Target Color Research Articles

Loss aversion in the control of attention.

Loss aversion is a psychological bias where an increase in loss is perceived as being larger than an equivalent increase in gain. In the present study, two experiments were conducted to explore whether attentional control reflects loss aversion. Participants performed a visual search task. On each trial, a red target and a green target were presented simultaneously, and participants were free to search for either one. Participants always gained points when they searched for a gain color target (e.g., red). However, they gained or lost points when they searched for a gain-loss color target (e.g., green). In Experiment 1, the expected values of the gain color and the gain-loss color were equal. Therefore, for maximizing the reward, participants did not need to preferably search for a particular color. However, results showed that participants searched for the gain color target more than the gain-loss color target, suggesting stronger attentional control for the gain color than the gain-loss color. In Experiment 2, even though the expected value of the gain-loss color was greater than that of the gain color, attention was allocated to the gain color more than to the gain-loss color. The results imply that attentional control can operate in accordance with the loss aversion principle when the boundary conditions for loss aversion in a repeated binary decision-making task were met.

Selection history contributes to suboptimal attention strategies.

Attentional control balances the competing drives of performance maximization and effort minimization. One way the attention system minimizes effort is through a bias to persist in the use of attentional control strategies that have been useful in the past. In the present study, we asked whether such selection history can result in the persistence of an attentional control strategy that is counterproductive, effectively competing with a more optimal strategy. Participants first completed a training in which one color target was encountered more frequently than another, and then completed a test phase in which they could search for one of two targets on any given trial, one of which would be more optimal to search for given the distribution of color stimuli. An attentional bias for the more frequent target color was observed in the training phase and the choice of which target to report was robustly optimal in the test phase, reflecting performance maximization. Importantly, participants also exhibited a tendency to report the target rendered in the previously more frequent target color in the test phase, even when the distribution of non-target colors made it suboptimal to do so. Our findings shed light on the fundamental question of why attentional control is sometimes suboptimal, demonstrating a role for selection history in the perseveration of previously employed attentional strategies even when such strategies produce suboptimal performance.

On the role of top-down and bottom-up guidance in conjunction search: Singleton interference revisited

The current study reassessed the potential of salient singleton distractors to interfere in conjunction search. Experiment 1 investigated conjunctions of colour and orientation, using densely packed arrays that produced highly efficient search. The results demonstrated clear interference effects of singleton distractors in task-relevant dimensions colour and orientation, but no interference from those in a task-irrelevant dimension (motion). Goals exerted an influence in constraining this interference such that the singleton interference along one dimension was modulated by target relevance along the other task relevant dimension. Colour singleton interference was much stronger when the singleton shared the target orientation, and orientation interference was much stronger when the orientation singleton shared the target colour. Experiments 2 and 3 examined singleton-distractor interference in feature search. The results showed strong interference particularly from task-relevant dimensions but a reduced role for top-down, feature-based modulation of singleton interference, compared with conjunction search. The results are consistent with a model of conjunction search based on core elements of the guided search and dimension weighting approaches, whereby weighted dimensional feature contrast signals are combined with top-down feature guidance signals in a feature-independent map that serves to guide search.

Evaluating the accuracy and quality of an iPad Pro's built-in lidar for 3D indoor mapping

This study aimed to assess the accuracy of the lidar sensor on an iPad Pro and its potential for creating 3D BIM. Data was collected through different scanning methods in various environmental conditions. The results showed that the iPad Pro's lidar sensor produced accurate point clouds in static acquisition experiments with point distances of less than 1 mm from their best-fitting plane. However, in dynamic acquisition experiments, the distance increased to an average of 1 cm due to changes in the iPad Pro's pose. The study also found that the accuracy of the registered point clouds was better when scanning smaller areas with limited pose changes. Based on the findings, the study recommends maintaining a distance of 1–1.5 m between the iPad Pro and the target, as the target's color has minimal impact on scanning accuracy. Avoiding large posture changes while scanning can improve the accuracy of the point clouds in scan-to-BIM projects.

P‐13.10: A New Color Space Model for AMOLED Display Based on IR Drop

In this paper, we present a new color space model for AMOLED display based on IR drop. This paper discusses the relationship between the color space and the IR drop of AMOLED display, then derives the corresponding color space model. Based on this model, the level of IR Drop can be adaptively adjusted to change the color space of the panel, to accurately display the brightness and chroma of target. In the experimental results, it is confirmed that the native color space of the panel can be auto tuned to less than 0.5 on average in CIEDE2000 for different target color spaces in different applications. In addition, a corresponding fast calibration method within 15 seconds has been developed to match with our driver chips, to be applied to individual panel correction and calibration requirements in a production line.

AVFP-MVX: Multimodal VoxelNet With Attention Mechanism and Voxel Feature Pyramid

In the current 3-D object detection tasks, most algorithms are based on pure point cloud. Although LiDAR can provide target location information and contour information for detection, it is sparse, especially for long-distance objects. Besides, camera sensors can provide more detailed target color, texture information, and so on. However, if both point cloud and image data are used for object detection at the same time, the problem of large model capacity and overfitting will occur. Different modes will also produce different gradients for different subnetworks, and the entire network will be difficult to optimize. In order to solve these problems and continuously improve the performance of detection algorithms, this article designs a 3-D object detection method attention mechanism and voxel feature pyramid multimodal VoxelNet (AVFP-MVX), which uses both point cloud and image data to solve the above problems. By referring to MVX-Net, attention mechanism and voxel feature pyramid are used to improve the detection accuracy of 3-D objects. The visualization results show that the overall performance of AVFP-MVX does well, which can accurately select the target object and return to a good bounding box. Comparative tests show that the proposed method detected 91.24%, 80.45%, and 76.91% for the easy, mod, and hard targets of car, respectively, while the average accuracy of pedestrian and cyclist is 62.44% and 67.64%, respectively, which is better than the other methods. The results of ablation experiments show that when the attention mechanism and the voxel feature pyramid network (Voxel-FPN) were added, the detection accuracy of car, pedestrian, and cyclist was increased by 1.87%, 1.85%, and 1.88%, respectively.

Self-Supervised Pre-Training Joint Framework: Assisting Lightweight Detection Network for Underwater Object Detection

In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color distortion and target feature blurring, which greatly affect the detection accuracy of underwater object detection. Although deep learning-based algorithms have achieved state-of-the-art results in the field of object detection, most of them cannot be applied to practice because of the limited computing capacity of a low-power processor embedded in unmanned underwater vehicles. This paper proposes a lightweight underwater object detection network based on the YOLOX model called LUO-YOLOX. A novel weighted ghost-CSPDarknet and simplified PANet were used in LUO-YOLOX to reduce the parameters of the whole model. Moreover, aiming to solve the problems of color distortion and unclear features of targets in underwater images, this paper proposes an efficient self-supervised pre-training joint framework based on underwater auto-encoder transformation (UAET). After the end-to-end pre-training process with the self-supervised pre-training joint framework, the backbone of the object detection network can extract more essential and robust features from degradation images when retrained on underwater datasets. Numerous experiments on the URPC2021 and detecting underwater objects (DUO) datasets verify the performance of our proposed method. Our work can assist unmanned underwater vehicles to perform underwater object detection tasks more accurately.

What is behind partial repetition costs? Event-files do not fully occupy bound feature codes.

Feature binding accounts state that features of perceived and produced events are bound into event-files. Performance while responding to an event is impaired when some, as opposed to all or none, of this event's features already belong to a previous event-file. While these partial repetition costs are generally considered to be indicators of feature binding, their cause is still unclear. Possibly, features are fully occupied when bound in an event-file and must be unbound in a time-consuming process before they can enter a novel event-file. In this study, we tested this code occupation account. Participants responded to the font color (target) of a word (distractor) by pressing one of three keys (response) while ignoring the word meaning. We measured partial repetition costs from prime to probe while introducing an intermediate trial. We compared sequences in which this intermediate trial did not repeat any prime features and sequences in which it repeated either the prime response or distractor. Partial repetition costs occurred in the probe, even when one (vs. none) of the prime features repeated in the intermediate trial, although significantly reduced. Thus, single bindings do not fully occupy feature codes. By ruling out a possible mechanism behind partial repetition costs, the present study contributes to the further specification of feature binding accounts.

Effect of colour-shape associations on visual feature discrimination.

Non-synesthetes exhibit a tendency to associate specific shapes with particular colours (i.e., circle-red, triangle-yellow, and square-blue). Here, we used two Go/No-go tasks to examine the congruency priming effect of colour-shape associations on recognition efficiency of colour and shape features. At the beginning of each trial, a target colour or shape word was introduced, followed by a coloured-shape visual stimulus. Participants were required to press a key to a target stimulus ("go" cues), while withholding their responses to a non-target stimulus ("no-go" cues). The targets were presented either visually (visual word, Experiment 1) or auditorily (spoken word, Experiment 2). Results showed a congruency effect of colour-shape associations on recognition efficiency for colour and shape features in both experiments. Response times were shorter in congruent than in incongruent conditions, that a target could be recognised faster when it was presented with the congruent visual features than with incongruent ones, irrespective of the presentation form (visual or auditory). These results suggest that colour-shape associations can be strong to influence visual recognition of colour and shape features.

Spectral missing color correction based on an adaptive parameter fitting model.

With the development of remote sensing technology, true-color visualization of hyperspectral LiDAR echo signals has become a hotspot for both academic research and commercial applications. The limitation of the emission power of hyperspectral LiDAR causes the loss of spectral-reflectance information in some channels of the hyperspectral LiDAR echo signal. The color reconstructed based on the hyperspectral LiDAR echo signal is bound to have serious color cast problem. To solve the existing problem, a spectral missing color correction approach based on adaptive parameter fitting model is proposed in this study. Given the known missing spectral-reflectance band intervals, the colors in incomplete spectral integration are corrected to accurately restore target colors. Based on the experimental results, the color difference between color blocks and the hyperspectral image corrected by the proposed color correction model is smaller than that of the ground truth, and the image quality is higher, realizing the accurate reproduction of the target color.

Perceptual Contiguity Does Not Modulate Matched-Case Identity-Priming Effects in Lexical Decision.

In recent studies with the masked priming lexical decision task, matched-case identity-priming effects occur for nonwords but not for words (e.g., nonwords: ERTAR-ERTAR faster than ertar-ERTAR; words: ALTAR-ALTAR produces similar response times as altar-ALTAR). This dissociation is thought to result from lexical feedback influencing orthographic representations in word processing. As nonwords do not receive this feedback, bottom-up processing of prime-target integration leads to matched-case effects. However, the underlying mechanism of this effect in nonwords remains unclear. In this study, we added a color congruency manipulation across the prime and target in the matched-case identity-priming design. We aimed to determine whether the case effects originate at the early stages of prime-target perceptual integration or due to bottom-up activation of case-specific letter detectors. Results replicated the previous dissociation between words and nonwords regarding the matched-case identity effect. Additionally, we did not find any modulation of these effects by prime-target color congruency. These findings suggest that the locus of the matched-case identity effect is at an orthographic level of representation that encodes case information.

The modulation of value-driven attentional capture by exploration for reward information.

Previous studies on value-driven attentional capture (VDAC) have demonstrated that the uncertainty of reward value modulates attentional allocation via associative learning. However, it is unclear whether such attentional exploration is executed based on the amount of potential reward information available for refining value prediction or the absolute size of reward prediction error. The present study investigated the effects of reward information (information entropy) and prediction error (variance) on attentional bias while controlling for the influence of the strength of reward association. Participants were instructed to search for either a red or green target circle and respond to the line orientation within the target. Each target color was associated with reward contingencies with different levels of uncertainty. In Experiment 1, one color was paired with a single reward value (zero entropy and variance) and the other with multiple reward values (high entropy and variance). In Experiment 2, one color had a high-entropy, low-variance reward contingency and the other had the inverse. Attentional interference for distractors with high entropy was consistently greater than low or zero entropy distractors. In addition, in Experiment 3, when distractors with an identical level of variance were given, information entropy was observed to modulate the attentional bias toward distractors. Lastly, Experiment 4 revealed that distractors associated with contrasting levels of variance, while information entropy was kept identical, failed to modulate VDAC. These results indicate that value-based attention is primarily allocated to cues that provide maximal information about the reward outcomes and that information entropy is one of the key predictors mediating attentional exploration and associative learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Top-down knowledge surpasses selection history in influencing attentional guidance

Visual attention is influenced by the characteristics of the stimuli (bottom-up), their task relevance (top-down), and prior experience (e.g., selection history and learning). However, it is largely unclear how learning and selection history interact with top-down attentional guidance. We combined trial-and-error learning with a spatial cueing protocol to test whether previously learned target-defining features continued to capture attention if participants were instructed to search for a new target feature (Experiment 1) or had to learn a new target feature (Experiment 2). It turned out that the previously learned feature quickly stopped capturing attention when the target feature changed (Experiment 1; even before participants learned the new target-defining feature, in Experiment 2). Finally, in Experiment 3, in which participants learned to search for targets defined by two redundant features (color and orientation), we found possible reasons for the dominance of the instructed feature over learning. Participants reported using only the target color for their search. Consequently, only cues with a target color captured attention. The unused target orientation only captured attention in participants aware of both target-defining features (13 out of 23) and only if the orientation was presented in the target color. We conclude that knowledge of target-defining features and their use as search criterion is critical for attentional guidance, while previously learned target features either influence attentional guidance only contingent on such deliberately selected top-down based attentional control settings or may influence visual search but not attentional guidance.

Preliminary Identification of Mixtures of Pigments Using the paletteR Package in R—The Case of Six Paintings by Andreina Rosa (1924–2019) from the International Gallery of Modern Art Ca’ Pesaro, Venice

Frequently, the study of modern and contemporary paintings requires the taking of micro-samples to gain an in-depth understanding of the employed materials and techniques. However, since this procedure is characterized by its invasive nature, it must be carried out only if strictly necessary. This study aimed to evaluate the potentiality of K-means clustering to the corrected images of paintings to identify mixtures of pigments. This could assist in obtaining relevant preliminary information, facilitate the research process, and guide the sampling collection. Additionally, this method would be less expensive than the traditional multi-analytical approach as it would only require a modified digital camera, lenses, a color target and three computational resources for the processing of data (Imatest Master, Adobe Express—online, and R), out of which the latter two are freely available. The six paintings that have been selected for this study belong to the International Gallery of Modern Art Ca’ Pesaro in Venice (Italy) and have been depicted by Andreina Rosa (1924–2019), a Venetian artist. The artworks were thoroughly investigated mainly through non-invasive analytical techniques (FORS, RAMAN, ER-FTIR, EDXRF). Using cluster analysis, simulating mixtures, and calculating the color differences, it was possible to infer the existence of color mixtures of two/three detected primary colors from the examined images, which could be validated by the analytical results. Hence, it was concluded that samples taken from mixtures might suffice, since primary colors would be concomitantly analyzed.

The persistence of value-driven attention capture is task-dependent

Visual features previously associated with reward can capture attention even when task-irrelevant, a phenomenon known as value-driven attention capture (VDAC). VDAC persists without reinforcement, unlike other forms of learning, where removing reinforcement typically leads to extinction. In five experiments, factors common to many studies were manipulated to examine their impact on VDAC and its extinction. All experiments included learning and test phases. During learning, participants completed a visual search task during which one of two target colors was associated with a reward, and the other with no reward. During test, 1 week later, participants completed another visual search task in which the reward association was not reinforced. When a rewarded feature remained task-relevant (Experiment 1), VDAC was observed. When the rewarded feature was made task-irrelevant (Experiments 2–5) there was no evidence of a VDAC effect, except when the target feature was physically salient and there was a reduction in the frequency of exposure to the reward-associated feature (Experiment 5). We failed to find evidence of VDAC in Experiments 2–4, suggesting that VDAC may depend on the demands of the task resulting in vulnerability to VDAC. When VDAC was observed, extinction was also observed. This indicates that VDAC is subject to extinction as would be expected from an effect driven by reinforcement learning.

Using deep learning approaches for coloring silicone maxillofacial prostheses: A comparison of two approaches.

This study aimed to compare the performance of two deep learning algorithms, attention-based gated recurrent unit (GRU), and the artificial neural networks (ANNs) algorithm for coloring silicone maxillofacial prostheses. This was an in vitro study. A total of 21 silicone samples in different colors were produced with four pigments (white, yellow, red, and blue). The color of the samples was measured with a spectrophotometer, then the LFNx01, aFNx01, and bFNx01 values were recorded. The relationship between the LFNx01, aFNx01, and bFNx01 values of each sample and the amount of each pigment in the compound of the same sample was used as the training dataset, entered into each algorithm, and the prediction models were obtained. While generating the prediction model for each sample, the data of the corresponding sample assigned as the target color were excluded. LFNx01, aFNx01, and bFNx01 values of each target sample were entered into the obtained models separately, and recipes indicating the ratios for mixing the four pigments were predicted. The mean absolute error (MAE) and root mean square error (RMSE) values between the original recipe used in the production of each silicone and the recipe created by both prediction models for the same silicone were calculated. Data were analyzed with the Student t-test (α=0.05). The mean RMSE values and MAE values for the ANN algorithm (0.029 ± 0.0152 and 0.045 ± 0.0235, respectively) were found significantly higher than the attention-based GRU model (0.001 ± 0.0005 and 0.002 ± 0.0008, respectively) (P < 0.001). Attention-based GRU model provided better performance than the ANN algorithm with respect to the MAE and RMSE values.

Guided Colorization Using Mono-Color Image Pairs.

Compared to color images captured by conventional RGB cameras, monochrome (mono) images usually have higher signal-to-noise ratios (SNR) and richer textures due to the lack of color filter arrays in mono cameras. Therefore, using a mono-color stereo dual-camera system, we can integrate the lightness information of target monochrome images with the color information of guidance RGB images to accomplish image enhancement in a colorization manner. In this work, based on two assumptions, we introduce a novel probabilistic-concept guided colorization framework. First, adjacent contents with similar luminance are likely to have similar colors. By lightness matching, we can utilize colors of the matched pixels to estimate the target color value. Second, by matching multiple pixels from the guidance image, if more of these matched pixels have similar luminance values to the target one, we can estimate colors with more confidence. Based on the statistical distribution of multiple matching results, we retain the reliable color estimates as initial dense scribbles and then propagate them to the rest of the mono image. However, for a target pixel, the color information provided by its matching results is quite redundant. Hence, we introduce a patch sampling strategy to accelerate the colorization process. Based on the analysis of the posteriori probability distribution of the sampling results, we can use much fewer matches for color estimation and reliability assessment. To alleviate incorrect color propagation in the sparsely scribbled regions, we generate extra color seeds according to the existed scribbles to guide the propagation process. Experimental results show that, our algorithm can efficiently and effectively restore color images with higher SNR and richer details from the mono-color image pairs, and achieves good performance in solving the color bleeding problem.

LIGHT EMISSION PHENOMENON AND GLARE IN VISUAL ENVIRONMENT DESIGN

Focusing on the light emission phenomenon and glare feeling, we conducted an experiment using HMD, where subjects were asked to evaluate appearance of a target color chip against background N3, covering all visual field of subjects, illuminated uniformly.

Applications of various natural pigments to a plant-based meat analog

To imitate the color of meat after cooking, natural pigments (red beet, monascus red, oleoresin paprika, sorghum, and cacao) were applied to a plant-based meat analog patty (PBMA). The target color ranges (external- and internal-cooked) for PBMA were set separately by evaluating the color values (L*, a*, and b*) of a Hanwoo (Korean native cattle) beef rib patty (HR). As a result of adding different levels of each pigment, the PBMA with cacao (1.0 mg/g) and sorghum (3.0–4.0 mg/g) pigments matched the external- and internal-cooked target ranges, respectively; however, adding a single pigment to PBMA insufficiently mimicked the external- and internal-cooked target ranges. To overcome the above limitations, red beet and cacao pigments were selected, and the color values of the PBMA with these pigments were optimized using response surface methodology. We obtained the optimized mixing ratio (0.4–1.5 mg/g of red beet and 1.1–1.3 mg/g of cacao pigments) and the sensory evaluation showed the color of PBMA with optimum pigments was most similar to HR, increasing appearance, overall acceptability, and purchase intention. These findings suggested that the cooked meat color can be mimicked by applying natural pigments to PBMA.

CHARACTERISTICS OF THE EXTERIOR OF THE POLESIE HORSE POPULATION OF DIFFERENT AGES

The article is devoted to the problem of studying the features of the development of the exterior of the horses of the Polesie population of Ukraine. A survey of the horses of the Polesie population of the Sherbaty Zugli farm, which are kept on the basis of the Ecological Research Station "Glyboki Balyky" of the Obukhovskyi district of the Kyivska region, was carried out. Today, the herd of Polesie horses has 22 heads. Measurements, assessment of the exterior, movements, temperament and performance of the studied herd of animals of different ages were carried out. Animals were evaluated according to the target standard of the main conformation traits of the Polesie horse population. According to the results of a comprehensive survey of horses of the Polesie population, it was found that young horses born in 2020–2021 are sufficiently developed for their age. Horses of 2.5 years of age are almost as large as adult animals. The difference is determined only by the girth of the chest (6.4 cm). There is also a rapid achievement of physiological maturity, which is characteristic of native breeds.&#x0D; It was noted that horses of the Polesie population, as well as typical representatives of local breeds, are characterized by: a rectangular shape of the body (92.0 ± 0.84), a large head (96.8 ± 1.31), a fairly pronounced right angle of the forearm (106.0 ± 1.07), small-sickle limbs (96.0 ± 1.69) and bone development proportional to body size (100.0 ± 0.84). A slight variability of the coefficient of variation (2.28–7.28%) was established.&#x0D; The studied herd fully complies with the requirements of the target color standard - the majority of horses are black or black-roan (56%). Also, most horses (75%) have signs of atavism characteristic of forest horses: transverse stripes on the legs, the so-called "zebroid", dark "plaque" on the shoulders and withers. Based on the research results, taking into account the experience of scientists from other countries, methodological recommendations have been developed for the assessment and selection of horses of the Polesie population with their subsequent introduction into production.&#x0D; Taking into account the difficult situation in the country and the small number of the studied horses, the issue of studying the features of the external development of horses in the Polesie population requires further research.