Thermal infrared imaging has attracted widespread attention in many fields due to the advantages of all-weather imaging and strong penetration. However, existing methods for thermal infrared novel-view synthesis often produce results with coarse details and floating artifacts, primarily caused by physical factors such as atmospheric transmission effects and thermal conduction. These challenges hinder accurate reconstruction of intricate structures and temperature distributions in thermal scenes, limiting the practical utility of previous approaches. To address these limitations, this paper introduces a physics-induced 3D Gaussian splatting method named Thermal3D-GS, the first novel-view synthesis method that relies exclusively on thermal infrared image. Thermal3D-GS begins by modeling atmospheric transmission effects and thermal conduction in three-dimensional media using neural networks. Additionally, considering the sparse features of infrared images, sparse feature priors are designed to improve the reconstruction accuracy of thermal infrared images. Furthermore, to validate the effectiveness of our method, the first large-scale benchmark dataset named Thermal Infrared Novel-view Synthesis Dataset (TI-NSD) is created. This dataset comprises 50 authentic thermal infrared video scenes, covering indoor, outdoor, traffic and uncrewed aerial vehicle (UAV) scenarios, with a total of 15,213 frames of thermal infrared image data. In addition, an expanded validation thermal infrared dataset, which includes three high-resolution scenes and five special scenes under varying atmospheric conditions and complex propagation media is constructed to assess generalization performance of the proposed method. Based on this dataset, this paper experimentally verifies the effectiveness of Thermal3D-GS. The results indicate that our method outperforms the baseline method with a 3.19 dB improvement in PSNR and significantly addresses the issues of floaters and indistinct edge features present in the baseline method.

With the rapid development of pathology foundation models, there is a growing demand for efficient fine-tuning strategies tailored to downstream tasks. However, existing parameter-efficient fine-tuning approaches are largely task-agnostic and exhibit limited generalization to histopathological images, particularly for unseen cancers and stains, due to substantial stain variability and the complexity of tissue microenvironments. To address these challenges, we present Histopathology-induced Adapter (HiAdapter), which incorporates domain-specific insights into staining and imaging mechanisms of histopathology. HiAdapter reconstructs stain-invariant representations via a Stain-invariant Adapter (S-Adapter) and integrates morphological features through a Morphology-aware Adapter (M-Adapter), effectively bridging the gap between low-level optical properties and high-level tissue semantics. Additionally, we introduce a Pathology Prototypical Contrastive Loss (PPCLoss) to reduce inter-class similarity and mitigate intra-class heterogeneity, enhancing feature discriminability. Extensive experiments using three pathology foundation models (CTransPath, CONCH and UNI) across six benchmarks, including two public datasets, an osteosarcoma tissue classification dataset (56,178 patches) and a chondrosarcoma necrosis classification dataset (3,867 patches) for unseen cancers generalization, as well as an IHC-stained dataset (4,967 patches) and an HIF1A IHC-stained dataset (4,433 patches) for unseen stains generalization, demonstrate the effectiveness of HiAdapter in both efficiency and accuracy. HiAdapter achieves an average improvement of 2.15 in F1 and 1.55 in accuracy over the second-best performer, maintaining strong biological and diagnostic interpretability. External validation on an independent osteosarcoma dataset (9,535 patches) and WSI-level survival analysis (178 slides) further confirm the superior generalizability and underscore the potential for patient-level diagnosis and prognosis in clinical practice. Our code is available at https://github.com/idata-ora/HiAdapter.

Precise segmentation of thermal infrared images is crucial in domains like surveillance, medical diagnostics, intelligent transportation, accurate guidance and remote sensing. However, current thermal segmentation methods often oversimplify by treating thermal images as grayscale, neglecting vital physical factors such as thermal imaging effects and material information, thereby constraining segmentation precision. To address these limitations, we propose TherNet, a novel thermal infrared segmentation framework integrating thermal imaging effects and material physical information. The study elucidates the impacts of object radiation, inter-object thermal exchange, atmospheric scattering, and camera thermal inertia on thermal infrared imaging, developing four modules to model or rectify these physical processes. To validate the proposed framework, two large-scale infrared datasets were created: TI-Cityscapes for multi-class semantic segmentation in traffic scenes (4,200 frames, 18 classes), and TBRSD for single-object blindroad segmentation (5,180 frames from a pedestrian perspective). The proposed methods achieved SoTA performance across three infrared semantic segmentation datasets and the blind road segmentation dataset, underscoring the pivotal role of leveraging physical properties. TherNet provides innovative perspectives and robust benchmarks for future developments in the domain.

Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) and computed tomography angiography (CTA) is essential in providing supportive information for diagnosing and treatment planning of multiple intracranial vascular diseases. Different imaging modalities utilize distinct principles to visualize the cerebral vasculature, which leads to the limitations of expensive annotations and performance degradation while training and deploying deep learning models. In this paper, we propose an unsupervised domain adaptation framework CereTS to perform translation and segmentation of cross-modality unpaired cerebral angiography. Considering the commonality of vascular structures and stylistic textures as domain-invariant and domain-specific features, CereTS adopts a multi-level domain alignment pattern that includes an image-level cyclic geometric consistency constraint, a patch-level masked contrastive constraint and a feature-level semantic perception constraint to shrink domain discrepancy while preserving consistency of vascular structures. Conducted on a publicly available TOF-MRA dataset and a private CTA dataset, our experiment shows that CereTS outperforms current state-of-the-art methods by a large margin.

Deep Image Prior (DIP) is a powerful unsupervised learning image restoration technique. However, DIP struggles when handling complex degradation scenarios involving mixed image artifacts. To address this limitation, we propose a novel technique to enhance DIP’s performance in handling mixed image degradation. Our method leverages additional deep denoiser, which is deployed as a denoising engine in the regularization by denoising (RED) framework. A new objective function is constructed by combining DIP with RED, and solved by the alternating direction method of multiplier (ADMM) algorithm. Our method explicitly learns a more comprehensive representation of the underlying image structure and being robust to different types of degradation. Experimental results demonstrate the effectiveness of our method, showing effective improvements in restoring images corrupted by mixed degradation on several image restoration tasks, such as image inpainting, super-resolution and deblurring.

Accurate molecular representation plays a crucial role in expediting the process of drug discovery. Graph neural networks (GNNs) have demonstrated robust capabilities in molecular representation learning, adept at capturing structural and spatial information in molecular graphs. For molecular representation learning, most previous GNN methods are specialized in dealing with 2D or 3D molecular data formats. By further fusing the geometric attributes and structural features of molecules, we can elevate the performance of molecular representation. To realize this, we present a novel geometry-augmented molecular representation learning model, designed to effectively encode both the 2D structural and 3D spatial information inherent in molecular graphs. By incorporating structural and spatial information as attention biases in the graph Transformer framework, our model offers a comprehensive architecture that introduces molecular structural details at both atom and bond levels. We further propose a geometry information fusion module to encode the geometry information within 3D molecular graphs. The experimental results show the efficacy of our model, demonstrating its ability to achieve competitive performance when compared to state-of-the-art (SOTA) models in various property prediction tasks.

Computer vision-based walking assistants are prominent tools for aiding visually impaired people in navigation. Blind road segmentation is a key element in these walking assistant systems. However, most walking assistant systems rely on visual light images, which is dangerous in weak illumination environments such as darkness or fog. To address this issue and enhance the safety of vision-based walking assistant systems, we developed a thermal infrared blind road segmentation neural network (TINN). In contrast to conventional segmentation techniques that primarily concentrate on enhancing feature extraction and perception, our approach is geared towards preserving the inherent radiation characteristics within the thermal imaging process. Initially, we modelled two critical factors in thermal infrared imaging - thermal light atmospheric transmission and thermal inertia effect. Subsequently, we use an encoder-decoder architecture to fuse the feathers extracted by the two modules. Additionally, to train the network and evaluate the effectiveness of the proposed method, we constructed a large-scale thermal infrared blind road segmentation dataset named TBRSD consists 5180 pixel-level manual annotations. The experimental results demonstrate that our method outperforms existing techniques and achieves state-of-the-art performance in thermal blind road segmentation, as validated on benchmark thermal infrared semantic segmentation datasets such as MFNet and SODA. The dataset and our code are both publicly available in https://github.com/chenjzBUAA/TBRSD or http://xzbai.buaa.edu.cn/datasets.html.

Infrared small target detection plays an important role in military and civilian fields while it is difficult to be solved by deep learning (DL) technologies due to scarcity of data and strong interclass imbalance. To relieve scarcity of data, we build a massive dataset IRDST, which contains 142 727 frames. Also, we propose a receptive-field and direction-induced attention network (RDIAN), which is designed using the characteristics of target size and grayscale to solve the interclass imbalance between targets and background. Using convolutional layers with different receptive fields in feature extraction, target features in different local regions are captured, which enhances the diversity of target features. Using multidirection guided attention mechanism, targets are enhanced in low-level feature maps. Experimental results with comparison methods and ablation study demonstrate effective detection performance of our model. Dataset and code will be available athttps://xzbai.buaa.edu.cn/datasets.html.

Infrared image segmentation is a challenging task, due to interference of complex background and appearance inhomogeneity of foreground objects. A critical defect of fuzzy clustering for infrared image segmentation is that the method treats image pixels or fragments in isolation. In this paper, we propose to adopt self-representation from sparse subspace clustering in fuzzy clustering, aiming to introduce global correlation information into fuzzy clustering. Meanwhile, to apply sparse subspace clustering for non-linear samples from an infrared image, we leverage membership from fuzzy clustering to improve conventional sparse subspace clustering. The contributions of this paper are fourfold. First, by introducing self-representation coefficients modeled in sparse subspace clustering based on high-dimensional features, fuzzy clustering is capable of utilizing global information to resist complex background as well as intensity inhomogeneity of objects, so as to improve clustering accuracy. Second, fuzzy membership is tactfully exploited in the sparse subspace clustering framework. Thereby, the bottleneck of conventional sparse subspace clustering methods, that they could be barely applied to nonlinear samples, can be surmounted. Third, as we integrate fuzzy clustering and subspace clustering in a unified framework, features from two different aspects are employed, contributing to precise clustering results. Finally, we further incorporate neighbor information into clustering, thus effectively solving the uneven intensity problem in infrared image segmentation. Experiments examine the feasibility of proposed methods on various infrared images. Segmentation results demonstrate the effectiveness and efficiency of the proposed methods, which proves the superiority compared to other fuzzy clustering methods and sparse space clustering methods.

Video semantic segmentation has achieved great success, which is significant for road scene understanding. However, semantic segmentation remains challenging in poor illumination and inclement weather. Thermal camera, highly invariant to light and highly penetrating to rain and fog, enables semantic segmentation to work under challenging conditions. Thus, this paper explores semantic segmentation in thermal videos to broaden the scope of the application of road scene understanding. We offer the first thermal video semantic segmentation dataset TVSS including 1695 thermal videos with 50850 frames in road scenes. It is available at:https://xzbai.buaa.edu.cn/datasets.html. TVSS is finely annotated by 17 categories at the frame rate of 1fps, with a labeled pixel density of 98.9%. Existing video semantic segmentation methods rely on the amount of labels and the representation power of backbones, which cannot achieve ideal results on thermal videos. Thus, we introduce a multi-granularity contrastive learning based thermal video semantic segmentation model (MGCL), which explores the abundant unlabeled frames to boost the supervised segmentation. Specifically, MGCL constructs multi-granularity self-supervised signals on unlabeled thermal videos by contrastive learning, including the intra-frame context generalization loss, the intra-clip temporal consistency loss, and the inter-video category discrimination loss. In addition, a hard anchor sampling strategy is introduced to focus on hard-classify pixels for further performance improvement. Extensive experiments on TVSS demonstrate the superior performance of MGCL in both accuracy and efficiency. Compared to the 12 state-of-the-art semantic segmentation methods, MGCL achieves 2.8% to 8.1% gains in mIoU performance while maintaining the inference speed.

Infrared ship segmentation is extensively applied in military fields. Due to noise and intensity inhomogeneity, the segmentation of infrared ship is a challenging task. The fuzzy c-means (FCM) clustering algorithm is widely used in image segmentation. However, traditional FCM is sensitive to noise and unable to obtain desirable segmentation results for infrared ship images. In this article, a novel probability induced intuitionistic FCM clustering algorithm is proposed to address the problem. First, the target probability information is incorporated into intuitionistic FCM to induce and refine membership which is affected by interferences. Second, by making use of neighborhood information in the form of a regularization term, the proposed method could suppress intensity inhomogeneity as well as maintain image details. Experimental results demonstrate that the proposed method could achieve better results than 12 other comparing algorithms for infrared ship segmentation.

Semantic segmentation in urban scenes is widely used in applications of intelligent transportation systems (ITS). In urban scenes, thermal infrared (TIR) images can be captured in weak illumination conditions or in the presence of obscuration (e.g., light fog, smoke). Therefore, TIR images have great potential to endow automated intelligent vehicles or assist navigation systems. However, TIR imaging is blurry and low-contrast due to the absorption by atmospheric gases and heat transfer effect. Hence, TIR semantic segmentation in urban scenes has rarely been explored even though it has a wide range of scenarios in ITS. To overcome this limitation, we analyze the light transport of TIR light. Our analysis reveals that contours are the reliable features shared by TIR and Visible Spectrum (VS) light. Inspired by this, we attempt to transfer joint features from VS domain to TIR domain. Thus, we propose a curriculum domain adaptation method to guide the TIR urban scene semantic segmentation task from VS domain through contours. Moreover, to evaluate the proposed model, we build TIR-SS: an open-for-request dataset consisting of TIR images and pixel level annotations of 8 classes in urban scenes. Qualitative and quantitative experimental results on the dataset indicate that the proposed domain adaptation method outperforms related methods on this TIR semantic segmentation task.

Cerebrovascular segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) volumes is essential for a variety of diagnostic and analytical applications. However, accurate cerebrovascular segmentation in 3D TOF-MRA is faced with multiple issues, including vast variations in cerebrovascular morphology and intensity, noisy background, and severe class imbalance between foreground cerebral vessels and background. In this work, a 3D adversarial network model called A-SegAN is proposed to segment cerebral vessels in TOF-MRA volumes. The proposed model is composed of a segmentation network A-SegS to predict segmentation maps, and a critic network A-SegC to discriminate predictions from ground truth. Based on this model, the aforementioned issues are addressed by the prevailing visual attention mechanism. First, A-SegS is incorporated with feature-attention blocks to filter out discriminative feature maps, though the cerebrovascular has varied appearances. Second, a hard-example-attention loss is exploited to boost the training of A-SegS on hard samples. Further, A-SegC is combined with an input-attention layer to attach importance to foreground cerebrovascular class. The proposed methods were evaluated on a self-constructed voxel-wise annotated cerebrovascular TOF-MRA segmentation dataset, and experimental results indicate that A-SegAN achieves competitive or better cerebrovascular segmentation results compared to other deep learning methods, effectively alleviating the above issues.

The goal of zero-shot learning (ZSL) is to transfer knowledge learned from seen classes during training to unseen classes for testing, with the help of auxiliary information, such as attributes and descriptions. Most of the existing methods view ZSL as a label-embedding problem, in which class and image representations are embedded to a common space. However, many methods either show a bias toward seen classes caused by the projection domain-shift problem, or sacrifice the performance of seen classes to generalize to unseen ones. In this article, we present an embedding approach for ZSL, which is motivated by human recognition memory, namely, recollection and familiarity (R&F). We propose a decoder to regularize the nonlinear mapping between the semantic space and the visual space, which represents the reasonable recollection process, and use a residual block to refine the recognition ability for seen classes, which indicates the familiarity process. R&F can generalize well to unseen classes, while retaining the discriminative ability for the seen classes. Extensive experiments are conducted on Animals with Attribute (AwA1), Animals with Attributes 2 (AwA2), Attribute Pascal&Yahoo (aPY), SUN Attribute (SUN), Caltech-UCSD-Birds 200-2011 (CUB), and ImageNet databases. As qualitative and quantitative results show, the proposed approach outperforms state-of-the-art embedding-based methods by a large margin and significantly alleviates the projection domain-shift problem.

Interferential background, boundary uncertainty, and noises are usually involved in infrared pedestrian imaging, which erect barrier for accurate segmentation. To counter the conundrum rising in these cases, we present a novel intuitionistic fuzzy clustering-based segmentation method, which integrates structural symmetry and local homoplasy information, for precise infrared pedestrian segmentation. Enlightened by the multiapplication and favorable performance of fuzzy clustering methods, intuitionistic fuzzy c-means (IFCM) is applied as the backbone of our segmentation method. The contributions of the proposed method mainly include two parts. First, motivated by potential target characteristics and tendency for clearer contour description, structural symmetry information is utilized, which is an intrinsic shape feature of objects and would be significant especially when the texture and details of the target are lost in infrared images. Further, a map that represents the probability of pixels belonging to the target is constructed in the form of ellipse symmetry region. Combined with the probability map, symmetry information is utilized to establish a novel dissimilarity function in fuzzy clustering. Second, local homoplasy information which is designed based on region similarity is introduced to suppress the intensity inhomogeneity and noises to further improve the performance of the proposed method. Finally, a dataset containing 500 infrared pedestrian images paired with corresponding pixel-wise annotation is constructed to verify segmentation effectiveness. The proposed SR-IFCM is compared with 12 state-of-the-art segmentation methods. The experimental results indicate that the proposed method outperforms the comparison methods and works better for infrared pedestrian segmentation.

This paper presents a distribution information based intuitionistic fuzzy clustering method for infrared ship segmentation. The algorithm could effectively suppress the influences of nontarget objects with high intensity and intensity inhomogeneity in the infrared ship images. There are mainly two improvements in this paper. First, it proposes a fuzzy clustering algorithm incorporating global distribution information of ship targets in the form of the Gaussian model. The spatial information, along with intensity, is used to exert different effects on different classes. Second, an intuitionistic fuzzy clustering way is incorporated into the process of ship segmentation, which combines the intensity distribution information of the local region. The intuitionistic fuzzy distance and local intensity distribution information would help in solving the problem of intensity inhomogeneity and blurring edges. Experiment results on the dataset containing 200 infrared ship images indicate the superiority of the proposed method compared with other state-of-the-art methods.

Fuzzy c-means (FCM) is a popular clustering method for image segmentation. However, FCM has difficulties in handling artifacts in brain magnetic resonance imaging (MRI), especially when it comes to bias field and noise. We propose a novel multiple-surface-approximation-based FCM with interval membership method for simultaneous bias correction and segmentation of Brain MRI. First, multiple surface representation of bias field is embedded into FCM to estimate and correct bias field. Then memberships of the improved FCM are extended to intervals. After the extension, clustering centers of different MR brain tissues could be solved more properly by the proposed method. Moreover, the proposed method is less sensitive to noise by introducing effects of neighboring pixels. Experiments conducted on artificial images and synthetic and real clinical Brain MRI show that the proposed method is effective and obtains better results of both bias field correction and segmentation than comparing methods.

Fast and accurate ellipse detection is critical in certain computer vision tasks. In this paper, we propose an arc adjacency matrix-based ellipse detection (AAMED) method to fulfill this requirement. At first, after segmenting the edges into elliptic arcs, the digraph-based arc adjacency matrix (AAM) is constructed to describe their triple sequential adjacency states. Curvature and region constraints are employed to make the AAM sparse. Secondly, through bidirectionally searching the AAM, we can get all arc combinations which are probably true ellipse candidates. The cumulative-factor (CF) based cumulative matrices (CM) are worked out simultaneously. CF is irrelative to the image context and can be pre-calculated. CM is related to the arcs or arc combinations and can be calculated by the addition or subtraction of CF. Then the ellipses are efficiently fitted from these candidates through twice eigendecomposition of CM using Jacobi method. Finally, a comprehensive validation score is proposed to eliminate false ellipses effectively. The score is mainly influenced by the constraints about adaptive shape, tangent similarity, distribution compensation. Experiments show that our method outperforms the 12 state-of-the-art methods on 9 datasets as a whole, with reference to recall, precision, F-measure, and time-consumption.

Weighted Schatten p-norm minimization (WSNM) has been used successfully for noisy-free image completion. However, WSNM can introduce extra artifacts if the observed entries of image contain noise. In this paper, we present a novel WSNM-based method for noisy image completion, which incorporates both local smoothness and nonlocal self-similarity in a unified framework. More concretely, the analysis operator is utilized to ensure local smoothness and the nonlocal statistical modeling (NLSM) is adopted to constrain nonlocal self-similarity while WSNM is effective for completing the missing entries. To make the proposed method tractable and robust, the alternating direction method of multipliers (ADM-M) is employed to solve the above inverse problem. Experimental results show the effectiveness of the proposed method for noisy image completion.

The projection of space circle can be used for relative pose measurement of satellite targets. The accuracy of elliptical parameters plays an important role in the accuracy of pose recovery. However, the quality of space visible and infrared image is poor. The traditional ellipse detection method is mainly based on the edge of pixel‐accuracy‐wise and the ellipse accuracy is low, resulting in pose measurement errors. In this study, a sub‐pixel‐accuracy‐wise edge‐based ellipse fitting method is proposed to improve the ellipse accuracy. To realise this goal, we improved the arc‐based ellipse detection method and designed sub‐pixel‐edge‐based ellipse detection method. Experimental results show that the ellipse accuracy fitted by sub‐pixel edge coordinates is at least 50% higher than that by pixel edge coordinates, especially when the ellipse is incomplete. The author's method is the first one to propose and verify the sub‐pixel edge coordinate which contributes to enhancing ellipse accuracy.

This paper presents a patch-sparsity-based image inpainting algorithm through a facet deduced directional derivative. The algorithm could ensure the continuity of boundaries of the inpainted region and achieve a better performance on restoring the missing structure of an image. In this paper, two improvements are proposed. First, the facet model is introduced to get direction features of the image, which could efficiently reduce the effect of noises. The first-order directional derivatives, along with pixel values, are used to measure the difference between patches. Consequently, a more reliable and accurate matching result is promised. At the same time, the local patch consistency constraint of sparse representation of the target patch is also rewritten in the form of the first-order directional derivative. Therefore, a more precise sparse linear combination could be obtained under constraints for both color and derivative information. Second, the value of patch confidence in the traditional exemplar-based inpainting algorithms drops sharply in the late stage so that the data term or structure sparsity has little influence on priority function. Aiming at this problem, the algorithm makes a modification to the calculating of priority. Thus, the filling order decided by priority function appears more reasonable as a result of a better balance between the values of modified confidence and structure sparsity. Experiments on different types of damages to images show the superiority of the algorithm.

Magnetic resonance imaging (MRI) is extensively applied in clinical practice. Segmentation of the MRI brain image is significant to the detection of brain abnormalities. However, owing to the coexistence of intensity inhomogeneity and noise, dividing the MRI brain image into different clusters precisely has become an arduous task. In this paper, an improved possibilistic fuzzy c -means (FCM) method based on a similarity measure is proposed to improve the segmentation performance for MRI brain images. By introducing the new similarity measure, the proposed method is more effective for clustering the data with nonspherical distribution. Besides that, the new similarity measure could alleviate the "cluster-size sensitivity" problem that most FCM-based methods suffer from. Simultaneously, the proposed method could preserve image details as well as suppress image noises via the use of local label information. Experiments conducted on both synthetic and clinical images show that the proposed method is very effective, providing mitigation to the cluster-size sensitivity problem, resistance to noisy images, and applicability to data with more complex distribution.

This paper introduces sparsity in the traditional fuzzy clustering framework and presents two novel clustering methods. The first one is called deviation-sparse fuzzy c-means (DSFCM). When spatial correlation is encountered, the second method is proposed, which is called deviation-sparse fuzzy c-means with neighbor information constraint (DSFCM_N). The contributions of this paper are threefold. First, the theoretical values of data, estimated from the measured values, are utilized in the clustering process. This could acquire more accurate cluster centers than the traditional fuzzy c-means. Second, by imposing sparsity on the deviations between measured values and theoretical values, DSFCM and DSFCM_N could identify noise and outliers. Finally, with the constraint of neighbor information, the estimation of the deviations between measured values and theoretical values of data would be more reliable than only considering the data itself. Experiments performed on artificial and real-world images show that DSFCM_N is effective and efficient, and thus more competitive than other fuzzy clustering methods.

A novel thermal infrared pedestrian segmentation algorithm based on conditional generative adversarial network (IPS-cGAN) is proposed for intelligent vehicular applications. The convolution backbone architecture of the generator is based on the improved U-Net with residual blocks for well utilizing regional semantic information. Moreover, cross entropy loss for segmentation is introduced as the condition for the generator. SandwichNet, a novel convolutional network with symmetrical input, is proposed as the discriminator for real-fake segmented images. Based on the c-GAN framework, good segmentation performance could be achieved for thermal infrared pedestrians. Compared to some supervised and unsupervised segmentation algorithms, the proposed algorithm achieves higher accuracy with better robustness, especially for complex scenes.

Fuzzy c-means (FCM) clustering algorithms have been proved to be effective image segmentation techniques. However, FCM clustering algorithms are sensitive to noises and initialization. They cannot effectively segment cell images with inhomogeneous gray value distributions and complex touching cells. Aiming to overcome these disadvantages, this paper proposes a cell image segmentation algorithm using fractional-order velocity based particle swarm optimization (FOPSO) combined with shape information improved intuitionistic FCM (SI-IFCM) clustering. Iterations are carried out between FOPSO and SI-IFCM to achieve final cell segmentation. Experimental results demonstrate that the proposed algorithm has advantages on cell image segmentation, with the highest recall (90.25%) and lowest false discovery rate (0.28%) compared with the state-of-the-art algorithms.

In this paper, an intuitionistic center-free fuzzy c-means clustering method (ICFFCM) is proposed for magnetic resonance (MR) brain image segmentation. First, in order to suppress the effect of noise in MR brain images, a pixel-to-pixel similarity with spatial information is defined. Then, for the purpose of handling the vagueness in MR brain images as well as the uncertainty in clustering process, a pixel-to-cluster similarity measure is defined by employing the intuitionistic fuzzy membership function. These two similarities are used to modify the center-free FCM so that the ability of the method for MR brain image segmentation could be improved. Second, on the basis of the improved center-free FCM method, a local information term, which is also intuitionistic and center-free, is appended to the objective function. This generates the final proposed ICFFCM. The consideration of local information further enhances the robustness of ICFFCM to the noise in MR brain images. Experimental results on the simulated and real MR brain image datasets show that ICFFCM is effective and robust. Moreover, ICFFCM could outperform several fuzzy-clustering-based methods and could achieve comparable results to the standard published methods like statistical parametric mapping and FMRIB automated segmentation tool.

Pedestrians are highlighted regions in infrared images. Hence, infrared images could be used for pedestrian detection. Segmentation is an important step for detection and an accurate segmentation would be helpful for infrared pedestrian detection. However, intensity inhomogeneity is a common drawback in infrared images due to occlusion or uneven heat dissipation. This phenomenon would have negative influences on pedestrian segmentation in infrared images. To address this problem, an intensity inhomogeneity suppressed fuzzy C-means method is proposed in this paper for the segmentation of infrared pedestrians with intensity inhomogeneity. Two improvements are made in the proposed method to suppress intensity inhomogeneity: 1) a weight based on membership information and image intensity information of infrared pedestrians is added to the objective function and 2) neighborhood information is considered by adding a regularization term into the objective function to suppress the intensity inhomogeneity in infrared images. Eight famous segmentation methods are utilized as comparison methods in our experiment. The experimental results show that the proposed method could effectively suppress the intensity inhomogeneity in infrared pedestrian images and perform better for segmentation than the comparison methods.

Organ localization is an essential preprocessing step for many medical image analysis tasks such as image registration, organ segmentation and lesion detection. In this work, we propose an efficient method for multiple organ localization in CT image using 3D region proposal network. Compared with other convolutional neural network based methods that successively detect the target organs in all slices to assemble the final 3D bounding box, our method is fully implemented in 3D manner, thus can take full advantages of the spatial context information in CT image to perform efficient organ localization with only one prediction. We also propose a novel backbone network architecture that generates high-resolution feature maps to further improve the localization performance on small organs. We evaluate our method on two clinical datasets, where 11 body organs and 12 head organs (or anatomical structures) are included. As our results shown, the proposed method achieves higher detection precision and localization accuracy than the current state-of-theart methods with approximate 4 to 18 times faster processing speed. Additionally, we have established a public dataset dedicated for organ localization on http://dx. doi.org/10.21227/df8g-pq27. The full implementation of the proposed method have also been made publicly available on https://github.com/superxuang/caffe_3d_faster_rcnn.

Pedestrian detection in infrared images is always a challenging task. Segmentation is an important step of pedestrian detection. An accurate segmentation could provide more information for further analysis. In this paper, an improved Fuzzy C-Means clustering method, which incorporates geometric symmetry information, is proposed for infrared pedestrian segmentation. In the proposed method, symmetry information is introduced by Markov random field theory. Moreover, a new metric is utilized to handle the weak symmetry of pedestrian. In addition, a whole procedure is proposed to extract infrared pedestrians. The experimental results indicate that our method performs better for infrared pedestrian segmentation and obtains better segmentation results compared with other state-of-the-art methods.

Robust and effective detection of small target and false alarm (FA) suppression are the key techniques in infrared search and track systems. In this paper, the derivative entropy-based contrast measure (DECM) is proposed for small-target detection under various complex background clutters. Initially, different directional derivatives of an infrared image are calculated based on the facet model. Then, by analyzing the derivative properties of small target, the primitive entropy formula is improved by incorporating derivative information. With the improved entropy, the contrast measure is constructed to enhance small target and suppress background clutters in each derivative subband. Finally, the contrast measure maps derived from derivative subbands are fused together. The small target could be segmented easily from the fusion result. Experimental results demonstrate that DECM could effectively enhance dim small targets and suppress complex background clutters. Besides, DECM is also robust to infrared small-target images with noises of different levels. The detection results achieve higher detection ratio and lower FA compared with those of other methods under various infrared scenes.

Pedestrian segmentation in infrared images is a challenging problem due to low SNR and inhomogeneous luminance distribution. In this paper, we first introduce background prior and object-center prior into infrared pedestrian segmentation, and propose a robust and efficient saliency-based scheme, which aims to obtain the accurate pedestrian object. First, background likelihood is developed to abstract the object representation based on the Gaussian mixture model soft decomposition. Second, by combining the shape information and the infrared character of the pedestrians, kernel density estimation-based foreground estimation is proposed to obtain the saliency iteratively with better adaptable for the fuzzy contour of the infrared object. Third, pedestrian boundary weight is employed to integrate the above two saliency maps for more intact and accurate results. Finally, pedestrians can be easily segmented from the infrared images, through any existing segmentation methods, as simple as the Otsu threshold method, on the obtained final saliency map. Extensive experiments on real infrared images captured by intelligent transportation systems demonstrate that our saliency algorithm consistently outperforms the state-of-the-art saliency detection methods, in terms of higher precision, F-measure, and lower mean absolute error. The effectiveness of our proposed segmentation algorithm is also evaluated by comparisons with the existing infrared segmentation methods and yields more precise and intact pedestrian regions.

Fuzzy c-means clustering (FCM) algorithm has been proved to be effective for image segmentation. However, it is sensitive to the noises and initialization. FCM could not effectively segment cell images with inhomogeneity and complicate adhesives. Aimed to overcome these disadvantages, this paper proposes a cell image segmentation algorithm using spatial information improved intuitionistic fuzzy c-means clustering (SI-IFCM) combined with fractional-order velocity based particle swarm optimization (FOPSO). SI-IFCM and FOPSO will iterate alternately with different object functions to obtain the clustering result. Experimental results demonstrate the advantages of our algorithm for cell segmentation comparing with state-of-arts algorithms.

Saliency detection is popular in image processing, but it is still a challenging problem for infrared pedestrian images. In this paper, an effective saliency detection method for infrared pedestrian images is proposed. Taking into consideration the characteristics of pedestrians including luminance and shape, the MSER-based local stableness (MLS) is firstly introduced. Then vertical edge-weighted contrast (VEC) is calculated. Finally, an intra-scale and inter-scale neighborhood based saliency propagation method is constructed to optimize and integrate the two features. Extensive experiments demonstrate the effectiveness of the proposed saliency method for infrared pedestrian images.

Detection of small target has been an important and challenging task in infrared systems. Most detection algorithms which only use single metric are difficult to separate target from clutter completely. The false alarm may be high when there exists complex backgrounds. In this letter, multiple novel features are proposed from four aspects to establish elaborate description. Each feature reflects specific characteristic of small target. The best feature vector is selected to apply these features for detection. Then, learning-based classifier is trained to screen candidate targets which are obtained by initial segmentation. Experimental results demonstrate that the proposed features could discriminate small targets from various clutters effectively. The better detection performance is achieved compared with other methods in different infrared backgrounds.

Pedestrian segmentation in infrared images is a difficult problem for the defects of low SNR and inhomogeneous luminance distribution. In this paper, we propose a method which aims to obtain the accurate pedestrian segmentation through a background prior and boundary weight-based saliency. Background likelihood is firstly calculated as background prior to get an abstract representation for infrared pedestrian. Then, by considering the object-center prior, the object-biased Gaussian model is applied to derive the probability density estimation for pedestrians. Finally, the above two results are integrated with the boundary weight to obtain the final saliency map for infrared image, based on which pedestrians can be easily segmented. Experimental results on real infrared images captured by intelligent transportation systems demonstrate the effectiveness of the proposed approach against the state-of-the-art algorithms.

Segmentation of infrared (IR) ship images is always a challenging task, because of the intensity inhomogeneity and noise. The fuzzy C-means (FCM) clustering is a classical method widely used in image segmentation. However, it has some shortcomings, like not considering the spatial information or being sensitive to noise. In this paper, an improved FCM method based on the spatial information is proposed for IR ship target segmentation. The improvements include two parts: 1) adding the nonlocal spatial information based on the ship target and 2) using the spatial shape information of the contour of the ship target to refine the local spatial constraint by Markov random field. In addition, the results of K -means are used to initialize the improved FCM method. Experimental results show that the improved method is effective and performs better than the existing methods, including the existing FCM methods, for segmentation of the IR ship images.

A novel infrared pedestrian segmentation algorithm based on the circular shortest path is proposed. The foreground containing pedestrians is estimated by saliency mapping and gray thresholding. In the foreground area, the human shape feature is introduced by a regional polar transformation. By adding the human shape coefficient to the object term of the cost function, the extracted contour can fit the human shape well while excluding most false alarms. The proposed algorithm performs better in areas with low contrast, and obtains good segmentation quantitatively and qualitatively.

The Fuzzy C-Means clustering (FCM) and the possibility FCM (PFCM) are popular methods in MRI brain image segmentation. However, using the Euclidean squared-norm distance as the similarity criterion makes FCM and PFCM only suitable for clustering the hyperspherically distributed data groups. The MRI brain image does not distribute hyperspherically, which means FCM and PFCM have intrinsic deficiency for the segmentation of MRI brain image. The center-free FCM could segment the non-linearly separable data. But, it does not consider the spatial information and is very sensitive to noise. In order to segment the non-linearly separable data groups with noise, a center-free PFCM is proposed in this paper. Firstly, we modify the center-free FCM to deal with the non-linearly separable data. Then, we combine the improved center-free FCM with PFCM to make the new method less sensitive to noise. Experimental results on artificial datasets and MRI brain images show that our method is effective and outperforms the conventional FCM methods in the segmentation of the MRI brain images with noise.

Parameter estimation in Total Variation (TV) deblurring has been extensively studied in the literature during the last decade. However, few works have been done for parameter estimation in ℓp(0poutperforms TV and ℓ1in natural image deblurring. In this paper, by utilizing the Bayesian framework, we propose an adaptive fast iteratively reweighted least squares algorithm for ℓpregularized image deconvolution, which automatically estimates the unknown image and regularization parameter. Experiments show that the proposed method yields nearly optimal results and outperforms the state-of-the-art methods.

Segmentation of infrared (IR) ship images is always a challenging task, because of the intensity inhomogeneity and noise. The Fuzzy C-Means (FCM) clustering is a classical method widely used in IR ship image segmentation. However, it has some shortcomings, like not considering the spatial information or being sensitive to noise. In this paper, an improved FCM algorithm based on the spatial information is proposed. The improvements include two parts: (1) adding the non-local spatial information based on the ship target; (2) using the spatial shape information of the contour of the ship target to refine the local spatial constraint by Markov Random Field (MRF). A preprocessing procedure and a target selection method are also used to further improve the performance of the segmentation result. Experimental results show that our method is very effective and performs better than the conventional FCM methods in segmentation of the infrared ship images.

Blind image deconvolution (BID) is a severely ill-posed problem which requires prior information on the latent image to estimate the blur kernel. In this paper, a new observation that blurring always pushes the gradient of a local image region toward its mean value is introduced. And we formulate a novel function to measure the distance between the local gradient and its mean value. A novel regularizer associated with local gradient means is proposed. As it requires to segment the whole image into small regions, we propose an approximate method without any segmentation. Thanks to its simplicity the algorithm is fast and robust. Numerous experimental results on synthetic and real data demonstrate that our method is capable of removing various uniform blurs such as motion blur, atmospheric blur and out-of-focus blur.