Adaptive complementary neighboring sub-aperture beamforming for thermoacoustic imaging.

BACKGROUND: When applied to thermoacoustic imaging (TAI), the delay-and-sum (DAS) algorithm produces strong sidelobes due to its disadvantages of uniform aperture weighting. As a result, the quality of TAI images recovered by DAS is often severely degraded by strong non-coherent clutter, which restricts the development and application of TAI. PURPOSE: To address this issue, we propose an adaptive complementary neighboring sub-aperture (NSA) beamforming algorithm for TAI. METHODS: In NSA, we introduce a coordinate system transformation when calculating the normalized cross-correlation (NCC) matrix. This approach enables the computation of the NCC coefficient within the specified kernel without complex coordinate calculations. We first conducted the numerical simulation experiment to validate NSA using a tree branch phantom. In addition, we also conducted phantom (five sauce tubes), ex vivo (ablation needle in ex vivo porcine liver), and in vivo (human arm) TAI experiments using our TAI system with a center frequency of 3 GHz. RESULTS: In the numerical simulation experiment, the structural similarity index (SSIM) value for NSA is increased from 0.37828 for DAS to 0.75492. In the point target phantom TAI experiment, the generalized contrast-to-noise ratio (gCNR) value for NSA is increased from 0.936 for DAS to 0.962. The experimental results show that NSA can recover clearer thermoacoustic images compared to DAS. In the ex vivo TAI experiment, the full width at half maxima (FWHM) of an ablation needle (diameter = 1.5 mm) for coherence factor (CF) weighted DAS and NSA are 0.9 and 1.3 mm, respectively. Furthermore, in the in vivo TAI experiment, CF reduces the signals within the arm compared to NSA. Therefore, compared with CF, NSA can maintain the integrity of target information in TAI while effectively suppressing non-coherent background clutter. CONCLUSIONS: NSA can effectively reduce non-coherent background noise while ensuring the completeness of the target information. So, NSA offers the potential to provide high-quality thermoacoustic images and further advance their clinical application.

Corrigendum: Where's Wally: the influence of visual salience on referring expression generation.

[This corrects the article DOI: 10.3389/fpsyg.2013.00329.].

Low-rank prior-based Fast-RPCA for clutter filtering and noise suppression in non-contrast ultrasound microvascular imaging.

Accurate and real-time separation of blood signal from clutter and noise signals is a critical step in clinical non-contrast ultrasound microvascular imaging. Despite the widespread adoption of singular value decomposition (SVD) and robust principal component analysis (RPCA) for clutter filtering and noise suppression, the SVD's sensitivity to threshold selection, along with the RPCA's limitations in undersampling conditions and heavy computational burden often result in suboptimal performance in complex clinical applications. To address those challenges, this study presents a novel low-rank prior-based fast RPCA (LP-fRPCA) approach to enhance the adaptability and robustness of clutter filtering and noise suppression with reduced computational cost. A low-rank prior constraint is integrated into the non-convex RPCA model to achieve a robust and efficient approximation of clutter subspace, while an accelerated alternating projection iterative algorithm is developed to improve convergence speed and computational efficiency. The performance of the LP-fRPCA method was evaluated against SVD with a tissue/blood threshold (SVD1), SVD with both tissue/blood and blood/noise thresholds (SVD2), and the classical RPCA based on the alternating direction method of multipliers algorithm through phantom and in vivo non-contrast experiments on rabbit kidneys. In the slow flow phantom experiment of 0.2 mm/s, LP-fRPCA achieved an average increase in contrast ratio (CR) of 10.68 dB, 9.37 dB, and 8.66 dB compared to SVD1, SVD2, and RPCA, respectively. In the in vivo rabbit kidney experiment, the power Doppler results demonstrate that the LP-fRPCA method achieved a superior balance in the trade-off between insufficient clutter filtering and excessive suppression of blood flow. Additionally, LP-fRPCA significantly reduced the runtime of RPCA by up to 94-fold. Consequently, the LP-fRPCA method promises to be a potential tool for clinical non-contrast ultrasound microvascular imaging.

Randomized waitlist-controlled trial of buried in treasures facilitated support groups and in-home uncluttering (BIT+) for hoarding disorder.

OBJECTIVE: The Buried in Treasures (BIT) workshop is a promising treatment for hoarding disorder (HD), though many participants struggle with home uncluttering. This randomized waitlist-controlled trial investigated the efficacy of a version of BIT, augmented with in-home uncluttering practice (BIT+). METHOD: Adults (N = 41) with hoarding disorder were recruited from the community and randomly assigned to BIT+ or waitlist. BIT+ consisted of 16 sessions of the BIT workshop and 10 uncluttering home visits over 18 weeks. Outcome measures included the Saving Inventory-Revised (self-report) and the Clutter Image Rating Scale (self and independent evaluator rated). Between group repeated measures analyses using general linear modeling examined the effect of BIT+ vs waitlist control on hoarding symptoms after 18 weeks. Within group analyses examined pre-post effects for all BIT+ participants combined after 18 weeks. RESULTS: After 18 weeks, BIT+ participants benefited significantly more than waitlist controls on hoarding severity with large effect size (Cohen's d = 1.5, p < .001). BIT+ was also associated with improvement reductions in hoarding symptoms, clutter, and functional impairment. CONCLUSIONS: The BIT+ intervention offers promise as a treatment option for hoarding. Adding in-home uncluttering practice may incrementally improve discarding practices. Future controlled trials are warranted.

Not seeing the forest for the trees: combination of path integration and landmark cues in human virtual navigation.

Introduction: In order to successfully move from place to place, our brain often combines sensory inputs from various sources by dynamically weighting spatial cues according to their reliability and relevance for a given task. Two of the most important cues in navigation are the spatial arrangement of landmarks in the environment, and the continuous path integration of travelled distances and changes in direction. Several studies have shown that Bayesian integration of cues provides a good explanation for navigation in environments dominated by small numbers of easily identifiable landmarks. However, it remains largely unclear how cues are combined in more complex environments. Methods: To investigate how humans process and combine landmarks and path integration in complex environments, we conducted a series of triangle completion experiments in virtual reality, in which we varied the number of landmarks from an open steppe to a dense forest, thus going beyond the spatially simple environments that have been studied in the past. We analysed spatial behaviour at both the population and individual level with linear regression models and developed a computational model, based on maximum likelihood estimation (MLE), to infer the underlying combination of cues. Results: Overall homing performance was optimal in an environment containing three landmarks arranged around the goal location. With more than three landmarks, individual differences between participants in the use of cues are striking. For some, the addition of landmarks does not worsen their performance, whereas for others it seems to impair their use of landmark information. Discussion: It appears that navigation success in complex environments depends on the ability to identify the correct clearing around the goal location, suggesting that some participants may not be able to see the forest for the trees.

Statistical Parameters Extracted from Radar Sea Clutter Simulated under Different Operational Conditions.

A complete framework of predicting the attributes of sea clutter under different operational conditions, specified by wind speed, wind direction, grazing angle, and polarization, is proposed for the first time. This framework is composed of empirical spectra to characterize sea-surface profiles under different wind speeds, the Monte Carlo method to generate realizations of sea-surface profiles, the physical-optics method to compute the normalized radar cross-sections (NRCSs) from individual sea-surface realizations, and regression of NRCS data (sea clutter) with an empirical probability density function (PDF) characterized by a few statistical parameters. JONSWAP and Hwang ocean-wave spectra are adopted to generate realizations of sea-surface profiles at low and high wind speeds, respectively. The probability density functions of NRCSs are regressed with K and Weibull distributions, each characterized by two parameters. The probability density functions in the outlier regions of weak and strong signals are regressed with a power-law distribution, each characterized by an index. The statistical parameters and power-law indices of the K and Weibull distributions are derived for the first time under different operational conditions. The study reveals succinct information of sea clutter that can be used to improve the radar performance in a wide variety of complicated ocean environments. The proposed framework can be used as a reference or guidelines for designing future measurement tasks to enhance the existing empirical models on ocean-wave spectra, normalized radar cross-sections, and so on.

A Novel Fast Iterative STAP Method with a Coprime Sampling Structure.

In space-time adaptive processing (STAP), the coprime sampling structure can obtain better clutter suppression capabilities at a lower hardware cost than the uniform linear sampling structure. However, in practical applications, the performance of the algorithm is often limited by the number of training samples. To solve this problem, this paper proposes a fast iterative coprime STAP algorithm based on truncated kernel norm minimization (TKNM). This method establishes a virtual clutter covariance matrix (CCM), introduces truncated kernel norm regularization technology to ensure the low rank of the CCM, and transforms the non-convex problem into a convex optimization problem. Finally, a fast iterative solution method based on the alternating direction method is presented. The effectiveness and accuracy of the proposed algorithm are verified through simulation experiments.

Autonomous Tumor Signature Extraction Applied to Spatially Registered Bi-Parametric MRI to Predict Prostate Tumor Aggressiveness: A Pilot Study.

BACKGROUND: Accurate, reliable, non-invasive assessment of patients diagnosed with prostate cancer is essential for proper disease management. Quantitative assessment of multi-parametric MRI, such as through artificial intelligence or spectral/statistical approaches, can provide a non-invasive objective determination of the prostate tumor aggressiveness without side effects or potential poor sampling from needle biopsy or overdiagnosis from prostate serum antigen measurements. To simplify and expedite prostate tumor evaluation, this study examined the efficacy of autonomously extracting tumor spectral signatures for spectral/statistical algorithms for spatially registered bi-parametric MRI. METHODS: Spatially registered hypercubes were digitally constructed by resizing, translating, and cropping from the image sequences (Apparent Diffusion Coefficient (ADC), High B-value, T2) from 42 consecutive patients in the bi-parametric MRI PI-CAI dataset. Prostate cancer blobs exceeded a threshold applied to the registered set from normalizing the registered set into an image that maximizes High B-value, but minimizes the ADC and T2 images, appearing "green" in the color composite. Clinically significant blobs were selected based on size, average normalized green value, sliding window statistics within a blob, and position within the hypercube. The center of mass and maximized sliding window statistics within the blobs identified voxels associated with tumor signatures. We used correlation coefficients (R) and p-values, to evaluate the linear regression fits of the z-score and SCR (with processed covariance matrix) to tumor aggressiveness, as well as Area Under the Curves (AUC) for Receiver Operator Curves (ROC) from logistic probability fits to clinically significant prostate cancer. RESULTS: The highest R (R > 0.45), AUC (>0.90), and lowest p-values (<0.01) were achieved using z-score and modified registration applied to the covariance matrix and tumor signatures selected from the "greenest" parts from the selected blob. CONCLUSIONS: The first autonomous tumor signature applied to spatially registered bi-parametric MRI shows promise for determining prostate tumor aggressiveness.

Distinct Yet Proximal Face- and Body-Selective Brain Regions Enable Clutter-Tolerant Representations of the Face, Body, and Whole Person.

Faces and bodies are processed in separate but adjacent regions in the primate visual cortex. Yet, the functional significance of dividing the whole person into areas dedicated to its face and body components and their neighboring locations remains unknown. Here we hypothesized that this separation and proximity together with a normalization mechanism generate clutter-tolerant representations of the face, body, and whole person when presented in complex multi-category scenes. To test this hypothesis, we conducted a fMRI study, presenting images of a person within a multi-category scene to human male and female participants and assessed the contribution of each component to the response to the scene. Our results revealed a clutter-tolerant representation of the whole person in areas selective for both faces and bodies, typically located at the border between the two category-selective regions. Regions exclusively selective for faces or bodies demonstrated clutter-tolerant representations of their preferred category, corroborating earlier findings. Thus, the adjacent locations of face- and body-selective areas enable a hardwired machinery for decluttering of the whole person, without the need for a dedicated population of person-selective neurons. This distinct yet proximal functional organization of category-selective brain regions enhances the representation of the socially significant whole person, along with its face and body components, within multi-category scenes.

Adaptive higher-order singular value decomposition clutter filter for ultrafast Doppler imaging of coronary flow under non-negligible tissue motion.

BACKGROUND AND OBJECTIVE: With the development of advanced clutter-filtering techniques by singular value decomposition (SVD) and leveraging favorable acquisition settings such as open-chest imaging by a linear high-frequency probe and plane waves, several studies have shown the feasibility of cardiac flow measurements during the entire cardiac cycle, ranging from coronary flow to myocardial perfusion. When applying these techniques in a routine clinical setting, using transthoracic ultrasound imaging, new challenges emerge. Firstly, a smaller aperture is needed that can fit between ribs. Consequently, diverging waves are employed instead of plane waves to achieve an adequate field of view. Secondly, to ensure imaging at a larger depth, the maximum pulse repetition frequency has to be reduced. Lastly, in comparison to the open-chest scenario, tissue motion induced by the heartbeat is significantly stronger. The latter complicates substantially the distinction between clutter and blood signals. METHODS: This study investigates a strategy to overcome these challenges by diverging wave imaging with an optimal number of tilt angles, in combination with dedicated clutter-filtering techniques. In particular, a novel, adaptive, higher-order SVD (HOSVD) clutter filter, which utilizes spatial, temporal, and angular information of the received ultrasound signals, is proposed to enhance clutter and blood separation. RESULTS: When non-negligible tissue motion is present, using fewer tilt angles not only reduces the decorrelation between the received waveforms but also allows for collecting more temporal samples at a given ensemble duration, contributing to improved Doppler performance. The addition of a third angular dimension enables the application of HOSVD, providing greater flexibility in selecting blood separation thresholds from a 3-D tensor. This differs from the conventional threshold selection method in a 2-D spatiotemporal space using SVD. Exhaustive threshold search has shown a significant improvement in Contrast and Contrast-to-Noise ratio for Power Doppler images filtered with HOSVD compared to the SVD-based clutter filter. CONCLUSION: With the improved settings, the obtained Power Doppler images show the feasibility of measuring coronary flow under the influence of non-negligible tissue motion in both in vitro and ex vivo.

Attentional Tunneling in Pilots During a Visual Tracking Task With a Head Mounted Display.

OBJECTIVE: We examined whether active head aiming with a Helmet Mounted Display (HMD) can draw the pilot's attention away from a primary flight task. Furthermore, we examined whether visual clutter increases this effect. BACKGROUND: Head up display symbology can result in attentional tunneling, and clutter makes it difficult to identify objects. METHOD: Eighteen military pilots had to simultaneously perform an attitude control task while flying in clouds and a head aiming task in a fixed-base flight simulator. The former consisted of manual compensation for roll disturbances of the aircraft, while the latter consisted of keeping a moving visual target inside a small or large head-referenced circle. A "no head aiming" condition served as a baseline. Furthermore, all conditions were performed with or without visual clutter. RESULTS: Head aiming led to deterioration of the attitude control task performance and an increase of the amount of roll-reversal errors (RREs). This was even the case when head aiming required minimal effort. Head aiming accuracy was significantly lower when the roll disturbances in the attitude control task were large compared to when they were small. Visual clutter had no effect on both tasks. CONCLUSION: We suggest that active head aiming of HMD symbology can cause attentional tunneling, as expressed by an increased number of RREs and less accuracy on a simultaneously performed attitude control task. APPLICATION: This study improves our understanding in the perceptual and cognitive effects of (military) HMDs, and has implications for operational use and possibly (re)design of HMDs.

Effects of insect pursuit on the Doppler shift compensation in a hipposiderid bat.

Doppler shift compensation (DSC) is a unique feature observed in certain species of echolocating bats and is hypothesized to be an adaptation to detecting fluttering insects. However, current research on DSC has primarily focused on bats that are not engaged in foraging activities. In this study, we investigated the DSC performance of Pratt's roundleaf bat, Hipposideros pratti, which was trained to pursue insects in various motion states within a laboratory setting. Our study yielded three main results. First, H. pratti demonstrated highly precise DSC during insect pursuit, aligning with previous findings of other flutter-detecting foragers during orientation or landing tasks. Second, we found that the motion state of the insect prey had little effect on the DSC performance of H. pratti. Third, we observed variations in the DSC performance of H. pratti throughout the course of insect pursuit. The bats exhibited the highest DSC performance during the phase of maximum flight speed but decreased performance during the phase of insect capture. These findings of high precision overall and the time-dependent performance of DSC during insect pursuit support the hypothesis that DSC is an adaptation to detecting fluttering insects.

Clutter filtering of angular domain data for contrast-free ultrafast microvascular imaging.

Objective. Contrast-free microvascular imaging is clinically valuable for the assessment of physiological status and the early diagnosis of diseases. Effective clutter filtering is essential for microvascular visualization without contrast enhancement. Singular value decomposition (SVD)-based spatiotemporal filter has been widely used to suppress clutter. However, clinical real-time imaging relies on short ensembles (dozens of frames), which limits the implementation of SVD filtering due to the large error of eigen-correlated estimations and high dependence on optimal threshold when used in such ensembles.Approach. To address the above challenges of imaging in short ensembles, two optimized filters of angular domain data are proposed in this paper: grouped angle SVD (GA-SVD) and angular-coherence-based higher-order SVD (AC-HOSVD). GA-SVD applies SVD to the concatenation of all angular data to improve clutter rejection performance in short ensembles, while AC-HOSVD applies HOSVD to the angular data tensor and utilizes angular coherence in addition to spatial and temporal features for filtering. Feasible threshold selection strategies in each feature space are provided. The clutter rejection performance of the proposed filters and SVD was evaluated with Doppler phantom andin vivostudies at different cases. Moreover, the robustness of the filters was explored under wrong singular value threshold estimation, and their computational complexity was studied.Main results. Qualitative and quantitative results indicated that GA-SVD and AC-HOSVD can effectively improve clutter rejection performance in short ensembles, especially AC-HOSVD. Notably, the proposed methods using 20 frames had similar image quality to SVD using 100 frames.In vivostudies showed that compared to SVD, GA-SVD increased the signal-to-noise-ratio (SNR) by 6.03 dB on average, and AC-HOSVD increased the SNR by 8.93 dB on average. Furthermore, AC-HOSVD remained better power Doppler image quality under non-optimal thresholds, followed by GA-SVD.Significance. The proposed filters can greatly enhance contrast-free microvascular visualization in short ensembles and have potential for different clinical translations due to the performance differences.

Meshless Search SR-STAP for Airborne Radar Based on Meta-Heuristic Algorithms.

The sparse recovery (SR) space-time adaptive processing (STAP) method has excellent clutter suppression performance under the condition of limited observation samples. However, when the cluttering is nonlinear in a spatial-Doppler profile, it will cause an off-grid effect and reduce the sparse recovery performance. A meshless search using a meta-heuristic algorithm (MH) can completely eliminate the off-grid effect in theory. Therefore, genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and grey wolf optimization (GWO) methods are applied to SR-STAP for selecting exact clutter atoms in this paper. The simulation results show that MH-STAP can estimate the clutter subspace more accurately than the traditional algorithm; PSO-STAP and GWO-STAP showed better clutter suppression performance in four MH-STAP methods. To search for more accurate clutter atoms, PSO and GWO are combined to improve the method's capacity for global optimization. Meanwhile, the fitness function is improved by using prior knowledge of the clutter distribution. The simulation results show that the improved PSO-GWO-STAP algorithm provides excellent clutter suppression performance, which solves the off-grid problem better than does single MH-STAP.

Real Aperture Radar Super-Resolution Imaging for Sea Surface Monitoring Based on a Hybrid Model.

In recent years, super-resolution imaging techniques have been intensely introduced to enhance the azimuth resolution of real aperture scanning radar (RASR). However, there is a paucity of research on the subject of sea surface imaging with small incident angles for complex scenarios. This research endeavors to explore super-resolution imaging for sea surface monitoring, with a specific emphasis on grounded or shipborne platforms. To tackle the inescapable interference of sea clutter, it was segregated from the imaging objects and was modeled alongside I/Q channel noise within the maximum likelihood framework, thus mitigating clutter's impact. Simultaneously, for characterizing the non-stationary regions of the monitoring scene, we harnessed the Markov random field (MRF) model for its two-dimensional (2D) spatial representational capacity, augmented by a quadratic term to bolster outlier resilience. Subsequently, the maximum a posteriori (MAP) criterion was employed to unite the ML function with the statistical model regarding imaging scene. This hybrid model forms the core of our super-resolution methodology. Finally, a fast iterative threshold shrinkage method was applied to solve this objective function, yielding stable estimates of the monitored scene. Through the validation of simulation and real data experiments, the superiority of the proposed approach in recovering the monitoring scenes and clutter suppression has been verified.

Mitigating skin tone bias in linear array in vivo photoacoustic imaging with short-lag spatial coherence beamforming.

Photoacoustic (PA) imaging has the potential to deliver non-invasive diagnostic information. However, skin tone differences bias PA target visualization, as the elevated optical absorption of melanated skin decreases optical fluence within the imaging plane and increases the presence of acoustic clutter. This paper demonstrates that short-lag spatial coherence (SLSC) beamforming mitigates this bias. PA data from the forearm of 18 volunteers were acquired with 750-, 810-, and 870-nm wavelengths. Skin tones ranging from light to dark were objectively quantified using the individual typology angle (ITA°). The signal-to-noise ratio (SNR) of the radial artery (RA) and surrounding clutter were measured. Clutter was minimal (e.g., -16 dB relative to the RA) with lighter skin tones and increased to -8 dB with darker tones, which compromised RA visualization in conventional PA images. SLSC beamforming achieved a median SNR improvement of 3.8 dB, resulting in better RA visualization for all skin tones.

Sideways maneuvers enable narrow aperture negotiation by free-flying hummingbirds.

Many birds routinely fly fast through dense vegetation characterized by variably sized structures and voids. Successfully negotiating these cluttered environments requires maneuvering through narrow constrictions between obstacles. We show that Anna's hummingbirds (Calypte anna) can negotiate apertures less than one wingspan in diameter using a novel sideways maneuver that incorporates continuous, bilaterally asymmetric wing motions. Crucially, this maneuver allows hummingbirds to continue flapping as they negotiate the constriction. Even smaller openings are negotiated via a faster ballistic trajectory characterized by tucked and thus non-flapping wings, which reduces force production and increases descent rate relative to the asymmetric technique. Hummingbirds progressively shift to the swept method as they perform hundreds of consecutive transits, suggesting increased locomotor performance with task familiarity. Initial use of the slower asymmetric transit technique may allow birds to better assess upcoming obstacles and voids, thereby reducing the likelihood of subsequent collisions. Repeated disruptions of normal wing kinematics as birds negotiate tight apertures may determine the limits of flight performance in structurally complex environments. These strategies for aperture transit and associated flight trajectories can inform designs and algorithms for small aerial vehicles flying within cluttered environments.

Ultra-fast ultrasound blood flow velocimetry for carotid artery with deep learning.

Accurate measurement of blood flow velocity is important for the prevention and early diagnosis of atherosclerosis. However, due to the uncertainty of parameter settings, the autocorrelation velocimetry methods based on clutter filtering are prone to incorrectly filter out the near-wall blood flow signal, resulting in poor velocimetric accuracy. In addition, the Doppler coherent compounding acts as a low-pass filter, which also leads to low values of blood flow velocity estimated by the above methods. Motivated by this status quo, here we propose a deep learning estimator that combines clutter filtering and blood flow velocimetry based on the adaptive property of one-dimensional convolutional neural network (1DCNN). The estimator is operated by first extracting the blood flow signal from the original Doppler echo signal through an affine transformation of the 1D convolution, and then converting the extracted signal into the desired blood flow velocity using a linear transformation function. The effectiveness of the proposed method is verified by simulation as well as in vivo carotid artery data. Compared with typical velocimetry methods such as high-pass filtering (HPF) and singular value decomposition (SVD), the results show that the normalized root means square error (NRMSE) obtained by 1DCNN is reduced by 54.99 % and 53.50 % for forward blood flow velocimetry, and 70.99 % and 69.50 % for reverse blood flow velocimetry, respectively. Consistently, the in vivo measurements demonstrate that the goodness-of-fit of the proposed estimator is improved by 8.72 % and 4.74 % for five subjects. Moreover, the estimation time consumed by 1DCNN is greatly reduced, which costs only 2.91 % of the time of HPF and 12.83 % of the time of SVD. In conclusion, the proposed estimator is a better alternative to the current blood flow velocimetry, and is capable of providing more accurate diagnosis information for vascular diseases in clinical applications.

Bilingual Cigarette Warning Labels: Effects on Young People's Recall and Attention in Lebanon.

The goal was to determine the effects of bilingual cigarette warning labels on the recall performance and attention of young bilingual Lebanese college students. Forty-eight students were shown English-only, Arabic-only, or bilingual cigarette warning labels in 2020. Participants recalled as many of the labels as they could after the experiment and then two weeks later. Eye tracking was used to determine attention to the label and subjective data were collected. Results showed that bilingual labels did not lead to better recall; participants needed more time to extract data from bilingual labels and first looked at them later in time, although bilingual labels were revisited more. However, participants believed that bilingual labels were better. It appeared that bilingual labels led to clutter rather than helped recall.

Oil spill detection on X-band marine radar images based on sea clutter fitting model.

Oil spills could cause great harm to the natural environment. The ability to identify them accurately is critical for prompt response and treatment. We proposed a sea clutter fitting model of marine radar images for oil spill detection. The model is derived from the geometric structure of the marine radar, the expression of marine radar received power, and the rough surface scattering model of the sea surface. In the denoised marine radar image, the sea clutter fitting model is used to detect coarse oil spills. Then the fine measurement is carried out by mean filter, the Otsu method, and noise reduction. The proposed oil spill detection method was used on radar images sampled after an oil spill accident happened in a coastal region in Dalian, China, on July 21, 2010. The proposed method can detect oil spills without human intervention, and the extracted oil spills are accurate and consistent with visual interpretation.