Spatial Color Research Articles

Salient object detection via joint perception of region-level spatial distribution and color contrast

Nowadays, many RGB-D saliency detection models have been proposed, and they are conducted based on the pixel-level depth data obtained by active depth sensors or dense stereo matching algorithms in advance. However, the pixel-level depth maps may have a negative impact on saliency ranking, especially when they are inconsistent or invalid within the object area. It can be found that people tend to observe an object as a whole and ignore the details of the target surface. Inspired by this characteristic, we propose a salient object detection method via joint perception of region-level spatial distribution and color contrast. First, a twice segmentation strategy based on multifeature fusion is used to compute the region-level information. Then, the region-level spatial distribution maps are constructed instead of pixel-level depth maps, which is helpful for avoiding the interference of dense depth information. To improve the integrity of object detection, color saliency maps are also computed based on regional segmentation information. After that, we adopt a fusion strategy to achieve the effective complementarity of the two kinds of information. Two optimization strategies are employed to further improve the results of saliency ranking. Experimental results on two benchmark datasets demonstrate that the proposed method has better performance than most of the state-of-the-art methods, and it also shows competitive capability compared with deep learning-based methods.

Versatile non-luminescent color palette based on guest exchange dynamics in paramagnetic cavitands

Multicolor luminescent portrayal of complexed arrays is indispensable for many aspects of science and technology. Nevertheless, challenges such as inaccessible readouts from opaque objects, a limited visible-light spectrum and restricted spectral resolution call for alternative approaches for multicolor representation. Here, we present a strategy for spatial COlor Display by Exploiting Host-guest Dynamics (CODE-HD), comprising a paramagnetic cavitand library and various guests. First, a set of lanthanide-cradled α-cyclodextrins (Ln-CDs) is designed to induce pseudo-contact shifts in the 19F-NMR spectrum of Ln-CD-bound guest. Then, capitalizing on reversible host-guest binding dynamics and using magnetization-transfer 19F-MRI, pseudo-colored maps of complexed arrays are acquired and applied in molecular-steganography scenarios, showing CODE-HD’s ability to generate versatile outputs for information encoding. By exploiting the widely shifted resonances induced by Ln-CDs, the guest versatility and supramolecular systems' reversibility, CODE-HD provides a switchable, polychromatic palette, as an advanced strategy for light-free, multicolor-mapping.

Robust moving object detection based on fusing Atanassov's Intuitionistic 3D Fuzzy Histon Roughness Index and texture features

Background modeling is a crucial step in various computer vision applications such as video surveillance, object tracking, and moving object detection. Classifying image pixels as foreground or background is yet a challenging task particularly in complicated situations such as illumination variations, rippling water, camera jitter, and the presence of fast and slow moving objects. Therefore, for a better detection of the moving objects, the multi-modal nature of a scene in those intricate situations should be modeled by multiple models for each image pixel. To this end, in this article, we improve our previous work by fusing color features and texture features using Choquet fuzzy integral. Thereby, our proposed spatial color features that are described by Atanassov's Intuitionistic 3D Fuzzy Histon Roughness Index are fused by the texture features extracted using a covariance matrix. As handling multi-modal background updating is an arduous task, we also propose a new model updating for tackling various challenges such as model initializing with moving objects, existence of fast and slow moving objects in a scene, and existence of the moving objects that stop for a while. We intensively evaluate our proposed approach on diverse benchmark datasets. Experimental results demonstrate the robustness and supremacy of our proposed approach compared to its previous version and the state-of-the-art algorithms in the field.

The Microstructure of Attentional Control in the Dorsal Attention Network.

The top-down control of attention involves command signals arising chiefly in the dorsal attention network (DAN) in frontal and parietal cortex and propagating to sensory cortex to enable the selective processing of incoming stimuli based on their behavioral relevance. Consistent with this view, the DAN is active during preparatory (anticipatory) attention for relevant events and objects, which, in vision, may be defined by different stimulus attributes including their spatial location, color, motion, or form. How this network is organized to support different forms of preparatory attention to different stimulus attributes remains unclear. We propose that, within the DAN, there exist functional microstructures (patterns of activity) specific for controlling attention based on the specific information to be attended. To test this, we contrasted preparatory attention to stimulus location (spatial attention) and to stimulus color (feature attention), and used multivoxel pattern analysis to characterize the corresponding patterns of activity within the DAN. We observed different multivoxel patterns of BOLD activation within the DAN for the control of spatial attention (attending left vs. right) and feature attention (attending red vs. green). These patterns of activity for spatial and feature attentional control showed limited overlap with each other within the DAN. Our findings thus support a model in which the DAN has different functional microstructures for distinctive forms of top-down control of visual attention.

Remote Sensing-Based Analysis of Spatial and Temporal Water Colour Variations in Baiyangdian Lake after the Establishment of the Xiong’an New Area

The Forel-Ule Index (FUI) is an important parameter that can be calculated from optical remote sensing data to assess water quality based on water colour. Using Sentinel-2 images from April to November within the 2016–2020 period coupled with the Google Earth Engine Platform, we calculated FUI to analyse the spatial distribution, seasonal variations, and inter-annual variations of water colour in Baiyangdian Lake in the Xiong’an New Area established on 1 April 2017. The lake was divided into seven sub-regions, A–G; subsequently, high and low FUI values were observed in the south and north, respectively. Additionally, the mean FUI values of G and F zones in the south were 11.9 and 12.7, respectively, whereas those for the A, B, C, D, and E zones in the north were 10.5, 9.8, 10.4, 11.1, 11.2, respectively. The seasonal variations in the Baiyangdian Lake and seven sub-regions were consistent, with turbid water in spring and autumn, and clear water in summer. Inter-annual variations analyses for 2016–2020 indicated that the zone of A became progressively turbid, whereas the B, C, D, E, F, and G zones exhibited slow and gradually decreasing trends. Our findings suggest that the overall water quality of Baiyangdian Lake may be better, which may be related to the governance policies of the region.

The Study on Spatial Color Model of Preschool Based on Visual Perception Theory - To Reduce Preschoolers' Lack of Concentration

The Study on Spatial Color Model of Preschool Based on Visual Perception Theory - To Reduce Preschoolers' Lack of Concentration

Self-Supervised Video Representation Learning by Uncovering Spatio-Temporal Statistics.

This paper proposes a novel pretext task to address the self-supervised video representation learning problem. Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and dominant color of the largest color diversity along the temporal axis, etc. Then a neural network is built and trained to yield the statistical summaries given the video frames as inputs. In order to alleviate the learning difficulty, we employ several spatial partitioning patterns to encode rough spatial locations instead of exact spatial Cartesian coordinates. Our approach is inspired by the observation that human visual system is sensitive to rapidly changing contents in the visual field, and only needs impressions about rough spatial locations to understand the visual contents. To validate the effectiveness of the proposed approach, we conduct extensive experiments with four 3D backbone networks, i.e., C3D, 3D-ResNet, R(2+1)D and S3D-G. The results show that our approach outperforms the existing approaches across these backbone networks on four downstream video analysis tasks including action recognition, video retrieval, dynamic scene recognition, and action similarity labeling. The source code is publicly available at: https://github.com/laura-wang/video_repres_sts.

Illumination-Invariant Flotation Froth Color Measuring via Wasserstein Distance-Based CycleGAN With Structure-Preserving Constraint.

Froth color can be referred to as a direct and instant indicator to the key flotation production index, for example, concentrate grade. However, it is intractable to measure the froth color robustly due to the adverse interference of time-varying and uncontrollable multisource illuminations in the flotation process monitoring. In this article, we proposed an illumination-invariant froth color measuring method by solving a structure-preserved image-to-image color translation task via an introduced Wasserstein distance-based structure-preserving CycleGAN, called WDSPCGAN. WDSPCGAN is comprised of two generative adversarial networks (GANs), which have their own discriminators but share two generators, using an improved U-net-like full convolution network to conduct the spatial structure-preserved color translation. By an adversarial game training of the two GANs, WDSPCGAN can map the color domain of froth images under any illumination to that of the referencing illumination, while maintaining the structure and texture invariance. The proposed method is validated on two public benchmark color constancy datasets and applied to an industrial bauxite flotation process. The experimental results show that WDSPCGAN can achieve illumination-invariant color features of froth images under various unknown lighting conditions while keeping their structures and textures unchanged. In addition, WDSPCGAN can be updated online to ensure its adaptability to any operational conditions. Hence, it has the potential for being popularized to the online monitoring of the flotation concentrate grade.

Spatial Color Analysis of Kindergarten Interior Design Based on Children's Psychological Activities in Digital Environment

In the digital environment, the kindergarten interior design should also be adjusted, and more attention should be paid to the spatial color matching. The progress of science and technology makes the comfort of children's living environment also increase. Designers should be aware of the importance of spatial color to children's physical and mental growth when designing the kindergarten interior. Therefore, this paper first analyzes the present situation of kindergarten interior color design in China under the digital environment from the perspective of children's color psychology, then puts forward the basic ideas of kindergarten interior color design according to space color psychology, and finally discusses the design methods of kindergarten interior color according to children's psychological activities, hoping to provide good living, entertainment and learning space for children's healthy development.

Symmetric Image Contents Analysis and Retrieval Using Decimation, Pattern Analysis, Orientation, and Features Fusion

Image retrieval procedures locate interest points and consider the features as the visual property of an image in computer vision. These primitive features define visual attributes as local or global features for content based image retrieval. The visual attributes of an image, include spatial information, shape, texture, object and color, describe the image category. The present research contributes feature detector by performing non-max suppression after detecting edges and corners based on corner score and pixel derivation-based shapes on intensity-based interest points. Thereafter, interest point description applied on interest point features set by applying symmetric sampling to cascade matching produced by validating dense distributed receptive fields after estimating perifoveal receptive fields. Spatial color-based features vector are fused with retinal and color-based feature vector extracted after applying L2 normalization on spatially arranged color image. Dimensions are reduced by applying PCA on massive feature vectors produced after symmetric sampling and transmitted to bag-of-word in fused form for indexing and retrieval of images. Extensive experiments are performed on well-known benchmarks corel-1000, core-10000, caltech-101, image net, alot, coil, ftvl, 102-flowers and 17-flowers. In order to measure the competitiveness, we designed a comparison of proposed method with seven descriptors and detectors RGBLBP, LBP, surf, sift, DoG, HoG and MSER. The proposed method reports remarkable AP, AR, ARP, ARR, P&R, mAP and mAR rates in many categories of image datasets.

Research on the Teaching Mode of Environmental Art Course with Spatial Thinking Creativity as the Core

In order to realize the cultivation of innovative thinking for modern talents, take the environmental art course as an example to carry out the study of the curriculum teaching mode centered on the creativity of spatial thinking. Analyze the teaching status of the environmental art course and the teaching requirements of the environmental art course, put forward the teaching reform measures to adjust the syllabus of environmental art with spatial thinking creativity as the core, and follow the sequence of "spatial perception-spatial affairs layout analysis-artistic space creation", In the syllabus, the knowledge related to the cultivation of spatial thinking is gradually integrated, and multiple teaching links with innovative expression capabilities or innovative themes are connected to form an organic and dynamic teaching system that can realize the cycle of knowledge points. In order to realize the cultivation of students' spatial thinking, the teaching content of environmental art courses can be innovated by integrating spatial colors to achieve the teaching purpose of reforming the original education mode and cultivating students' spatial thinking.

Image Sensor Module for Detecting Spatial Color Temperature in Indoor Environment

Image Sensor Module for Detecting Spatial Color Temperature in Indoor Environment

Fusion of region based extracted features for instance- and class-based CBIR applications

One of the major challenges in content-based image retrieval (CBIR) is understanding the actual semantic meaning of images. Generally, natural images possess a rich collection of different visual features, each of which have different contributions to the image semantics. These vary drastically in their local sub-regions. However, most of the state-of-the-art CBIR techniques give equal importance to all the visual features, which are not always effective for generating relevant retrieval outcomes. To overcome these issues, a CBIR scheme has been proposed which considers the image semantic properties of the different regions with their positional significance in the overall image. In this paper, a novel regions-of-attention (ROA) based feature extraction and fusion scheme for efficient image retrieval is suggested. Initially, a salient key-points and opponent-color feature based scheme is used to locate ROA of the image. Multi-directional texture, and spatial correlation-based color features are extracted from this ROA of the image to obtain most of the image’s semantic meanings. Spatial features from salient non-ROA local regions are also obtained to capture vital background information. A dissimilarity factor-based fusion scheme then is utilized to fuse the ROA and non-ROA features effectively, considering the importance and uniqueness of each feature to support the positional invariance notion in their respective sub-regions. Finally, the fused feature vector is used to perform instance- and class-based image retrieval in five different image datasets using different classifiers. The outcomes of the retrieval experiment have shown competitive performances as compared to the current state-of-the-art architectures.

StainedView: Variable-Intensity Light-Attenuation Display with Cascaded Spatial Color Filtering for Improved Color Fidelity.

We present StainedView, an optical see-through display that spatially filters the spectral distribution of light to form an image with improved color fidelity. Existing light-attenuation displays have limited color fidelity and contrast, resulting in a degraded appearance of virtual images. To use these displays to present virtual images that are more consistent with the real world, we require three things: intensity modulation of incoming light, spatial color filtering with narrower bandwidth, and appropriate light modulation for incoming light with an arbitrary spectral distribution. In StainedView, we address the three requirements by cascading two phase-only spatial light modulators (PSLMs), a digital micromirror device, and polarization optics to control both light intensity and spectrum distribution. We show that our design has a 1.8 times wider color gamut fidelity (75.8% fulfillment of sRGB color space) compared to the existing single-PSLM approach (41.4%) under a reference white light. We demonstrated the design with a proof-of-concept display system. We further introduce our optics design and pixel-selection algorithm for the given light input, evaluate the spatial color filter, and discuss the limitation of the current prototype.

A way to Calibrate a Colour Texture Feature

Several colour descriptors are presented each year. The existing protocols to evaluate and compare these descriptors are restricted to the use of image databases without information about the spatio-chromatic content. In this article, we present a first answer to calibrate a colour texture descriptor. By calibration, we intend evaluate the capacity of the descriptor to discriminate the non-uniform aspect according to different scales of samples. In order to assess all the possibilities in term of spatial frequencies and colour content, we propose to use reference images based on a fractal vector colour model. Three texture features are compared from this protocol allowing to express the interest of the proposed calibration sequence.

Pre-processing framework with virtual mono-layer sequence of boxes for video based vehicle detection applications

The increase in day to day vehicular traffic demands an efficient, automated and innovative approach for its management and regulation. One of the most practical and commonly used Intelligent Transportation Systems (ITS) based solution in recent times is video based traffic monitoring. The primary task of such systems is vehicle detection and the existing methods are not robust enough to handle diverse backgrounds and illumination changes in the traffic video. In order to address this issue, a Pre-processing Framework with Virtual Mono-Layered Sequence of Boxes (PF-VMSB) is proposed to make any vehicle detection process robust and efficient. The two composite modules of the proposed system include Effective Element Estimation (E3) module and Dynamic Multilevel Parallel Video Image Processing (D-MPVIP) module. The E3 module enhances the illumination of the traffic video only when necessary to produce an ideal environment for detection. Further, it classifies the type of background in video content (Static, Moderate or Dynamic) to provide an option to choose the appropriate image processing algorithm suitable for vehicle detection. In D-MPVIP module, the redundant video content are eliminated from processing to reduce the computational cost and processing time. Additionally, the spatial color information of the traffic video content is preserved and processed in parallel.The efficiency of the proposed framework of VMSB with E3 and D-MPVIP were analysed using benchmark datasets such as DETRAC, Urban Trackr, Ko-per and self recorded videos. The results shows an overall accuracy of detection rate using conventional image processing techniques such as Background Subtraction (BS) increases by 34.6% and the processing time is reduced by 37.5%. By incorporating the proposed framework into any vehicle detection process, the processing time and computational cost are improved without compromising the detection accuracy. The resultant of the detection process can be utilized by ITS application to enumerate traffic parameters such as vehicle volume count, congestion estimation, speed monitoring, travel time prediction, etc.

“Design-Led Space” Series Show Flat Design - Taking Dongjin Century City in Xiangyang as An Example

With the acceleration of our country’s urbanization process and the rapid development of the social economy, the urban living thought, the sense of high quality in everyday life and the upgrade of the entire social environment have contributed to the rapid rise of the real estate market, and different types of residential communities and commercial housing continue to emerge. As show flat design is the most important marketing method and marketing hot spot in the real estate market, a show flat design case with great design sense and artistry can often attract consumers’ attention at the first time, effectively transfer spatial information of the real estate, show the functional advantages of the house, and give future residents a sense of identity and confidence in the project. This article took three types of show flats in different design styles in the Dongjin Century City project in Xiangyang as an example to deeply analyze the spatial function analysis, color, soft decoration, light source and other factors that should be paid attention to during the design of the show flat, summarized the design-led and people-oriented principles, and created a show flat design in line with practical and functional factors of residence, to achieve the ultimate artistry, exhibition and marketing of show flats.

Spatially Responsive Multicolor Lanthanide-MOF Heterostructures for Covert Photonic Barcodes.

Micro/nanoscale photonic barcodes based on multicolor luminescent segmented heterojunctions hold potential for applications in information security. However, such multicolor heterojunctions reported thus far are exclusively based on static luminescent signals, thus restricting their application in advanced confidential information protection. Reported here is a strategy to design responsive photonic barcodes with heterobimetallic (Tb3+ /Eu3+ ) metal-organic framework multicolor heterostructures. The spatial colors could be precisely controlled by thermally manipulating the energy-transfer process between the two lanthanides, thus achieving responsive covert photonic barcodes. Also demonstrated is that spatially resolved responsive barcodes with multi-responsive features could be created in a single heterostructure. These findings offer unique opportunities to purposely design highly integrated responsive microstructures and smart devices toward advanced anti-counterfeiting applications.

Spatially Responsive Multicolor Lanthanide‐MOF Heterostructures for Covert Photonic Barcodes

AbstractMicro/nanoscale photonic barcodes based on multicolor luminescent segmented heterojunctions hold potential for applications in information security. However, such multicolor heterojunctions reported thus far are exclusively based on static luminescent signals, thus restricting their application in advanced confidential information protection. Reported here is a strategy to design responsive photonic barcodes with heterobimetallic (Tb3+/Eu3+) metal—organic framework multicolor heterostructures. The spatial colors could be precisely controlled by thermally manipulating the energy‐transfer process between the two lanthanides, thus achieving responsive covert photonic barcodes. Also demonstrated is that spatially resolved responsive barcodes with multi‐responsive features could be created in a single heterostructure. These findings offer unique opportunities to purposely design highly integrated responsive microstructures and smart devices toward advanced anti‐counterfeiting applications.

Unsupervised Super-Resolution of OCT Images Using Generative Adversarial Network for Improved Age-Related Macular Degeneration Diagnosis

Age-related macular degeneration (AMD) is the leading cause of progressive vision loss in the elderly. Optical coherence tomography (OCT) is a promising diagnostic tool for early detection and management of AMD. However, the speckle noise and low resolution (LR) of the OCT images affect its diagnostic viabilities. Therefore, denoising and super-resolution (SR) techniques present a potential solution to improve the quality of the OCT images. Recent methods rely on example-based approaches that require paired LR and high resolution (HR) images for training. However, the large scale acquisition of paired LR-HR images presents pertinent challenges in clinical settings. Therefore, this work proposes an unsupervised framework using the generative adversarial network (GAN) to perform fast and reliable SR without the requirement of aligned LR-HR pairs. We use adversarial learning with cycle consistency and identity mapping priors to preserve the spatial correlation, color and texture details in the generated clean HR images. Experimental results on clinical-grade OCT images show that the proposed method outperforms the existing methods both in terms of SR performance and computational time. Improved classification accuracy of 96.54% is obtained when the generated images are used for automated AMD diagnosis. The enhanced generalizability and faithful reconstruction attributes make the proposed method suitable for assisting ophthalmologists in better diagnosis and treatment planning.