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A top-down approach for semantic segmentation of big remote sensing images
摘要： The increasing amount of remote sensing data has opened the door to new challenging research topics. Nowadays, significant efforts are devoted to pixel and object based classification in case of massive data. This paper addresses the problem of semantic segmentation of big remote sensing images. To do this, we proposed a top-down approach based on two main steps. The first step aims to compute features at the object-level. These features constitute the input of a multi-layer feed-forward network to generate a structure for classifying remote sensing objects. The goal of the second step is to use this structure to label every pixel in new images. Several experiments are conducted based on real datasets and results show good classification accuracy of the proposed approach. In addition, the comparison with existing classification techniques proves the effectiveness of the proposed approach especially for big remote sensing data.
关键词： Neural networks,Remote sensing images,Big data,Semantic segmentation
更新于2025-09-23 15:23:52
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Exploiting superior CNN-based iris segmentation for better recognition accuracy
摘要： CNN-based iris segmentations have been proven to be superior to traditional iris segmentation techniques in terms of segmentation error metrics. To properly utilize them in a traditional biometric recognition systems requires a parameterization of the iris, based on the generated segmentation, to obtain the normalised iris texture typically used for feature extraction. This is an unsolved problem. We will introduce a method to parameterize CNN based segmentation, bridging the gap between CNN based segmentation and the rubbersheet-transform. The parameterization enables the CNN segmentation as full segmentation step in any regular iris biometric system, or alternatively the segmentation can be utilized as a noise mask for other segmentation methods. Both of these options will be evaluated.
关键词： Iris segmentation,CNN,Parameterization of iris masks,Iris biometrics
更新于2025-09-23 15:23:52
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Impact of segmentation density on spectral domain optical coherence tomography assessment in Stargardt disease
摘要： Purpose Automated spectral domain optical coherence tomography (SD-OCT) segmentation algorithms currently do not perform well in segmenting individual intraretinal layers in eyes with Stargardt disease (STGD). We compared selective B-scan segmentation strategies for generating mean retinal layer thickness and preserved area data from SD-OCT scans in patients with STGD1. Methods Forty-five eyes from 40 Stargardt patients were randomly selected from the ongoing Natural History of the Progression of Atrophy Secondary to Stargardt Disease (ProgStar) study. All eyes underwent SD-OCT using a standard macular volume consisting of 1024 × 49 equally spaced B-scans within a 20 × 20 degree field centered on the fovea. All 49 B-scans were segmented manually to quantify total retina, outer nuclear layer (ONL), photoreceptor inner segments, photoreceptor outer segments (OS), and retinal pigment epithelial layer (RPE). Mean thickness and total area were generated using all 49 B-scans (spaced 122 μm apart), 25 B-scans (every other B-scan, spaced 240 μm apart), 17 B-scans (every third scan, 353 μm apart), and 13 B-scans (every fourth scan, 462 μm apart), as well as by using an Badaptive^ method where a subset (minimum 25 B-scans) of B-scans that the grader deemed as significantly different from adjacent B-scans were utilized. Mean absolute and percentage errors were calculated for macular thickness and area of different retinal layers for the different B-scan subset selection strategies relative to using all 49 B-scans, which was considered the reference or ground truth. Results Mean thickness and area measurements were significantly different for any regularly spaced reduction in B-scan density relative to the ground truth. When an adaptive approach was applied using a minimum of half the scans, the differences relative to ground truth were no longer significantly different. The mean percent differences for the area and thicknesses of the various layers ranged from 0.02 to 33.66 (p < 0.05 for all comparisons) and 0.44 to 7.24 (p > 0.05) respectively. Conclusion Manual segmentation of a subset of B-scans using an adaptive strategy can yield thickness and area measurements of retinal sublayers comparable to the reference ground truth derived from using all B-scans in the volume. These results may have implications for increasing the efficiency of SD-OCT grading strategies in clinical trials for STGD and other related macular degenerative disorders.
关键词： Stargardt,Segmentation density,Spectral domain optical coherence tomography,Retinal layers
更新于2025-09-23 15:23:52
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Combining SUN-based visual attention model and saliency contour detection algorithm for apple image segmentation
摘要： Accurate segmentation of apple fruit under natural illumination conditions provides benefits for growers to plan relevant applications of nutrients and pesticides. It also plays an important role for monitoring the growth status of the fruit. However, the segmentation of apples throughout various growth stages had only achieved a limited success so far due to the color changes of apple fruit as it matures as well as occlusion and the non-uniform background of apple images acquired in an orchard environment. To achieve the segmentation of apples with different colors and with various illumination conditions for the whole growth stage, a segmentation method independent of color was investigated. Features, including saliency and contour of the image, were combined in this algorithm to remove background and extract apples. Saliency using natural statistics (SUN) visual attention model was used for background removal and it was combined with threshold segmentation algorithm to extract salient binary region of apple images. The centroids of the obtained salient binary region were then extracted as initial seed points. Image sharpening, globalized probability of boundary-oriented watershed transform-ultrametric contour map (gPb-OWT-UCM) and Otsu algorithms were applied to detect saliency contours of images. With the built seed points and extracted saliency contours, a region growing algorithm was performed to accurately segment apples by retaining as many fruit pixels and removing as many background pixels as possible. A total of 556 apple images captured in natural conditions were used to evaluate the effectiveness of the proposed method. An average segmentation error (SE), false positive rate (FPR), false negative rate (FNR) and overlap Index (OI) of 8.4, 0.8, 7.5 and 90.5% respectively, were achieved and the performance of the proposed method outperformed other six methods in comparison. The method developed in this study can provide a more effective way to segment apples with green, red, and partially red colors without changing any features and parameters and therefore it is also applicable for monitoring the growth status of apples.
关键词： Fruit segmentation,Apples,Region growing,gPb-OWT-UCM,Growth stage,SUN
更新于2025-09-23 15:23:52
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A new effective and powerful medical image segmentation algorithm based on optimum path snakes
摘要： Novel segmentation methods based on models of deformable active contours are constantly proposed and validated in different fields of knowledge, with the aim to make the detection of the regions of interest standard. This paper propose a new method called Optimum Path Snakes (OPS), a new adaptive algorithm and free of parameters to define the total energy of a active contour model with automatic initialization and stop criteria. In the experimental assessment, the OPS is compared against some approaches commonly used in the following fields, such as vector field convolution, gradient vector flow, and other specialists methods for lung segmentation using thorax computed tomography images. The segmentation of regions with stroke was carried out with methods based on region growing, watershed and a specialist level set approach. Statistical validations metrics using Dice coefficient (DC) and Hausdorff distance (HD) were also evaluated, as well as the processing time. The results showed that the OPS is a promising tool for image segmentation, presenting satisfactory results for DC and HD, and, many times, superior to the other algorithms it was compared with, including those generated by specialists. Another advantage of the OPS is that it is not restricted to specific types of images, neither applications.
关键词： Image Segmentation,Optimum Path Forest,Snakes,Active Contour Method
更新于2025-09-23 15:23:52
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An image segmentation method of a modified SPCNN based on human visual system in medical images
摘要： An image segmentation method of a modified simplified pulse-coupled neural network (MSPCNN) based on human visual system (HVS) is proposed for medical images. The method successfully determines the stimulus input of the MSPCNN according to the characteristics of PCNN and HVS. In order to accomplish the goal, we attempt to deduce the sub-intensity range of central neurons firing by introducing neighboring firing matrix Q and calculating intensity distribution range based on a new MSPCNN(NMSPCNN), and then reveal the way how sub-intensity range parameter Sint generates the stimulus input Sioij closer to HVS. Besides, we try to substitute the above stimulus input into the MSPCNN to extract more suitable lesions for medical images. In contrast to prevalent PCNN models, the MSPCNN has higher segmentation accuracy rates and lower computational complexity because of the parameter setting method. Finally, the proposed method comparing with the state-of-the-art methods has a better performance, presenting the overall metric OEM with MIAS of 0.8784, DDSM of 0.8606 and gallstones of 0.8585.
关键词： Sub-intensity Range,Modified Simplified Pulse-coupled Neural Network,Image Segmentation,Stimulus Input,Human Visual System
更新于2025-09-23 15:23:52
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SCN: Switchable Context Network for Semantic Segmentation of RGB-D Images
摘要： Context representations have been widely used to profit semantic image segmentation. The emergence of depth data provides additional information to construct more discriminating context representations. Depth data preserves the geometric relationship of objects in a scene, which is generally hard to be inferred from RGB images. While deep convolutional neural networks (CNNs) have been successful in solving semantic segmentation, we encounter the problem of optimizing CNN training for the informative context using depth data to enhance the segmentation accuracy. In this paper, we present a novel switchable context network (SCN) to facilitate semantic segmentation of RGB-D images. Depth data is used to identify objects existing in multiple image regions. The network analyzes the information in the image regions to identify different characteristics, which are then used selectively through switching network branches. With the content extracted from the inherent image structure, we are able to generate effective context representations that are aware of both image structures and object relationships, leading to a more coherent learning of semantic segmentation network. We demonstrate that our SCN outperforms state-of-the-art methods on two public datasets.
关键词： Context representation,convolutional neural network (CNN),RGB-D images,semantic segmentation
更新于2025-09-23 15:23:52
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Learning Deep Conditional Neural Network for Image Segmentation
摘要： Combining Convolutional Neural Networks (CNNs) with Conditional Random Fields (CRFs) achieve great success among recent object segmentation methods. There are two advantages by such usage. First, CNNs can extract low-level features, which are very similar to the extracted features in primates’ primary visual cortex (V1). Second, CRFs can set up the relationship between input features and output labels in a direct way. In this paper, we extend the first advantage by using CNNs for low-level feature extraction and Structured Random Forest (SRF) based border ownership detector for high-level feature extraction, which are similar to the outputs of primates secondary visual cortex (V2). Compared to the CRF model, an improved Conditional Boltzmann Machine (CBM) which has a multi-channel visible layer are proposed to model the relationship between predicted labels, local and global contexts of objects with multi-scale and multilevel features. Besides, our proposed CBM model is extended for object parsing by using multi visible branches instead of a single visible layer of CBM, which can not only segment the whole body but also the parts of the body under. These visible branches use each branch for the segmentation of the whole body or one of the body parts. All the branches share the same hidden layers of CBM and train the branches under an iterative way. By exploiting object parsing, the whole body segmentation performance of object is improved. To refine the segmentation output, two kinds of optimization algorithms are proposed. The superpixel based algorithm can re-label the overlapped regions of multi-kinds of objects. The other curve correction algorithm corrects the edges of segmented object parts by using smooth edges under a curve similarity criterion. Experiments demonstrate that our models yield competitive results for object segmentation on PASCAL VOC 2012 dataset and for object parsing on PennFudan Pedestrian Parsing dataset, Pedestrian Parsing Surveillance Scenes dataset, Horse-Cow parsing dataset, PASCAL Quadrupeds dataset.
关键词： Convolutional Neural Networks,Conditional Boltzmann Machines,Segmentation,object parsing
更新于2025-09-23 15:23:52
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[IEEE 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) - Wuhan (2018.3.22-2018.3.23)] 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) - Image Processing Based Indoor Localization System for Assisting Visually Impaired People
摘要： Indoor localization or indoor positioning system is a known as a process of detecting position of any object or people inside a building or room by different sensory data collected from different devices using different techniques such as radio waves, magnetic fields, acoustic signals or other procedures. However, lacking of a standard localization system is still a very big concern. Solution of this issue can be very beneficial for people in many cases but it can be especially very beneficial for the visually impaired people. In this paper, an image processing based indoor localization system has been developed using OpenCV and Python by following color detection technique to detect position of the user with maximum accuracy and then location of user is determined by analyzing that location matrix. Location accuracy depends on the size of the matrix and successful identification of target color. Firebase real time database was added to the system which made real time operations between server and the user end device easier. To justify the proposed model, successful experiments were conducted in indoor environments as well and correct result was achieved each time by detecting accurate locations. This will be very advantageous to observe the fully or partially sightless people and guide them towards their destination and also to inspect them for their security purpose.
关键词： Color Segmentation,Indoor localization,Image Processing,Indoor positioning system,Wireless communication,Connected object detection
更新于2025-09-23 15:23:52
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An automatic multi-thread image segmentation embedded system for surface plasmon resonance sensor
摘要： In order to reduce the uncertainties associated with manual selection of regions of interest (ROIs) commonly used in Surface Plasmon Resonance (SPR) imaging system, we proposed and implemented an automatic image segmentation method in an embedded system to facilitate the potential real-time applications. Intuitive marker-controlled watershed algorithm is developed to segment ROIs (reaction, blank, and background regions) from images acquired from an experimental image SPR system. The marker assignment algorithms and pre-processing algorithms are executed in parallel by multi-threading programming on the multi-core embedded system to both real-time and good quality of segmentation. This method exhibited a good robustness in a series of ROIs segmentation test. Furthermore, the intensity response from triplicate detection of glucose standard solutions indicated a good reproducibility of data. The linear range was from 2.5 mg/mL to 20.4 mg/mL, with a correlation coefficient (R2) of 0.999 and sensitivity of 2.69 a.u./mg/mL. In conclusion, the proposed automatic image segmentation method effectively makes the measurement more precise and simplified.
关键词： Optical sensor,Surface plasmon resonance (SPR),Biosensor,Watershed algorithm,Image segmentation
更新于2025-09-23 15:23:52