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Tupas and Lorente (2014. A 'new' politics of language in the Philippines: Bilingual education and the new challenge of the mother tongues. In Peter Sercombe & Ruanni Tupas (eds.), Language, education and nation-building: Assimilation and shift in Southeast Asia, 165–180. New York: Springer) contended that "the politics of language in the Philippines always featured the tension between English on the one hand and the vernacular languages on the other." But how exactly does this language dynamic manifest itself in the linguistic landscapes (LL) of the Philippines? To explore this question, this paper conducted an exploratory LL analysis of Intramuros, the famed "Walled City" of Manila, using Scollon and Scollon's (2003. Discourses in place: Language in the material world. London: Routledge) place semiotics and Ben-Rafael et al.'s (2006. Linguistic landscape as symbolic construction of the public space: The case of Israel. International Journal of Multilingualism 3(1). 7–30) top-down and bottom-up sign classification. It found that English-based signs are used to accommodate a global audience, i.e., foreign tourists, whereas Filipino-based signs are used to police and regulate the behavior of residents and, to a certain extent, local tourists. To conclude, it argued that by looking at its linguistic landscape, historical districts like Intramuros articulate beliefs and assumptions on language that, in turn, make them deeply political and ideological sites. [ABSTRACT FROM AUTHOR]

Abstract We propose an improved superpixel segmentation algorithm based on visual saliency and color entropy for online color detection in printed products. This method addresses the issues of low accuracy and slow speed in detecting color deviations in print quality control. The improved superpixel segmentation algorithm consists of three main steps: Firstly, simulating human visual perception to obtain visually salient regions of the image, thereby achieving region-based superpixel segmentation. Secondly, adaptively determining the superpixel size within the salient regions using color information entropy. Finally, the superpixel segmentation method is optimized using hue angle distance based on chromaticity, ultimately achieving a region-based adaptive superpixel segmentation algorithm. Color detection of printed products compares the color mean values of post-printing images under the same superpixel labels, outputting labels with color deviations to identify areas of color differences. The experimental results show that the improved superpixel algorithm introduces color phase distance with better segmentation accuracy, and combines it with human visual perception to better reproduce the color information of printed materials. Using the method described in this article for printing color quality inspection can reduce data computation, quickly detect and mark color difference areas, and provide the degree of color deviation.

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Near-infrared fluorescence imaging in the second window has emerged as a valuable tool for the non-invasive and real-time assessment of vascular information in skin flaps. Enhancing flap images to provide more accurate flap vascularization information is critical for predicting flap viability. To address the limitations of existing methods in enhancing vessel images, we propose a novel and adaptive technique for enhancing flap microvessel images. Multiple strategies can be employed to effectively enhance the visualization of small-scale vessels. Firstly, the proposed method leverages the multiscale rolling guided filter to acquire the base layer and detail layers at different scales. Furthermore, correlation coefficients are utilized to weigh and fuse the detail layers effectively. To suppress noise amplification while enhancing vascular structures, an improved adaptive gamma correction method based on local visual saliency is introduced. Meanwhile, the bilateral gamma correction is used to enhance the base layer. Finally, the enhanced base layer and detail layer are fused using the weighted fusion strategy. We conducted experiments on skin flap vessel images, retinal fundus images, finger vein images, and low-light images. Our method achieved excellent results in metrics such as NIQE, AMBE, and WPSNR, demonstrating significant advantages in preserving the structural integrity and brightness consistency of the images. The obtained results validate the potential of this method in enhancing vascular images, indicating promising prospects in the field of medicine. [ABSTRACT FROM AUTHOR]

Background: Visual exploration is abnormal in schizophrenia; however, few studies have investigated the physiological responses during selecting objectives in more ecological scenarios. This study aimed to demonstrate that people with schizophrenia have difficulties observing the prominent elements of an image due to a deficit mechanism of sensory modulation (active sensing) during natural vision. Methods: An electroencephalogram recording with eye tracking data was collected on 18 healthy individuals and 18 people affected by schizophrenia while looking at natural images. These had a prominent color element and blinking produced by changes in image luminance. Results: We found fewer fixations when all images were scanned, late focus on prominent image areas, decreased amplitude in the eye-fixation-related potential, and decreased intertrial coherence in the SCZ group. Conclusions: The decrease in the visual attention response evoked by the prominence of visual stimuli in patients affected by schizophrenia is generated by a reduction in endogenous attention mechanisms to initiate and maintain visual exploration. Further work is required to explain the relationship of this decrease with clinical indicators.

To ensure survival, the visual system must rapidly extract the most important elements from a large stream of information. This necessity clashes with the computational limitations of the human brain, so a strong early data reduction is required to efficiently process information in fast vision. A theoretical early vision model, recently developed to preserve maximum information using minimal computational resources, allows efficient image data reduction by extracting simplified sketches containing only optimally informative, salient features. Here, we investigate the neural substrates of this mechanism for optimal encoding of information, possibly located in early visual structures. We adopted a flicker adaptation paradigm, which has been demonstrated to specifically impair the contrast sensitivity of the magnocellular pathway. We compared flicker-induced contrast threshold changes in three different tasks. The results indicate that, after adapting to a uniform flickering field, thresholds for image discrimination using briefly presented sketches increase. Similar threshold elevations occur for motion discrimination, a task typically targeting the magnocellular system. Instead, contrast thresholds for orientation discrimination, a task typically targeting the parvocellular system, do not change with flicker adaptation. The computation performed by this early data reduction mechanism seems thus consistent with magnocellular processing. [ABSTRACT FROM AUTHOR]

Strong sun glint noise is an inevitable obstruction for tiny human object detection in maritime search and rescue (SAR) tasks, which can significantly deteriorate the performance of local contrast method (LCM)-based algorithms and cause high false alarm rates. For SAR tasks in noisy environments, it is more important to find tiny objects than localize them. Hence, considering background clutter and strong glint noise, in this study, a noise suppression methodology for maritime scenarios (HDetect-VS) is established to achieve tiny human object enhancement and detection based on visual saliency. To this end, the pixel intensity value distributions, color characteristics, and spatial distributions are thoroughly analyzed to separate objects from background and glint noise. Using unmanned aerial vehicles (UAVs), visible images with rich details, rather than infrared images, are applied to detect tiny objects in noisy environments. In this study, a grayscale model mapped from the HSV model (HSV-gray) is used to suppress glint noise based on color characteristic analysis, and large-scale Gaussian Convolution is utilized to obtain the pixel intensity surface and suppress background noise based on pixel intensity value distributions. Moreover, based on a thorough analysis of the spatial distribution of objects and noise, two-step clustering is employed to separate objects from noise in a salient point map. Experiments are conducted on the SeaDronesSee dataset; the results illustrate that HDetect-VS has more robust and effective performance in tiny object detection in noisy environments than other pixel-level algorithms. In particular, the performance of existing deep learning-based object detection algorithms can be significantly improved by taking the results of HDetect-VS as input. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Salient Object Detection (SOD) can mimic the human vision system by using algorithms that simulate the way how the eye detects and processes visual information. It focuses mainly on the visually distinctive parts of an image, similar to how the human brain processes visual information. The approach proposed in this study is an ensemble approach that incorporates classification algorithm, foreground connectivity and prior calculations. It involves a series of preprocessing, feature generation, selection, training, and prediction using random forest to identify and extract salient objects in an image as a first step. Next, an object proposals map is created for the foreground object. Subsequently, a fusion map is generated using boundary, global, and local contrast priors. In the feature generation step, different edge filters are implemented as the saliency score at edges will be high; additionally, with the use of Gabor's filter the texture-based features are calculated. The Boruta feature selection algorithm is then used to identify the most appropriate and discriminative features, which helps to reduce the computational time required for feature selection. Ultimately, the initial map obtained from the random forest, along with the fusion saliency maps based on foreground connectivity and prior calculations, is merged to produce a saliency map. This map is then refined using post-processing techniques to acquire the final saliency map. The approach we propose surpasses the performance of 17 cutting-edge techniques across three benchmark datasets, showcasing superior results in terms of precision, recall, and f-measure. The proposed method performs well even on the DUTOMRON dataset, known for its multiple salient objects and complex backgrounds, achieving a Mean Absolute Error (MAE) value of 0.113. The method also demonstrates high recall values (0.862, 0.923, 0.849 for ECSSD, MSRA-B and DUT-OMRON datasets, respectively) across all datasets, further establishing its suitability for salient object detection. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Landslides, ubiquitous geological hazards on steep slopes, present formidable challenges in tropical regions with dense rainforest vegetation, impeding accurate mapping and risk assessment. To address this, we propose an innovative deep-learning framework utilizing visual saliency for automatic landslide identification, employing super-resolution remote sensing image datasets. Unlike conventional models relying on raw images, our method leverages saliency-generated feature maps, achieving a remarkable 94% accuracy, surpassing existing models by 5%. Comprehensive experimental findings consistently demonstrate its superiority over established algorithms, highlighting its robust performance. This novel approach introduces a valuable dimension to landslide detection, particularly in complex terrains, offering a promising tool for advancing risk assessment and management in landslide-prone areas.

Point cloud upsampling is a process of increasing the point density to represent an object or environment effectively. Recent studies have focused on deep learning-based approaches that learn mapping from a sparse to a dense region of the point cloud. Self-supervised learning-based upsampling techniques have gained attention due to their capability to learn predefined characteristics without previous training on a large dataset. This study proposes deep self-sampling with point saliency. The approach involves the use of a self-sampling network with two predefined consolidation strategies, namely density and curvature, along with a saliency feature, to restore the underlying characteristics of an object effectively. Additionally, multistep upsampling is applied to determine the best order of different consolidation strategies for optimal results. Experimental results show that multistep self-sampling using point saliency outperforms the existing approach because it can effectively restore the underlying shapes of the object qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Deep neural networks detect visual saliency with semantic information. These high-level features locate salient regions efficiently but pay less attention to structure preservation. In our paper, we emphasize crucial low-level features for deep neural networks in order to preserve local structure and integrity of objects. The proposed framework consists of an image enhancement network and a saliency prediction network. In the first part of our model, we segment the image with a superpixel based unit-linking pulse coupled neural network (PCNN) and generate a weight map representing contrast and spatial properties. With the help of these low-level features, a fully convolutional network (FCN) is employed to compute saliency map in the second part. The weight map enhances the input channels of the FCN, meanwhile refines the output prediction with polished details and contours of salient objects. We demonstrate the superior performance of our model against other state-of-the-art approaches through experimental results on five benchmark datasets. [ABSTRACT FROM AUTHOR]

In order to solve the problem that a large number of low-value and easy-to-consume graphic designs consume the human and financial resources of the society and also waste the inherent value of design resources, by dividing the layers of pixels, elements, relations, planes and applications, this article defines the corresponding feature representations and studies related methods for quantifying geometric features, perceptual features and style features. The content includes the extraction method of element colour, the calculation method of layout-aware feature and colour-match-aware feature, and the pairwise comparison method of style feature. The experimental results show that the method proposed in this article has lower complexity and is significantly better than other algorithms. Comparing the method proposed in this article with other methods, the average time of this algorithm is 2.6 s under the condition of guaranteed images. The study of the design feature-driven graphic image style calculation method is not only of great significance for solving repeated design problems but also provides a basis and reference for the future development of intelligent design.

Strong sun glint noise is an inevitable obstruction for tiny human object detection in maritime search and rescue (SAR) tasks, which can significantly deteriorate the performance of local contrast method (LCM)-based algorithms and cause high false alarm rates. For SAR tasks in noisy environments, it is more important to find tiny objects than localize them. Hence, considering background clutter and strong glint noise, in this study, a noise suppression methodology for maritime scenarios (HDetect-VS) is established to achieve tiny human object enhancement and detection based on visual saliency. To this end, the pixel intensity value distributions, color characteristics, and spatial distributions are thoroughly analyzed to separate objects from background and glint noise. Using unmanned aerial vehicles (UAVs), visible images with rich details, rather than infrared images, are applied to detect tiny objects in noisy environments. In this study, a grayscale model mapped from the HSV model (HSV-gray) is used to suppress glint noise based on color characteristic analysis, and large-scale Gaussian Convolution is utilized to obtain the pixel intensity surface and suppress background noise based on pixel intensity value distributions. Moreover, based on a thorough analysis of the spatial distribution of objects and noise, two-step clustering is employed to separate objects from noise in a salient point map. Experiments are conducted on the SeaDronesSee dataset; the results illustrate that HDetect-VS has more robust and effective performance in tiny object detection in noisy environments than other pixel-level algorithms. In particular, the performance of existing deep learning-based object detection algorithms can be significantly improved by taking the results of HDetect-VS as input.

Architecture is a representative of a city. It is also a spatial carrier of urban culture. Identifying the architectural features in a city can help with urban transformation and promote urban development. The use of visual saliency models in regional architectural texture recognition can effectively enhance the effectiveness of regional architectural texture recognition. In this paper, the improved visual saliency model first enhances the texture images of regional buildings through histogram enhancement technology, and uses visual saliency algorithms to extract the visual saliency of the texture features of regional buildings. Then, combined with the maximum interclass difference method of threshold segmentation, the visual saliency image is segmented to achieve accurate target recognition. Finally, the feature factor iteration of the Bag of Visual Words model and the function classification of support vector machines were used to complete the recognition of regional architectural texture features. Through experimental verification, the constructed regional architectural texture feature recognition method based on visual saliency model can effectively enhance the recognition image. This method performs well in boundary contour separation and visual saliency, with an average recognition rate of 0.814 for texture features in different building scenes, indicating high stability. [ABSTRACT FROM AUTHOR]

Scene graph aims to faithfully reveal humans' perception of image content. When humans look at a scene, they usually focus on their interested parts in a special priority. This innate habit indicates a hierarchical preference about human perception. Therefore, we argue to generate the Scene Graph of Interest which should be hierarchically constructed, so that the important primary content is firstly presented while the secondary one is presented on demand. To achieve this goal, we propose the Tree–Guided Importance Ranking (TGIR) model. We represent the scene with a hierarchical structure by firstly detecting objects in the scene and organizing them into a Hierarchical Entity Tree (HET) according to their spatial scale, considering that larger objects are more likely to be noticed instantly. After that, the scene graph is generated guided by structural information of HET which is modeled by the elaborately designed Hierarchical Contextual Propagation (HCP) module. To further highlight the key relationship in the scene graph, all relationships are re-ranked through additionally estimating their importance by the Relationship Ranking Module (RRM). To train RRM, the most direct way is to collect the key relationship annotation, which is the so-called Direct Supervision scheme. As collecting annotation may be cumbersome, we further utilize two intuitive and effective cues, visual saliency and spatial scale, and treat them as Approximate Supervision, according to the findings that these cues are positively correlated with relationship importance. With these readily available cues, the RRM is still able to estimate the importance even without key relationship annotation. Experiments indicate that our method not only achieves state-of-the-art performances on scene graph generation, but also is expert in mining image-specific relationships which play a great role in serving subsequent tasks such as image captioning and cross-modal retrieval. [ABSTRACT FROM AUTHOR]

Fabric defect detection is of great importance in the modern textile industry. In actual production, subjective factors usually affect manual detection, which is prone to problems such as false and missed detection. With the development of computer vision, a large number of fabric defect automatic detection algorithms have been proposed. Traditional algorithms rely too heavily on setting parameters manually, while deep learning algorithms have expensive training and computing costs. Given these limitations, a new fabric defect detection method based on the latest cartoon texture image decomposition model, visual saliency algorithm, and mathematical morphology, is proposed in this article. A digital image acquisition system is also designed and constructed. Therefore, the self-made dataset used in the experiment is composed of self-collected images and network public images. To further evaluate the performance of the proposed method, this study conducted fabric defect detection experiments and comparison experiments based on the self-made dataset. The results show that this method can successfully detect fabric defects, having high detection accuracy and efficiency. Combining subjective vision and objective evaluation, comparison experiments prove that this method is superior to other common fabric defect detection methods and has the highest value of accuracy and F1-score. This research provides a new method and technical support for fabric defect detection and other fields. [ABSTRACT FROM AUTHOR]

Individuals with cerebral visual impairment (CVI) have difficulties identifying common objects, especially when presented as cartoons or abstract images. In this study, participants were shown a series of images of ten common objects, each from five possible categories ranging from abstract black & white line drawings to color photographs. Fifty individuals with CVI and 50 neurotypical controls verbally identified each object and success rates and reaction times were collected. Visual gaze behavior was recorded using an eye tracker to quantify the extent of visual search area explored and number of fixations. A receiver operating characteristic (ROC) analysis was also carried out to compare the degree of alignment between the distribution of individual eye gaze patterns and image saliency features computed by the graph-based visual saliency (GBVS) model. Compared to controls, CVI participants showed significantly lower success rates and longer reaction times when identifying objects. In the CVI group, success rate improved moving from abstract black & white images to color photographs, suggesting that object form (as defined by outlines and contours) and color are important cues for correct identification. Eye tracking data revealed that the CVI group showed significantly greater visual search areas and number of fixations per image, and the distribution of eye gaze patterns in the CVI group was less aligned with the high saliency features of the image compared to controls. These results have important implications in helping to understand the complex profile of visual perceptual difficulties associated with CVI. [ABSTRACT FROM AUTHOR]

In the process of making a movie, directors constantly care about where the spectator will look on the screen. Shot composition, framing, camera movements, or editing are tools commonly used to direct attention. In order to provide a quantitative analysis of the relationship between those tools and gaze patterns, we propose a new eye-tracking database, containing gaze-pattern information on movie sequences, as well as editing annotations, and we show how state-of-the-art computational saliency techniques behave on this dataset. In this work, we expose strong links between movie editing and spectators gaze distributions, and open several leads on how the knowledge of editing information could improve human visual attention modeling for cinematic content. The dataset generated and analyzed for this study is available at https://github.com/abruckert/eye_tracking_filmmaking [ABSTRACT FROM AUTHOR]

Abstract Unconstrained face images are interfered by many factors such as illumination, posture, expression, occlusion, age, accessories and so on, resulting in the randomness of the noise pollution implied in the original samples. In order to improve the sample quality, a weighted block cooperative sparse representation algorithm is proposed based on visual saliency dictionary. First, the algorithm uses the biological visual attention mechanism to quickly and accurately obtain the face salient target and constructs the visual salient dictionary. Then, a block cooperation framework is presented to perform sparse coding for different local structures of human face, and the weighted regular term is introduced in the sparse representation process to enhance the identification of information hidden in the coding coefficients. Finally, by synthesising the sparse representation results of all visual salient block dictionaries, the global coding residual is obtained and the class label is given. The experimental results on four databases, that is, AR, extended Yale B, LFW and PubFig, indicate that the combination of visual saliency dictionary, block cooperative sparse representation and weighted constraint coding can effectively enhance the accuracy of sparse representation of the samples to be tested and improve the performance of unconstrained face recognition.

Small target detection under a complex background has always been a hot and difficult problem in the field of image processing. Due to the factors such as a complex background and a low signal-to-noise ratio, the existing methods cannot robustly detect targets submerged in strong clutter and noise. In this paper, a local gradient contrast method (LGCM) is proposed. Firstly, the optimal scale for each pixel is obtained by calculating a multiscale salient map. Then, a subblockbased local gradient measure is designed; it can suppress strong clutter interference and pixel-sized noise simultaneously. Thirdly, the subblock-based local gradient measure and the salient map are utilized to construct the LGCM. Finally, an adaptive threshold is employed to extract the final detection result. Experimental results on six datasets demonstrate that the proposed method can discard clutters and yield superior results compared with state-of-the-art methods.

Video compression has become increasingly crucial as video resolution and bitrate have surged in recent years. However, most widely applied video compression methods do not fully exploit the characteristics of the Human Visual System (HVS) to reduce perceptual redundancy in videos. In this paper, we propose a novel video compression method that integrates visual saliency information with foveation to reduce perceptual redundancy. We present a new approach to subsample and restore the input image using saliency data, which allocates more space for salient regions and less for non-salient ones. We analyze the information entropy in video frames before and after applying our algorithm and demonstrate that the proposed method reduces redundancy. Through subjective and objective evaluations, we show that our method produces videos with superior perceptual visual quality. Moreover, our approach can be added to most existing video compression standards without altering their bitstream format.

Web designers should develop webpages that are easy to navigate and engage users. However, an aesthetic design does not guarantee usability. Users tend to avoid webpages with low usability, resulting in poor service and sales. One cause of inadequate usability is the difference between the intended content and what the users see on the webpage. On the other hand, visual saliency is the ease with which the human eye notices something. Visual saliency maps (VSMs) are used to recognize areas in an image where people pay attention. VSMs often represent the visual features of landscapes and human faces. Here, we propose a method to generate a webpage-specific VSM based on the layout. Our method computes and visualizes the visual salience level for each webpage element, allowing web designers to easily recognize webpage elements with a high visual prominence. An evaluation revealed that the quality of VSMs generated by our method is higher than those generated by existing methods. Moreover, the VSMs generated by our method are easier to recognize than those of existing methods.

This study improves a collection of attributes to detect subtle faults in three dimensional data obtained from the Krishna-Godavari (KG) basin, with results displayed on synthetic and real datasets. Seismic attributes, for instance, curvature and coherence, are often used to delineate discontinuities, such as faults and fractures where hydrocarbons may have been trapped. These attributes have their advantages subjective to the seismic data. In this paper, we propose a multi-attribute framework for identifying subtle faults inside seismic volumes. Curvature attribute is a powerful and popular technique to deal with these faults. The faulted horizon is fitted on the quadratic surface using the least-square method, and the most positive and most-negative curvature attributes are calculated, which are further used in saliency map calculations. Several signal processing techniques, such as Hough transform and ant tracking, have been used to delineate faults. Here, we have proposed a novel signal processing approach based on energy variations known as top-down saliency on the curvature attributes using 3D-FFT local spectra and multi-dimensional plane projections. To analyze the directional nature of seismic data, the directional center-surround technique is employed for visual attention. Furthermore, the log-Gabor filter and image erosion are applied to the saliency-rendered seismic volume to highlight the oriented amplitude discontinuities at faults. Most of the time, these discontinuities may not be very prominent to find the subtle faults and other trace-to-trace hidden geological features in three-dimensional seismic data. In our work, calculated attributes assist us in mapping these changes, because they are all differently sensitive to the faults and fractures in unique ways. Experimental results on real field seismic data from the Krishna-Godavari basin prove that the proposed algorithm is effective and efficient in tracking subtle and minor faults, better than previous works. [ABSTRACT FROM AUTHOR]

Fabric defect detection plays an irreplaceable role in the quality control of the textile manufacturing industry, but it is still a challenging task due to the diversity and complexity of defects and environmental factors. Visual saliency models imitating the human vision system can quickly determine the defect regions from the complex texture background. However, most visual saliency-based methods still suffer from incomplete predictions owing to the variability of fabric defects and low contrast with the background. In this paper, we develop a context-aware attention cascaded feedback network for fabric defect detection to achieve more accurate predictions, in which a parallel context extractor is designed to characterize the multi-scale contextual information. Moreover, a top-down attention cascaded feedback module was devised adaptively to select the important multi-scale complementary information and then transmit it to an adjacent shallower layer to compensate for the inconsistency of information among layers for accurate location. Finally, a multi-level loss function is applied to guide our model for generating more accurate prediction results via optimizing multiple side-output predictions. Experimental results on the two fabric datasets built under six widely used evaluation metrics demonstrate that our proposed framework outperforms state-of-the-art models remarkably. [ABSTRACT FROM AUTHOR]

The automatic identification of bird’s nest in the inspection image of transmission line is of great significance to the safe operation of transmission line. In this paper, a bird’s nest recognition method which combines visual saliency and depth learning is proposed. This method not only has the advantage of rich feature information of visible light image, but also has the advantage of significant bird’s nest target. The experimental results show that this method can accurately identify the images with different background, tower shape, shooting angle and shooting distance, and has good robustness and generalization, and the precision index values of Precision, Recall and IoU are 0.9622, 0.9465 and 0.9543 respectively. Compared with Faster R-CNN model, YOLO model and RetinaNet model, each index is greatly improved. This method is instructive to the operation and maintenance of transmission lines.

In order to address driver's dazzle caused by the abuse of high beams when vehicles meet at night, a night vision anti-halation algorithm based on image fusion combining visual saliency with YUV-FNSCT is proposed. Improved Frequency-turned (FT) visual saliency detection is proposed to quickly lock on the objects of interest, such as vehicles and pedestrians, so as to improve the salient features of fusion images. The high- and low-frequency sub-bands of infrared saliency images and visible luminance components can quickly be obtained using fast non-subsampled contourlet transform (FNSCT), which has the characteristics of multi-direction, multi-scale, and shift-invariance. According to the halation degree in the visible image, the nonlinear adaptive fusion strategy of low-frequency weight reasonably eliminates halation while retaining useful information from the original image to the maximum extent. The statistical matching feature fusion strategy distinguishes the common and unique edge information from the high-frequency sub-bands by mutual matching so as to obtain more effective details of the original images such as the edges and contours. Only the luminance Y decomposed by YUV transform is involved in image fusion, which not only avoids color shift of the fusion image but also reduces the amount of computation. Considering the night driving environment and the degree of halation, the visible images and infrared images were collected for anti-halation fusion in six typical halation scenes on three types of roads covering most night driving conditions. The fused images obtained by the proposed algorithm demonstrate complete halation elimination, rich color details, and obvious salient features and have the best comprehensive index in each halation scene. The experimental results and analysis show that the proposed algorithm has advantages in halation elimination and visual saliency and has good universality for different night vision halation scenes, which help drivers to observe the road ahead and improve the safety of night driving. It also has certain applicability to rainy, foggy, smoggy, and other complex weather. [ABSTRACT FROM AUTHOR]

The just noticeable difference (JND) model reflects the visibility limitations of the human visual system (HVS), which plays an important role in perceptual image/video processing and is commonly applied to perceptual redundancy removal. However, existing JND models are usually constructed by treating the color components of three channels equally, and their estimation of the masking effect is inadequate. In this paper, we introduce visual saliency and color sensitivity modulation to improve the JND model. Firstly, we comprehensively combined contrast masking, pattern masking, and edge protection to estimate the masking effect. Then, the visual saliency of HVS was taken into account to adaptively modulate the masking effect. Finally, we built color sensitivity modulation according to the perceptual sensitivities of HVS, to adjust the sub-JND thresholds of Y, Cb, and Cr components. Thus, the color-sensitivity-based JND model (CSJND) was constructed. Extensive experiments and subjective tests were conducted to verify the effectiveness of the CSJND model. We found that consistency between the CSJND model and HVS was better than existing state-of-the-art JND models. [ABSTRACT FROM AUTHOR]

Unconstrained face images are interfered by many factors such as illumination, posture, expression, occlusion, age, accessories and so on, resulting in the randomness of the noise pollution implied in the original samples. In order to improve the sample quality, a weighted block cooperative sparse representation algorithm is proposed based on visual saliency dictionary. First, the algorithm uses the biological visual attention mechanism to quickly and accurately obtain the face salient target and constructs the visual salient dictionary. Then, a block cooperation framework is presented to perform sparse coding for different local structures of human face, and the weighted regular term is introduced in the sparse representation process to enhance the identification of information hidden in the coding coefficients. Finally, by synthesising the sparse representation results of all visual salient block dictionaries, the global coding residual is obtained and the class label is given. The experimental results on four databases, that is, AR, extended Yale B, LFW and PubFig, indicate that the combination of visual saliency dictionary, block cooperative sparse representation and weighted constraint coding can effectively enhance the accuracy of sparse representation of the samples to be tested and improve the performance of unconstrained face recognition. [ABSTRACT FROM AUTHOR]

Research and development of image quality assessment (IQA) algorithms have been in the focus of the computer vision and image processing community for decades. The intent of IQA methods is to estimate the perceptual quality of digital images correlating as high as possible with human judgements. Full-reference image quality assessment algorithms, which have full access to the distortion-free images, usually contain two phases: local image quality estimation and pooling. Previous works have utilized visual saliency in the final pooling stage. In addition to this, visual saliency was utilized as weights in the weighted averaging of local image quality scores, emphasizing image regions that are salient to human observers. In contrast to this common practice, visual saliency is applied in the computation of local image quality in this study, based on the observation that local image quality is determined both by local image degradation and visual saliency simultaneously. Experimental results on KADID-10k, TID2013, TID2008, and CSIQ have shown that the proposed method was able to improve the state-of-the-art’s performance at low computational costs.

Objective quality assessment of retargeted images aims to find the best retargeting method for showing an image on different display terminals. This paper uses a Radial Basis Function (RBF) neural network to assess the quality of retargeted images. First, invariant feature points in the original image and their counterparts in the retargeted image are matched in a spatial order-preserving manner. Feature points centered local patches are extracted from original and retargeted images. Then, multi-scale local structural similarity and multi-scale local HSV color histograms difference of matched local patches are measured. A saliency map is employed as the weights of the local patches for evaluating the structural distortion and content change. Finally, the overall assessment of the retargeted image quality can be obtained by the RBF neural network. Experimental results on a benchmark test show the high consistency between the proposed objective assessment and subjective evaluations, and our method is closer to the practical application due to its simplicity compared with those complex ones. [ABSTRACT FROM AUTHOR]

In naturalistic conditions, objects in the scene may be partly occluded and the visual system has to recognize the whole image based on the little information contained in some visible fragments. Previous studies demonstrated that humans can successfully recognize severely occluded images, but the underlying mechanisms occurring in the early stages of visual processing are still poorly understood. The main objective of this work is to investigate the contribution of local information contained in a few visible fragments to image discrimination in fast vision. It has been already shown that a specific set of features, predicted by a constrained maximumentropy model to be optimal carriers of information (optimal features), are used to build simplified early visual representations (primal sketch) that are sufficient for fast image discrimination. These features are also considered salient by the visual system and can guide visual attention when presented isolated in artificial stimuli. Here, we explore whether these local features also play a significant role in more natural settings, where all existing features are kept, but the overall available information is drastically reduced. Indeed, the task requires discrimination of naturalistic images based on a very brief presentation (25 ms) of a few small visible image fragments. In the main experiment, we reduced the possibility to perform the task based on globalluminance positional cues by presenting randomly inverted-contrast images, and we measured how much observers’ performance relies on the local features contained in the fragments or on global information. The size and the number of fragments were determined in two preliminary experiments. Results show that observers are very skilled in fast image discrimination, even when a drastic occlusion is applied. When observers cannot rely on the position of global-luminance information, the probability of correct discrimination increases when the visible fragments contain a high number of optimal features. These results suggest that such optimal local information contributes to the successful reconstruction of naturalistic images even in challenging conditions. [ABSTRACT FROM AUTHOR]

Simple Summary: Insect pests cause major nutritional and economic losses in stored grains through their pestilential activities, such as feeding, excretion, and reproduction. Therefore, the detection of grain pests and the estimation of their population density are necessary for taking the proper management initiatives in order to control insect infestation. The popular techniques for the detection of grain pests include probe sampling, acoustic detection, and image recognition, among which the image recognition can provide rapid, economic and accurate solutions for the detection of grain pests. With the development of deep learning, convolutional neural networks (CNN) have been extensively used in image classification and object detection. Nevertheless, the pixel-level segmentation of small pests from the cluttered grain background remains a challenging task in the detection and monitoring of grain pests. Inspired by the observation that humans and birds can find the insects in grains with a glance, we propose a saliency detection model to detect the insects in pixels. Firstly, we construct a dedicated dataset, named GrainPest, with small insect objects in realistic storage scenes. Secondly, frequency clues for both the discrete wavelet transformation (DWT) and the discrete cosine transformation (DCT) are leveraged to enhance the performance of salient object segmentation. Moreover, we design a new receptive field block, aggregating multiscale saliency features to improve the segmentation of small insects. As insect infestation is the leading factor accounting for nutritive and economic losses in stored grains, it is important to detect the presence and number of insects for the sake of taking proper control measures. Inspired by the human visual attention mechanism, we propose a U-net-like frequency-enhanced saliency (FESNet) detection model, resulting in the pixelwise segmentation of grain pests. The frequency clues, as well as the spatial information, are leveraged to enhance the detection performance of small insects from the cluttered grain background. Firstly, we collect a dedicated dataset, GrainPest, with pixel-level annotation after analyzing the image attributes of the existing salient object detection datasets. Secondly, we design a FESNet with the discrete wavelet transformation (DWT) and the discrete cosine transformation (DCT), both involved in the traditional convolution layers. As current salient object detection models will reduce the spatial information with pooling operations in the sequence of encoding stages, a special branch of the discrete wavelet transformation (DWT) is connected to the higher stages to capture accurate spatial information for saliency detection. Then, we introduce the discrete cosine transform (DCT) into the backbone bottlenecks to enhance the channel attention with low-frequency information. Moreover, we also propose a new receptive field block (NRFB) to enlarge the receptive fields by aggregating three atrous convolution features. Finally, in the phase of decoding, we use the high-frequency information and aggregated features together to restore the saliency map. Extensive experiments and ablation studies on our dataset, GrainPest, and open dataset, Salient Objects in Clutter (SOC), demonstrate that the proposed model performs favorably against the state-of-the-art model. [ABSTRACT FROM AUTHOR]

Being able to describe any user interface (UI) screenshot in natural language can promote understanding of the main purpose of the UI, yet currently it cannot be accomplished with state-of-the-art captioning systems. We introduce XUI, a novel method inspired by the global precedence effect to create informative descriptions of UIs, starting with an overview and then providing fine-grained descriptions about the most salient elements. XUI builds upon computational models for topic classification, visual saliency prediction, and natural language generation (NLG). XUI provides descriptions with up to three different granularity levels that, together, describe what is in the interface and what the user can do with it. We found that XUI descriptions are highly readable, are perceived to accurately describe the UI, and score similarly to human-generated UI descriptions. XUI is available as open-source software. [ABSTRACT FROM AUTHOR]

Signs, landmarks, and other urban elements should attract attention to or harmonize with the environment for successful landscape design. These elements also provide information during navigation—particularly for people with cognitive difficulties or those unfamiliar with the geographical area. Nevertheless, some urban components are less eye-catching than intended because they are created and positioned irrespective of their surroundings. While quantitative measures such as eye tracking have been introduced, they help the initial or final stage of the urban design process and they involve expensive experiments. We introduce machine-learning-predicted visual saliency as iterative feedback for pedestrian attention during urban element design. Our user study focused on wayfinding signs as part of urban design and revealed that providing saliency prediction promoted a more efficient and helpful design experience without compromising usability. The saliency-guided design practice also contributed to producing more eye-catching and aesthetically pleasing urban elements. The study demonstrated that visual saliency can lead to an improved urban design experience and outcome, resulting in more accessible cities for citizens, visitors, and people with cognitive impairments. [ABSTRACT FROM AUTHOR]

We propose a no-reference image quality assessment (NR-IQA) approach to predict the perceptual quality score of a given image without using any reference image. Our model consists of two steps and trains two similar convolutional neural networks (CNN) progressively. In order to consider the quality of different blocks in the whole picture, the first CNN takes the weighted average of the FR-IQA score of each patch and the differential mean opinion scores of the whole image as target output. The second CNN considers the interaction of the adjacent patches in an image. This paper not only uses visual saliency to address the importance of different patches, but also considers the spatial interaction of adjacent patches using Gaussian function. We compare the prediction results with several up-to-date proposed methods in six databases, and demonstrate the advance of our method. The source code can be found in https://github.com/busigushen/SCVS. [ABSTRACT FROM AUTHOR]

Fabric defect detection plays a critical role for measuring quality control in the textile manufacturing industry. Deep learning-based saliency models can quickly spot the most interesting regions that attract human attention from the complex background, which have been successfully applied in fabric defect detection. However, most of the previous methods mainly adopted multi-level feature aggregation yet ignored the complementary relationship among different features, and thus resulted in poor representation capability for the tiny and slender defects. To remedy these issues, we propose a novel saliency-based fabric defect detection network, which can exploit the complementary information between different layers to enhance the representation features ability and discrimination of defects. Specifically, a multiscale feature aggregation unit (MFAU) is proposed to effectively characterize the multi-scale contextual features. Besides, a feature fusion refinement module (FFR) composed of an attention fusion unit (AFU) and an auxiliary refinement unit (ARU) is designed to exploit complementary important information and further refine the input features for enhancing the discriminative ability of defect features. Finally, a multi-level deep supervision (MDS) is adopted to guide the model to generate more accurate saliency maps. Under different evaluation metrics, our proposed method outperforms most state-of-the-art methods on our developed fabric datasets. [ABSTRACT FROM AUTHOR]

Salient object detection has been long studied to identify the most visually attractive objects in images/videos. Recently, a growing amount of approaches have been proposed, all of which rely on the contour/edge information to improve detection performance. The edge labels are either put into the loss directly or used as extra supervision. The edge and body can also be learned separately and then fused afterward. Both methods either lead to high prediction errors near the edge or cannot be trained in an end-to-end manner. Another problem is that existing methods may fail to detect objects of various sizes due to the lack of efficient and effective feature fusion mechanisms. In this work, we propose to decompose the saliency detection task into two cascaded sub-tasks, i.e., detail modeling and body filling. Specifically, detail modeling focuses on capturing the object edges by supervision of explicitly decomposed detail label that consists of the pixels that are nested on the edge and near the edge. Then the body filling learns the body part that will be filled into the detail map to generate more accurate saliency map. To effectively fuse the features and handle objects at different scales, we have also proposed two novel multi-scale detail attention and body attention blocks for precise detail and body modeling. Experimental results show that our method achieves state-of-the-art performances on six public datasets. [ABSTRACT FROM AUTHOR]

Small target detection under a complex background has always been a hot and difficult problem in the field of image processing. Due to the factors such as a complex background and a low signal-to-noise ratio, the existing methods cannot robustly detect targets submerged in strong clutter and noise. In this paper, a local gradient contrast method (LGCM) is proposed. Firstly, the optimal scale for each pixel is obtained by calculating a multiscale salient map. Then, a subblock-based local gradient measure is designed; it can suppress strong clutter interference and pixel-sized noise simultaneously. Thirdly, the subblock-based local gradient measure and the salient map are utilized to construct the LGCM. Finally, an adaptive threshold is employed to extract the final detection result. Experimental results on six datasets demonstrate that the proposed method can discard clutters and yield superior results compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Visual saliency prediction plays an important role in Unmanned Aerial Vehicle (UAV) video analysis tasks. In this paper, an efficient saliency prediction model for UAV video is proposed based on spatial–temporal features, prior information and the relationship of frames. It can achieve high efficiency by designing a simplified network model. Since UAV videos usually cover a wide range of scenes containing various background disturbances, a cascading architecture module is proposed for feature extraction from coarse to fine, in which a saliency related feature sub-network is utilized to obtain useful clues from each frame, then a new convolution block is designed to capture spatial–temporal features. This structure can achieve advanced performance and high speed based on a 2D CNN framework. Moreover, a multi-stream prior module is proposed to model the bias phenomenon in viewing behavior for UAV video scenes. It can automatically learn prior information based on the video context, and can also combine other priors. Finally, based on the spatial–temporal features and learned priors, a temporal weighted average module is proposed to model the inter-frame relationship and generate the final saliency map, which can make the generated saliency maps look smoother in the temporal dimension. The proposed method is compared with 17 state-of-the-art models on two public UAV video saliency prediction datasets. The experimental results demonstrate that our model outperforms other competitors. Source code is available at: https://github.com/zhangkao/IIP_UAVSal_Saliency. [ABSTRACT FROM AUTHOR]

The complex spatial environment of underground commercial street spaces will affect users' behavior and information needs. As a medium to coordinate the interaction between the underground commercial street space environment and people, guide signs can provide useful information for users. However, the visual saliency of guide signs is the fundamental premise for determining the transmission of information to users. Based on field research, this study identified and examined the factors influencing the significance of guide signs in underground commercial streets from the user's perspective using the order relation analysis method (G1 method) and with the help of screen-based eye tracking and virtual reality (VR) eye-tracking technology, In addition, we explored the design relationship between critical influencing factors and the space between underground commercial streets, and the visual significance of the differences between each important influencing variable. The study showed that the set position, set height, and design color of underground commercial street guide signs are essential factors in their visual prominence. The prominence was more significant when the position of guide signs was located in the middle and upper area of the space, and the prominence was more significant when the set height was 2.56~2.75 m and 3.12~3.31 m. This study can provide data and theoretical support for the visual saliency design of underground commercial street guide signs and provide a reference for the humanized design of underground commercial street guide signs for public facilities in cities. [ABSTRACT FROM AUTHOR]