Fuzzy geometrical approach based on unit hyper-cubes for image contrast enhancement

In order to realize the stability and inheritance of image characteristics in the development process of a series of products, we comprehensively analyzed the cognitive differences among users, designers, and engineers and propose a multicriteria decision system for an intelligent design method of product forms based on a logistic regression model, relative entropy theory, and preference mapping (PREFMAP). First, from the perspective of the role characteristics of the design subjects, an equilibrium evaluation model was constructed using the logistic regression model and relative entropy theory. Second, combining the multidimensional perception space and the characteristics measurement of the product form, the fitness function of the image form was constructed based on PREFMAP. Third, a genetic algorithm was applied to establish the intelligent image-style-oriented design method, which could guide the image form development of a product series through innovative design. Lastly, the method was verified by taking Audi A4L series headlights as an example. And the image evaluation of the two new schemes was greater than that of the previous seven generations of headlights. The results verify the effectiveness and feasibility of the method. In this paper, we structured a relatively preliminary model to explain the fusion of cognitive information. More subjective and objective factors, algorithms, and image recognition technology need to be further studied to improve the model in our future work.

Section: Discussionmentioning

confidence: 99%

Fusion of Cognitive Information: Evaluation and Evolution Method of Product Image Form

Zhang

Liu

2021

“…Image segmentation is an important issue of image analysis and pattern recognition, which can determine the final analysis quality and recognition effect of the image [ 23 ]. During image processing, it is important to consider the performance of the method when images corrupted with noise and other imaging relics [ 24 , 25 ]. As we all know, the images collected by the system are often displayed using the RGB color model, but due to the high correlation of the R, G, and B three components, that is, when the brightness changes, the three components will change accordingly, which is not suitable for image segmentation.…”

Section: Methodsmentioning

confidence: 99%

Research on the Correlation between Breeding Environment and Activity of Yellow Feather Broilers Based on the Multichromatic Aberration Model

Zhao

Zou

Yin

et al. 2021

Broiler behavior is closely related to the breeding environment. Therefore, studying broiler behavior helps breeding farm workers to better carry out welfare breeding. In the breeding environment of yellow feather broilers, temperature, humidity, and ammonia concentration are the main factors that affect the behavior of the broilers. This study used a multichromatic aberration model to process the color images of yellow feather broilers to extract the activity feature of the broilers at different periods, utilized the Cb component of YCbCr color model and the b component of Lab color model to remove background litter in images, and employed the Q component of YIQ color model to remove the feeders and the drinkers from the image. The segmented images were constructed into an accumulator to generate a heat map of yellow feather broilers’ activity. Then, the correlation between the activity and the temperature and humidity index (THI) and the correlation between the activity and ammonia concentration were explored. The experiment found that the activity of the broilers was significantly positively correlated with ammonia concentration ( P < 0.05 ), indicating that the activity of yellow feather broilers increased with ammonia concentration ascending. Besides, the THI in the broiler chamber had a significant positive correlation with the ammonia data ( P < 0.01 ), indicating that when the THI in the broiler chamber increases, the ammonia concentration rises. The research provides a direction for exploring the impact of THI and ammonia concentration on the performance of yellow feather broilers. At the same time, it provides a theoretical basis for the early warning and judgment of broiler breeding by farm workers in the future.

“…Image preprocessing [ 44 , 45 ] also plays an important role in BIMM. Given a frame sequence F = { F 1 , F 2 ,…, F k }, P i k =( x i k , y i k ) denotes the i -th pixel in frame F k .…”

Section: Background-independent Motion Maskmentioning

confidence: 99%

Attention‐Based Temporal Encoding Network with Background‐Independent Motion Mask for Action Recognition

Weng

Zhang

Chen

et al. 2021

Convolutional neural network (CNN) has been leaping forward in recent years. However, the high dimensionality, rich human dynamic characteristics, and various kinds of background interference increase difficulty for traditional CNNs in capturing complicated motion data in videos. A novel framework named the attention-based temporal encoding network (ATEN) with background-independent motion mask (BIMM) is proposed to achieve video action recognition here. Initially, we introduce one motion segmenting approach on the basis of boundary prior by associating with the minimal geodesic distance inside a weighted graph that is not directed. Then, we propose one dynamic contrast segmenting strategic procedure for segmenting the object that moves within complicated environments. Subsequently, we build the BIMM for enhancing the object that moves based on the suppression of the not relevant background inside the respective frame. Furthermore, we design one long-range attention system inside ATEN, capable of effectively remedying the dependency of sophisticated actions that are not periodic in a long term based on the more automatic focus on the semantical vital frames other than the equal process for overall sampled frames. For this reason, the attention mechanism is capable of suppressing the temporal redundancy and highlighting the discriminative frames. Lastly, the framework is assessed by using HMDB51 and UCF101 datasets. As revealed from the experimentally achieved results, our ATEN with BIMM gains 94.5% and 70.6% accuracy, respectively, which outperforms a number of existing methods on both datasets.