Salient Target Research Articles

Salient region growing based on Gaussian pyramid

For region growing image segmentation, seed selection and image noise are two major concerns causing negative segmentation performance. This paper proposes a regional growth algorithm based on the Gaussian pyramid (GPRG), which automatically selects seed points and optimizes the growth path. To achieve automatic seed selection, Gaussian difference pyramid generated by the Gaussian pyramid is required. The growing seed of the salient target in an image is acquired by searching for extremums in each image of the Gaussian difference pyramid. For growing path optimization, edge changing direction is updated according to different Gaussian difference pyramid images with sizes from small to large. The edge of region growing is determined by the edge of the salient target in an image. Segmentation experiments show that this algorithm is able to automatically select seeds and accurately segment the salient target in an image with and without image noise. Performances in terms of segmentation accuracy (SA), mean intersection-over-union (mIoU) and E-measure are superior compared with other methods.

Pain and Opioid Use in Cancer Survivors: A Practical Guide to Account for Perceived Injustice

The presence of pain decreases survival rates in cancer. Pain management in clinical settings is often suboptimal and secondary to other cancer-related treatments, leaving many people undertreated. Opioid use is associated with side effects and decreased survival rate in cancer patients. Hence, there is an urgent need for considering factors such as perceived injustice that sustain post-cancer pain and trigger a behavioral pattern associated with opioid use. Injustice beliefs represent a maladaptive pattern of cognitive appraisal that may be a salient target for improving pain-related coping in these patients. Perceived injustice is associated with increased opioid prescription and prospectively predicted opioid use at 1-year follow-up, urging the need for targeted interventions to diminish perceived injustice. Explain the importance of screening for perceived injustice in patients with pain following cancer treatment, its potential relevance for opioid abuse, and its potential impact on the management of pain following cancer. Also, prove clinicians with a clinical guide for an approach comprising of modified pain neuroscience education, motivational interviewing, and acceptance-based interventions to account for perceived injustice in patients having pain following cancer. A narrative review, perspective and treatment manual. Several universities, a university of applied science department, a university hospital, and a private clinic (i.e., transdisciplinary pain treatment center). Patients were cancer survivors with pain. Intervention included modified pain neuroscience education, motivational interviewing, and acceptance-based interventions. Measurements were taken through the Injustice Experience Questionnaire (IEQ). The IEQ can be used to assess perceived injustice in a valid way. Education about pain, including discussing perceived injustice, should be the first part of the management of pain in cancer survivors. In order to obtain the often-required behavioral change towards a more adaptive lifestyle, motivational interviewing can be used. To thoroughly tackle perceived injustice in patients having pain following cancer, special emphasis should be given to the individual reasons patients identify for experiencing (continued) pain and related symptoms. Pain acceptance should also be thoroughly addressed. Clinical trials exploring the benefits, including cost-effectiveness, of such a multimodal approach in patients with pain following cancer treatment are needed. In light of its potential relevance for opioid abuse and potential impact on conservative management strategies, clinicians are advised to screen for perceived injustice in patients with pain following cancer treatment. Therapeutic targeting of perceived injustice can be done through an approach comprising of modified pain neuroscience education, motivational interviewing, and acceptance-based interventions.

Plasma proteomic profile associated with platelet dysfunction after trauma

BackgroundCoagulopathic bleeding is a major cause of mortality after trauma, and platelet dysfunction contributes to this problem. The causes of platelet dysfunction are relatively unknown, but a great deal can be learned from the plasma environment about the possible pathways involved. ObjectiveDescribe the changes in plasma proteomic profile associated with platelet dysfunction after trauma. MethodsCitrated blood was collected from severely injured trauma patients at the time of their arrival to the Emergency Department. Samples were collected from 110 patients, and a subset of twenty‐four patients was identified by a preserved (n = 12) or severely impaired (n = 12) platelet aggregation response to five different agonists. Untargeted proteomics was performed by nanoflow liquid chromatography tandem mass spectrometry. Protein abundance levels for each patient were normalized to total protein concentration to control for hemodilution by crystalloid fluid infusion prior to blood draw. ResultsPatients with platelet dysfunction were more severely injured but otherwise demographically similar to those with retained platelet function. Of 232 proteins detected, twelve were significantly different between groups. These proteins fall into several broad categories related to platelet function, including microvascular obstruction with platelet activation, immune activation, and protease activation. ConclusionsThis observational study provides a description of the change in proteomic profile associated with platelet dysfunction after trauma and identifies twelve proteins with the most profound changes. The pathways involving these proteins are salient targets for immediate investigation to better understand platelet dysfunction after trauma and identify targets for intervention.

Reduced alpha amplitudes predict perceptual suppression

The amplitude of prestimulus alpha oscillations over parieto-occipital cortex has been shown to predict visual detection of masked and threshold-level stimuli. Whether alpha activity similarly predicts target visibility in perceptual suppression paradigms, another type of illusion commonly used to investigate visual awareness, is presently unclear. Here, we examined prestimulus alpha activity in the electroencephalogram (EEG) of healthy participants in the context of a generalized flash suppression (GFS) task during which salient target stimuli are rendered subjectively invisible in a subset of trials following the onset of a full-field motion stimulus. Unlike for masking or threshold paradigms, alpha (8–12 Hz) amplitude prior to motion onset was significantly higher when targets remained subjectively visible compared to trials during which the targets became perceptually suppressed. Furthermore, individual prestimulus alpha amplitudes strongly correlated with the individual trial-to-trial variability quenching following motion stimulus onset, indicating that variability quenching in visual cortex is closely linked to prestimulus alpha activity. We conclude that predictive correlates of conscious perception derived from perceptual suppression paradigms differ substantially from those obtained with “near threshold paradigms”, possibly reflecting the effectiveness of the suppressor stimulus.

SSPNet: Learning spatiotemporal saliency prediction networks for visual tracking

We present SSPNet, a novel method for learning the spatiotemporal saliency of a target for visual tracking. State-of-the-art trackers typically track targets by predicting the target state, ie coordinates of a bounding box encompassing the target, from the target candidates sampled around a previous target state. However, they have two limitations: 1) vulnerability to tracking distractors present in a frame and 2) the strong bias of the target state estimation to the initial frame. The proposed method addresses this problem by predicting the spatiotemporal features of the target so-called current and future target saliencies. Given a frame, the current target saliency represents the spatial aspect of the target, whereas the future target saliency depicts how the target will appear in the next frame based on the temporal features. This technique improves tracking accuracy in two ways: we can exploit the similarity between the current and the future target saliencies to detect the distractors. Further, SSPNet provides better prior knowledge about the current target state compared to using only the previous frame, which mitigates the bias to the initial frame and occlusion problem. We show that SSPNet outperforms the state-of-the-art trackers, particularly in challenging sequences.

Understanding the collinear masking effect in visual search through eye tracking.

Recent research has reported that, while both orientation contrast and collinearity increase target salience in visual search, a combination of the two counterintuitively masks a local target. Through eye-tracking and eye-movement analysis with hidden Markov models (EMHMM), here we showed that this collinear masking effect was associated with reduced eye-fixation consistency (as measured in entropy) at the central fixation cross prior to the search display presentation. As a decreased precision of saccade landing position is shown to be related to attention shift away from the saccadic target, our result suggested that the collinear masking effect may be related to attention shift to a non-saccadic-goal location in expectation of the search display before saccading to the central fixation cross. This attention shift may consequently interfere with attention capture by the collinear distractor containing the target, resulting in the masking effect. In contrast, although older adults had longer response times, more dispersed eye-movement pattern, and lower eye-movement consistency than young adults during visual search, the two age groups did not differ in the masking effect, suggesting limited contribution from ageing-related cognitive decline. Thus, participants' pre-saccadic attention shift prior to search may be an important factor influencing their search behavior.

Self-spectral learning with GAN based spectral–spatial target detection for hyperspectral image

To alleviate the shortcomings of target detection in only one aspect and reduce redundant information among adjacent bands, we propose a spectral–spatial target detection (SSTD) framework in deep latent space based on self-spectral learning (SSL) with a spectral generative adversarial network (GAN). The concept of SSL is introduced into hyperspectral feature extraction in an unsupervised fashion with the purpose of background suppression and target saliency. In particular, a novel structure-to-structure selection rule that takes full account of the structure, contrast, and luminance similarity is established to interpret the mapping relationship between the latent spectral feature space and the original spectral band space, to generate the optimal spectral band subset without any prior knowledge. Finally, the comprehensive result is achieved by nonlinearly combining the spatial detection on the fused latent features with the spectral detection on the selected band subset and the corresponding selected target signature. This paper paves a novel self-spectral learning way for hyperspectral target detection and identifies sensitive bands for specific targets in practice. Comparative analyses demonstrate that the proposed SSTD method presents superior detection performance compared with CSCR, ACE, CEM, hCEM, and ECEM.

Top-down and bottom-up guidance in normal aging during scene search.

Age-related differences in visual search have been extensively studied using simple item arrays, showing an attentional decline. Little is known about how aging affects attentional guidance during search in more complex scenes. To study this issue, we analyzed eye-movement behavior in realistic scene search. We examined age-related differences in top-down guidance, manipulating target template specificity (picture vs. word cue) and target-scene semantic consistency (consistent vs. inconsistent), and in bottom-up guidance, manipulating perceptual salience (high vs. low) of targets and distractors. Compared to young adults (YA), older adults (OA) were overall slower, from the first saccade in the scene. They showed a smaller benefit of a specific target template, suggesting that precision of visual information in working memory may decrease with age. The benefit of semantic consistency did not depend on age, suggesting a preserved ability in OA to use knowledge about object occurrence in scenes. OA showed greater bottom-up search facilitation due to target's high salience, which may depend on reduced selection of low-salience stimuli. Attentional capture by distractors was greater in OA than YA, with respect to engagement (probability of distractor fixation), but only following a picture cue, and disengagement (fixation duration on distractors) in all conditions. Overall, our study shows that age-related differences in visual selection of targets and distractors depend on specific task demands in terms of top-down and bottom-up guidance. It also indicates that scene search difficulties in OA can be limited by cognitive and perceptual forms of environmental support. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A novel measure of matching categories for early development: Item creation and pilot feasibility study

BackgroundMany cognitive tests assess a limited developmental span, making longitudinal measurement for trials aimed at improving cognition challenging. Tests targeting transitional skills, which integrate foundational abilities into complex schemas, may be amenable to assessment across a wide developmental span. Furthermore, tablet-based tests permit computer adaptive testing (CAT), which is psychometrically more efficient and could increase testing motivation, especially for children with developmental delays. Such measures may be useful for research and clinical practice. AimsOutline the creation of a novel, tablet-based concept formation test, and evaluate its feasibility in individuals with mental ages less than 24-months. Methods and proceduresItem generation, user interface construction, and pre-piloting were conducted in consultation with subject matter experts. Item content and interface parameters underwent iterative revisions, resulting in the pilot test. Outcomes and resultsWe created and piloted a tablet-based test of concept formation suitable for CAT-based administration with items of increasing difficulty based on target salience. We show feasibility in individuals with mental ages less than 24-months-old. Conclusions and implicationsTablet-based assessment of concept formation may be a useful outcome measure of an aspect of cognitive ability in young children. Future work will address optimizing the user interface and developing CAT administration.

Attentional capture: An ameliorable side-effect of searching for salient targets

ABSTRACT This commentary highlights that some of the remaining discrepancies in the attentional-capture debate can be resolved by a simple assumption: observers do not use the priority map when this map is useless to solve the task. Rather, whenever search targets are known to be non-salient, observers resort to a previously postulated alternative search strategy for which (distractor) saliency signals are irrelevant. Equipped with this assumption, we trace thus-far unaccounted-for discrepancies between empirical studies on attentional capture back to specific design choices that affect relative target saliency (display density and non-target heterogeneity).

Multiple Infrared Small Targets Detection Based on Hierarchical Maximal Entropy Random Walk

The technique of detecting multiple dim and small targets with low signal-to-clutter ratios (SCR) is essential for infrared search and tracking systems. In this letter, we establish a multiple small targets detection method derived from hierarchical maximal entropy random walk (HMERW). The HMERW revolves the limitation of strong bias to the most salient target of the primal maximal entropy random walk (MERW) based on a proposed graph decomposition theory. To enhance the characteristics of small targets and suppress strong clutters, we design a specific weight matrix for HMERW instead of using the conventional weight matrix in MERW. First, a stationary distribution map is obtained by importing the filtered infrared image into the HMERW. Second, a coefficient map is constructed based on the designed weight matrix to fuse the stationary distribution map. Then, an adaptive threshold is used to segment multiple small targets from the fusion map. Extensive experiments on practical datasets demonstrate that the proposed method is superior to the state-of-the-art methods in terms of target enhancement, background suppression, and multiple small targets detection.

Research on image saliency target detection in fog and haze weather based on improved FT algorithm

The increase of air turbidity in fog and haze weather is not conducive to the accurate extraction of image feature information of important salient targets such as vehicles and pedestrians, nor is it conducive for drivers to make correct judgment on the road environment ahead of their vision. Therefore, this paper proposes an improved FT algorithm in fog and haze weather. This algorithm firstly uses the Retinex algorithm to remove fog and haze and restore color of fog and haze weather images, then uses FT algorithm to detect the saliency of the target, and finally completes the simulation based on MATLAB platform. It can be seen from the simulation results that the improved FT algorithm is significantly better than the effect of using the traditional FT algorithm, which means that the improved FT algorithm has a better role in helping drivers to detect road salient targets in fog and haze weather. And this improvement has a strong practical value for reducing the incidence of traffic accidents in fog and haze weather.

Salient target detection in hyperspectral image based on visual attention

Salient target detection in hyperspectral image is a significant task in image segmentation, target tracking, image classification and so on. Many existing saliency detection algorithms for hyperspectral image detection cannot present the boundary of the salient target well and the description of the target is not enough. A method based on visual attention to detect the salient target of hyperspectral image is proposed in this paper. In this method, frequency-tuned (FT) salient detection model is combined with spectral salient to detect target in hyperspectral image. FT model is used to get target with clear border, and spectral information is made full use of to improve the accuracy of target detection. Firstly, FT is used to detect saliency of hyperspectral image and the saliency map is generated. Then, spectral information of the hyperspectral image is measured by similarity, and the spectral saliency is obtained by calculating spectral angle distance between the spectral vectors. Finally, the FT's saliency map and the spectral saliency map are combined to form the final saliency target maps. Experimental results show that our method is superior to other methods in saliency target detection of hyperspectral image, and the precision-recall curve and F-measure are better as well.

Infrared and visible image fusion based on edge-preserving guided filter and infrared feature decomposition

Infrared (IR) images can distinguish salient targets from their backgrounds based on the radiation difference in all-weather conditions. By contrast, visible (VIS) images can contain high-resolution texture and details information, which is more suitable for human observation. Therefore, it is quite necessary to combine both imaging advantages of these two kinds of images. Compared with the existing methods, we believe that scale decomposition based methods are the most active and efficient image fusion methods, which also have the best fusion effects. Inspired by the present scale decomposition methods, we propose a new feature decomposition method. Firstly, we propose an improved guided filter called edge-preserving guided filter (EPGF), which adopts the image gradient map for further improving the filtering effect. Subsequently, by using the EPGF, we decompose the IR and VIS images into three kinds of layers, including salient feature layers, luminance layers and detail layers. At the same time, we combine all the layers together to get an initial fusion result. Finally, we optimize the initial fusion image according to a new image fusion optimization model and ADMM, and the final fusion result will be obtained after several iterations. Compared with other scale decomposition methods, our proposed feature decomposition based method takes the IR salient targets, IR and VIS background illumination, as well as VIS details into consideration which is more in line with human visual observation, besides the computational efficiency is also superior. Experimental results indicate that this method has better subjective and objective evaluation results compared with other state-of-the-art methods.

The effect of target salience and size in visual search within naturalistic scenes under degraded vision.

We address two questions concerning eye guidance during visual search in naturalistic scenes. First, search has been described as a task in which visual salience is unimportant. Here, we revisit this question by using a letter-in-scene search task that minimizes any confounding effects that may arise from scene guidance. Second, we investigate how important the different regions of the visual field are for different subprocesses of search (target localization, verification). In Experiment 1, we manipulated both the salience (low vs. high) and the size (small vs. large) of the target letter (a “T”), and we implemented a foveal scotoma (radius: 1°) in half of the trials. In Experiment 2, observers searched for high- and low-salience targets either with full vision or with a central or peripheral scotoma (radius: 2.5°). In both experiments, we found main effects of salience with better performance for high-salience targets. In Experiment 1, search was faster for large than for small targets, and high-salience helped more for small targets. When searching with a foveal scotoma, performance was relatively unimpaired regardless of the target's salience and size. In Experiment 2, both visual-field manipulations led to search time costs, but the peripheral scotoma was much more detrimental than the central scotoma. Peripheral vision proved to be important for target localization, and central vision for target verification. Salience affected eye movement guidance to the target in both central and peripheral vision. Collectively, the results lend support for search models that incorporate salience for predicting eye-movement behavior.

Infrared and visible image fusion based on two-scale decomposition and improved saliency detection

Aiming at the problems of poor target saliency, loss of background information and time-consuming in image fusion, a fast image fusion algorithm combining two-scale decomposition and improved saliency detection is proposed. Mean filtering is used to decompose the source image into a base layer and a detail layer. The maximum symmetric surround (MSS) saliency detection algorithm is improved to obtain the dim suppressed MSS algorithm. dim suppressed MSS saliency detection and guided filtering is used to generate fusion rules for each layer. The inverse transformation of two-scale decomposition is used for the fusion sub-image of the base layer and the detail layer to obtain the final fusion result. Experimental results show that the algorithm consumes less time and has better fusion quality, which reflects the feasibility of the proposed algorithm.

Trends in method-specific suicide in Brazil from 2000 to 2017

PurposeUnderstanding long-term patterns of suicide methods can inform public health policy and prevention strategies. In Brazil, firearm-related policies may be one salient target for suicide prevention. This study describes trends in method-specific suicide at the national and state-levels in Brazil, with a particular focus on firearm-related suicides.MethodsBrazilian mortality data for suicide and undetermined intent among people aged 10 years and older between 2000 and 2017 were obtained from the National Mortality Information System. We examined national and state-level trends in age-standardised suicide rates for hanging, self-poisoning, firearms, jumping from a high place, other, and unspecified methods. We also compared total rates of mortality from suicide and undetermined intent over the period. Applying Joinpoint regression, we tested changes in trends of firearm-specific suicide rates.ResultsThe total suicide rate increased between 2000 and 2017. Rates of hanging, self-poisoning by drugs or alcohol and jumping from a high place showed the largest increases, while firearm-specific suicide rates decreased over the study period. Trends in methods of suicide varied by sex and state.ConclusionIt is of public health concern that suicide rates in Brazil have risen this millennium. Restricting access to firearms might be an effective approach for reducing firearm-specific suicides, especially in states where firearm availability remains particularly high. Treatment and management of substance misuse may also be an important target for suicide prevention policies. More work is needed to understand the causes of rising suicide rates in Brazil and to improve the mental health of the population.

Deployment dynamics of hypnotic anger modulation

To understand the role that attention plays in the deployment timeline of hypnotic anger modulation, we composed an Attentional Blink paradigm where the first and second targets were faces, expressing neutral or angry emotions. We then suppressed the salience of angry faces through a “hypnotic numbing” suggestion. We found that hypnotic suggestion only attenuated the emotional salience of the second target (T2). By implementing drift-diffusion decision modelling, we also found that hypnotic suggestion mainly affected decision thresholds. These findings suggest that hypnotic numbing resulted from belated changes in response strategy. Interestingly, a contrast against non-hypnotized participants revealed that the numbing suggestion had the instruction-like feature of incorporating emotional valence into the attentional task-set. Together, our results portray hypnotic anger modulation as a two-tiered process: first, hypnotic suggestion alters the attentional task-set; second, provided processing and response preparation are not interrupted, a hypnotizability-dependent response based on said altered task-set is produced through late cognitive control strategies.

Cross-attention multi-branch network for fundus diseases classification using SLO images.

Fundus diseases classification is vital for the health of human beings. However, most of existing methods detect diseases by means of single angle fundus images, which lead to the lack of pathological information. To address this limitation, this paper proposes a novel deep learning method to complete different fundus diseases classification tasks using ultra-wide field scanning laser ophthalmoscopy (SLO) images, which have an ultra-wide field view of 180-200˚. The proposed deep model consists of multi-branch network, atrous spatial pyramid pooling module (ASPP), cross-attention and depth-wise attention module. Specifically, the multi-branch network employs the ResNet-34 model as the backbone to extract feature information, where the ResNet-34 model with two-branch is followed by the ASPP module to extract multi-scale spatial contextual features by setting different dilated rates. The depth-wise attention module can provide the global attention map from the multi-branch network, which enables the network to focus on the salient targets of interest. The cross-attention module adopts the cross-fusion mode to fuse the channel and spatial attention maps from the ResNet-34 model with two-branch, which can enhance the representation ability of the disease-specific features. The extensive experiments on our collected SLO images and two publicly available datasets demonstrate that the proposed method can outperform the state-of-the-art methods and achieve quite promising classification performance of the fundus diseases.

How Do Art Skills Influence Visual Search? - Eye Movements Analyzed With Hidden Markov Models.

The results of two experiments are analyzed to find out how artistic expertise influences visual search. Experiment I comprised survey data of 1,065 students on self-reported visual memory skills and their ability to find three targets in four images of artwork. Experiment II comprised eye movement data of 50 Visual Literacy (VL) experts and non-experts whose eye movements during visual search were analyzed for nine images of artwork as an external validation of the assessment tasks performed in Sample I. No time constraint was set for completion of the visual search task. A latent profile analysis revealed four typical solution patterns for the students in Sample I, including a mainstream group, a group that completes easy images fast and difficult images slowly, a fast and erroneous group, and a slow working student group, depending on task completion time and on the probability of finding all three targets. Eidetic memory, performance in art education and visual imagination as self-reported visual skills have significant impact on latent class membership probability. We present a hidden Markov model (HMM) approach to uncover underlying regions of attraction that result from visual search eye-movement behavior in Experiment II. VL experts and non-experts did not significantly differ in task time and number of targets found but they did differ in their visual search process: compared to non-experts, experts showed greater precision in fixating specific prime and target regions, assessed through hidden state fixation overlap. Exploratory analysis of HMMs revealed differences between experts and non-experts in image locations of attraction (HMM states). Experts seem to focus their attention on smaller image parts whereas non-experts used wider parts of the image during their search. Differences between experts and non-experts depend on the relative saliency of targets embedded in images. HMMs can determine the effect of expertise on exploratory eye movements executed during visual search tasks. Further research on HMMs and art expertise is required to confirm exploratory results.