This paper describes the development of a low-cost software, called Rat Steps, which allows the obtention of quantitative data (total distance traveled and average speed) as well as the graphic trajectory performed by...
详细信息
ISBN:
(纸本)9783030706012;9783030706005
This paper describes the development of a low-cost software, called Rat Steps, which allows the obtention of quantitative data (total distance traveled and average speed) as well as the graphic trajectory performed by an animal in the open field test. This behavioral test is widely used in neuroscience in order to visualize locomotor impairment following acute brain injury, including stroke, as well as the effect of experimental therapies for these neural disorders. The main tools used for the software development were digital imageprocessing techniques, Python programming, OpenCV library and machine learning algorithms, including the Mean Shift method. The software was successfully developed with effective obtention of quantitative parameters from the Open Field Test, which allows several applications in neuroscience research.
In the present time, there has been many adaptations of Object Detection is developed. Object Detection means catching the object name and it's other characteristics in an image or a video. This field is known to ...
详细信息
When using traditional phase-shift profilometry for 3D measurement, it is necessary to keep the measured object static during the shooting process. When the measured object is moving, errors will occur if the projecti...
详细信息
A better backbone network usually benefits the performance of various computer visionapplications. This paper aims to introduce an effective solution for infection percentage estimation of COVID-19 for the computed t...
详细信息
ISBN:
(纸本)9783031133244;9783031133237
A better backbone network usually benefits the performance of various computer visionapplications. This paper aims to introduce an effective solution for infection percentage estimation of COVID-19 for the computed tomography (CT) scans. We first adopt the state-of-the-art backbone, Hierarchical Visual Transformer, as the backbone to extract the effective and semantic feature representation from the CT scans. Then, the non-linear classification and the regression heads are proposed to estimate the infection scores of COVID-19 symptoms of CT scans with the GELU activation function. We claim that multi-tasking learning is beneficial for better feature representation learning for the infection score prediction. Moreover, the maximum-rectangle cropping strategy is also proposed to obtain the region of interest (ROI) to boost the effectiveness of the infection percentage estimation of COVID-19. The experiments demonstrated that the proposed method is effective and efficient.
In recent years, the global population has shown substantial growth, leading to an increase in its food security needs. In response, greenhouse cultivation has emerged as a strategy to ensure controlled conditions for...
详细信息
Recent work in machine Learning and Computer vision has highlighted the presence of various types of systematic flaws inside ground truth object recognition benchmark datasets. Our basic tenet is that these flaws are ...
详细信息
Analyzing human facial expressions using machinevision systems is indeed a challenging yet fascinating problem in the field of computer vision and artificial intelligence. Facial expressions are a primary means throu...
详细信息
Analyzing human facial expressions using machinevision systems is indeed a challenging yet fascinating problem in the field of computer vision and artificial intelligence. Facial expressions are a primary means through which humans convey emotions, making their automated recognition valuable for various applications including man-computer interaction, affective computing, and psychological research. Pre-processing techniques are applied to every image with the aim of standardizing the images. Frequently used techniques include scaling, blurring, rotating, altering the contour of the image, changing the color to grayscale and normalization. Followed by feature extraction and then the traditional classifiers are applied to infer facial expressions. Increasing the performance of the system is difficult in the typical machine learning approach because feature extraction and classification phases are separate. But in Deep Neural Networks (DNN), the two phases are combined into a single phase. Therefore, the Convolutional Neural Network (CNN) models give better accuracy in Facial Expression Recognition than the traditional classifiers. But still the performance of CNN is hampered by noisy and deviated images in the dataset. This work utilized the preprocessing methods such as resizing, gray-scale conversion and normalization. Also, this research work is motivated by these drawbacks to study the use of image pre-processing techniques to enhance the performance of deep learning methods to implement facial expression recognition. Also, this research aims to recognize emotions using deep learning and show the influences of data pre-processing for further processing of images. The accuracy of each pre-processing methods is compared, then combination between them is analysed and the appropriate preprocessing techniques are identified and implemented to see the variability of accuracies in predicting facial expressions. .
Unpaired image-to-image translation is a challenging task that requires mapping between source and target domains without paired examples. Conventional approaches rely on convolutional neural networks (CNNs) and their...
详细信息
ISBN:
(数字)9798350355413
ISBN:
(纸本)9798350355420
Unpaired image-to-image translation is a challenging task that requires mapping between source and target domains without paired examples. Conventional approaches rely on convolutional neural networks (CNNs) and their variants to achieve reliable translation results. However, in applications where structural consistency is key, CNN-based models are unable to capture global and long-range dependencies. In this paper, we introduce Contrastive Swin-UNet, a framework that leverages contrastive learning with shifted window self-attention mechanism, structured in U-shaped encoder-decoder network. Our approach employs a fully transformer-based generation for effective local and global feature learning. Taking advantage of self-attention, we show that integrated contrastive loss significantly improves translation consistency and generative fidelity by maximizing mutual information between source image and translated output. Our findings indicate that Contrastive Swin-UNet sets a new benchmark for unpaired image-to-image translation tasks, particularly in scenarios requiring high precision and regularity.
An improved YOLOv8n algorithm is proposed to address the issue of low helmet detection accuracy caused by the high density of electric bike riders in complex scenarios. Introducing Channel Prior Convolutional Attentio...
详细信息
ISBN:
(数字)9798350355413
ISBN:
(纸本)9798350355420
An improved YOLOv8n algorithm is proposed to address the issue of low helmet detection accuracy caused by the high density of electric bike riders in complex scenarios. Introducing Channel Prior Convolutional Attention (CPCA) mechanism to enhance the feature extraction capability of the model; Introducing Symmetric Positive Definite Convolution (SPDConv) to enhance ordinary convolutions; Replace the bounding box loss function with a Weighted Intersection over Union (Wise IoU) function to improve the model’s ability to locate small targets. The experimental results showed that the improved model improved the mAP accuracy detected on the self-made dataset by 4.4%, with only a slight increase in the number of model parameters, and has the potential to be used as a solution in practical applications.
Although mass is one of the most relevant process variables, industries may lack an inline monitoring of mass, which has a high cost in some cases. Due to their availability in sorting processes, cameras have potentia...
详细信息
Although mass is one of the most relevant process variables, industries may lack an inline monitoring of mass, which has a high cost in some cases. Due to their availability in sorting processes, cameras have potential as a low-cost alternative for the estimation of mass in recycling applications. Nevertheless, further research is needed to transform image information into mass. This work tackles this challenge by proposing a novel method of converting image information into mass of particles, complementing size measures with intensity and texture features extracted from the whole picture. Models were adjusted, employing machine learning techniques, using an industrial waste sample of post-consumer plastic film. The visual properties showed a dependency on mass labels, and the models achieved an error of 9 g for subsamples between 2 and 82 g. The analysis and validation of this imageprocessing method provide a new alternative for the estimation of particle mass.
暂无评论