The aim of click-based interactive image segmentation is to obtain pixel-level segmentation masks by only a small number of manual clicks. This approach streamlines the process of pixel-level annotation and image editing. Much research has focused on this area. In particular, SimpleClick, which utilizes Vision Transformers, has implemented a straightforward design that has demonstrated its effectiveness in interactive image segmentation. However, two issues remain: First, this simple design does not fully utilize the global guidance provided by the interaction map. Second, treating all clicks equally fails to maximize the benefits of the new clicks’ guidance in each iteration. To address these challenges, we propose a novel interactive image segmentation network called Glclick. Specifically, we introduce a Global Hint Module (GHM) that integrates global information from clicks into the transformer backbone. In addition, Glclick incorporates a Local Correction Module (LCM) that performs local optimization on the target masks generated by the backbone network. Extensive experiments on four generic datasets and three medical datasets demonstrate the superiority and generalizability of Glclick. • Designed a brand-new interactive image segmentation model called “Glclick”. • Designed a Global Hint Module called GHM and a Local Correction Module called LCM. • Advanced performance was achieved on generic datasets and three medical datasets.

Referring camouflaged object detection (Ref-COD) is an emerging and challenging task that aims to localize camouflaged objects in complex scenes based on a small set of referring images with salient objects. However, existing methods primarily focus on semantic alignment between the referring and camouflaged objects while overlooking scale discrepancies, leading to under-response when small references guide large objects and over-response when large references guide small ones. To overcome this limitation, we propose a novel Multi-scale Interaction Network (MINet), explicitly designed to handle feature interactions across different scales in Ref-COD. MINet begins with a Dual-Source Fusion Block (DSFB) for semantic fusion between the referring and camouflaged features. Then, the Intra-scale Interaction Block (IIB) enhances local saliency within each scale by modeling contextual importance. Next, the Cross-scale Interaction Block (CIB) performs offset-guided alignment to bridge spatial gaps in multiscale feature fusion. Finally, the Cross-scale Aggregation Decoder (CAD) integrates multiscale features, effectively decoding the aggregated information to produce accurate predictions. Extensive experiments on Ref-COD datasets demonstrate that our method achieves state-of-the-art performance, highlighting the importance of scale interaction in Ref-COD.

Camouflaged object detection involves identifying camouflaged objects visually blended into the surroundings, holding crucial significance in various visual applications. Existing methods primarily focus on leveraging boundary information to enhance camouflaged object detection. However, they often overlook the background interference near the object boundaries, which leads to coarse boundary predictions and results in suboptimal detection performance. In this paper, to address this problem, we propose GBNet, a gated boundary-aware network designed to enhance boundary precision and improve overall detection performance. Specifically, GBNet incorporates a boundary-enhanced module that selectively filters extraneous background information through a boundary gate block, ensuring the generation of high-quality boundary information. Additionally, a boundary-aware decoder is designed to enrich the representation ability of the decoder by injecting high-quality boundary features and aggregating contextual features. With meticulous design, GBNet excels in accurately segmenting camouflaged objects in challenging scenarios. Extensive experiments demonstrate that GBNet outperforms 19 state-of-the-art methods significantly across four widely-used benchmark datasets. The source code is publicly available at https://github.com/wooownn/GBNet.

Generalized Few-Shot Semantic Segmentation (GFSS) enables pixel-level segmentation of various classes in images when limited annotated data is available. This effectively reduces the reliance on extensive fine-grained annotated data for training segmentation models. The training process for GFSS is typically divided into two stages: base class training and novel class updating. However, due to the imbalance in the distribution between novel and base classes, this two-stage training approach may result in the misidentification of similar classes and segmentation loss caused by background interference. To address these challenges, this paper proposes a prototype guided multi-scale class aggregation method. The multi-scale design preserves more image details, while the prototype-guided strategy leverages the rich features learned from both base and novel class prototypes to guide class aggregation. This approach enhances the understanding of inter-class differences through aggregation and supports the effective learning of novel classes. Extensive experiments confirm the efficacy of our approach, with our model achieving state-of-the-art performance on COCO-20iand outperforming existing methods on most metrics in PASCAL-5i.

Accurate recognition of marine species in drone-captured images is essential in maintaining the stability of coastal zone ecosystems. Unmanned aerial vehicle (UAV) remote sensing images usually lack paired supervised signals and suffer from color distortion and blurring due to the interaction of ambient light with cross-medium transmission between air and water. However, current algorithms mainly focus on supervised training methods and also ignore the interaction involved in the cross-medium transmission of light in water. In this article, for UAV in coastal zones, we propose an unsupervised image water removal model, named DewaterGAN, which is based solely on low-tide and high-tide images without paired supervised signals and also preserves color and texture in the water removal process. Specifically, our approach involves two key steps: an unsupervised training CycleGAN network accomplishes domain transitions from low-tide level to high-tide level, and a physics-based attention module guides image water removal and maintains authenticity. Additionally, we utilize evaluation metrics of image restoration peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) to quantitatively analyze the performance of the model. We also employed several non-reference metrics (UIQM, UCIQE, NIQE, BRISQUE, LIQE, ILNIQE, and CLIPIQA) to evaluate the visual quality of the image de-watering process. Extensive experiments conducted on both our water removal dataset and public datasets validate the efficacy of our model. The code is athttps://github.com/yfq-yy/Dewater.git.

The critical challenge of video object counting is to avoid counting the same object multiple times in different frames. By comparing the appearance and motion feature information of the detection results, the authors use the multi-object tracking method to assign an independent ID number to each object. From the time the ID tag is obtained until the end of the video, each object is counted only once. However, even minor amounts of image noise can cause irreversible changes in feature information, resulting in severe tracking drifts. This paper introduces the concept of scene awareness and addresses unreasonable ID assignment caused by unreliable feature matching in the context of region division. Through the macro analysis of the scene, the authors define the region (called the transition region) where the number of objects can increase or decrease and require that all ID assignments for new objects and ID deletions for existing objects take place only in the transition region. Because the actual number of objects in the non-transition region is constant, they rematch unmatched objects with existing IDs in the region (called ID relocation) because changes in object ID are caused by feature matching failure. In this paper, the authors create algorithms for dynamically generating transition regions, detecting object increases and decreases, and relocating object IDs. Experimental results show that the method effectively improves the accuracy of video object counting.

Deep learning based object detection methods have achieved promising performance in controlled environments. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) images in the underwater datasets and real applications are blurry whilst accompanying severe noise that confuses the detectors and (2) objects in real applications are usually small. In this paper, we propose a Sample-WeIghted hyPEr Network (SWIPENET), and a novel training paradigm named Curriculum Multi-Class Adaboost (CMA), to address these two problems at the same time. Firstly, the backbone of SWIPENET produces multiple high resolution and semantic-rich Hyper Feature Maps, which significantly improve small object detection. Secondly, inspired by the human education process that drives the learning from easy to hard concepts, we propose the noise-robust CMA training paradigm that learns the clean data first and then move on to learns the diverse noisy data. Experiments on four underwater object detection datasets show that the proposed SWIPENET+CMA framework achieves better or competitive accuracy in object detection against several state-of-the-art approaches.

In recent years, deep learning based methods have achieved promising performance in standard object detection. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) Objects in real applications are usually small and their images are blurry, and (2) images in the underwater datasets and real applications accompany heterogeneous noise. To address these two problems, we first propose a novel neural network architecture, namely Sample-WeIghted hyPEr Network (SWIPENet), for small object detection. SWIPENet consists of high resolution and semantic-rich Hyper Feature Maps which can significantly improve small object detection accuracy. In addition, we propose a novel sample-weighted loss function which can model sample weights for SWIPENet, which uses a novel sample re-weighting algorithm, namely Invert Multi-Class Adaboost (IMA), to reduce the influence of noise on the proposed SWIPENet. Experiments on two underwater robot picking contest datasets URPC2017 and URPC2018 show that the proposed SWIPENet+IMA framework achieves better performance in detection accuracy against several state-of-the-art object detection approaches.

High-quality sea ice monitoring is crucial to navigation safety and climate research in the polar regions. In this letter, a transferred multilevel fusion network (MLFN) is proposed for sea ice change detection from synthetic-aperture radar (SAR) images. Considering the fact that training data are limited in the task of sea ice change detection, a large data set was used to train the MLFN, and the deep knowledge can be transferred to sea ice analysis. In addition, cascade dense blocks are employed to optimize the convolutional layers. Multilayer feature fusion is introduced to exploit the complementary information among low-, mid-, and high-level feature representations. Therefore, more discriminative feature extraction can be achieved by the MLFN. Furthermore, the fine-tune strategy is utilized to optimize the network parameters. The experimental results on two real sea ice data sets demonstrated that the proposed method achieved better performance than other competitive methods.

Sea ice change detection from synthetic aperture radar (SAR) images can be regarded as a classification procedure, in which pixels are classified into changed and unchanged classes. However, existing methods usually suffer from the intrinsic speckle noise of multitemporal SAR images. To solve the problem, this letter presents a change detection method based on convolutional-wavelet neural networks (CWNNs). In CWNN, dual-tree complex wavelet transform is introduced into convolutional neural networks for changed and unchanged pixels' classification, and then, the effect of speckle noise is effectively reduced. In addition, a virtual sample generation scheme is employed to create samples for CWNN training, and the problem of limited samples is alleviated. Experimental results on two real SAR image data sets demonstrate the effectiveness and robustness of the proposed method.

Sea ice change detection from synthetic aperture radar (SAR) images is important for navigation safety and natural resource extraction. This paper proposed a sea ice change detection method from SAR images based on collaborative representation. First, neighborhood-based ratio is used to generate a difference image (DI). Then, some reliable samples are selected from the DI by hierarchical fuzzy C-means (FCM) clustering. Finally, based upon these samples, collaborative representation method is utilized to classify pixels from the original SAR images into unchanged and changed class. From there, the final change map can be obtained. Experimental results on two real sea ice datasets demonstrate the superiority of the proposed method over two closely related methods.

Hyperspectral imagery has evident advantages for sea ice classification due to enormous spectral bands. In this paper, we proposed a novel sea ice classification framework from hyperspectral image based on self-paced boost learning (SPBL). First, the criterion of linear prediction error is used for unsupervised band selection. Then, local binary pattern (LBP) features are extracted from the selected bands. Finally, SPBL is employed as the classifier to provide probability outputs using the extracted features. The proposed framework can capture the intrinsic inter-class discriminative models while ensuring the reliability of the samples involved in learning. The experimental results in real-world dataset demonstrate that the proposed framework is superior to several closely related methods.

Person re-identification that aims at matching individuals across multiple camera views has become indispensable in intelligent video surveillance systems. It remains challenging due to the large variations of pose, illumination, occlusion and camera viewpoint. Feature representation and metric learning are the two fundamental components in person reidentification. In this paper, we present a Special Dense Convolutional Neural Network (SD-CNN) to extract the feature and apply Joint Bayesian to measure the similarity of pedestrian image pairs. The SD-CNN can preserve more horizontal information to against viewpoint changes, maximize the feature reuse and ensure feature distributing discriminative. Joint Bayesian models the extracted feature representation as the sum of inter- and intra-personal variations, and the joint probability of two images being a same person can be obtained through log-likelihood ratio. Experiments show that our approach significantly outperforms state-of-the-art methods on several benchmarks of person re-identification.

Internal waves are a widespread geophysical phenomenon in stratified fluids and studying internal features in the coastal ocean is an important task. Using satellite imagery for studying oceanic internal waves is very popular and studying the low altitude aerial oblique photograph is a new direction. In this paper, we study the images captured from a circling aircraft which track a number of internal wave packets. The captured images are first rectified and photogram metrically mapped to ground coordinates, and then we use canny edge detector to find the internal wave propagation direction. The first several waves' ridges of each ground coordinated images are also exactly labeled and propagation speeds can be achieved. Experiment results show the performance of our algorithms.

Research on pedestrian detection still presents a lot of space for improvements, both on speed and detection accuracy. State-of-the-art object proposals approach has shown the very effective computational efficiency in object detection. In this paper, we present a framework for pedestrian detection based on the object proposals. Instead of scaling the test image to different sizes, we generate a pyramid of HOG templates by simply scaling the primary HOG descriptor. For the test image, Histogram of Oriented Gradient feature vector is calculated only in the region of object proposals without using sliding window, and the corresponding HOG template is selected according to the size of proposal region. Experiment results show that our proposed methods can achieve the equal detection rate while using only 20% time.

Person re-identification has become a hot research topic due to its importance in surveillance and forensics applications. The purpose of person re-identification is to find the same person from disjoint camera views at different time. Most of the existing methods try to identify the person by measuring the similarity of two still images from different camera views, which only uses intraimage features such as color, shape and texture. In this paper, we propose a person re-identification architecture which analyzes a sequence pair while not an image pair, so not only intra-image features but also the gait feature is also considered. In contrast to existing works that use handcrafted features, our method automatically learns spatio-temporal features optimal for the person re-identification task with a deep convolutional network. To learn a discriminant metric, we use a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA).The process of XQDA is also called transfer learning. Experiments show that our method significantly outperforms the state of the art on both a large dataset (CUHK03) and a medium-sized dataset (CUHK01). We also get a better performance on a small dataset (VIPeR) with a pre-trained network without fine-tuning.

The precise location and tracking of the moving vehicles is an important part in intelligent transportation system. This paper presents a new method for detecting vehicles which can remove the shadows fast. The main process is done in three steps: moving region detection, shadow detection, and edge detection. Firstly, a moving region of the image is achieved using the self-adaptive background updating method quickly. Then a coarse shadow area can be taken with the model-based method, and the shadow detection method based on the HSV color space is only applied on the coarse area. Finally, impose edge detection on both the moving regions and shadow areas. Subtraction of the two areas leads to the result of the detection of real vehicle. Experiment results show that this method can improve the efficiency of the moving vehicles detection greatly.