely have the same label for two symmetric pixels. The success of our approach lies in efficiently handling a distinct unary potential on every node (pixel), typical of CV applications. This allows us to lift the large class of algorithms that model a CV problem via PGM inference. We propose a generic template for coarse-to-fine (C2F) inference in CV, which progressively refines an initial coarsely lifted PGM for varying quality-time trade-offs. We demonstrate the performance of C2F inference by developing lifted versions of two near state-of-the-art CV algorithms for stereo vision and interactive image segmentation. We find that, against flat algorithms, the lifted versions have a much superior anytime performance, without any loss in final solution quality.