Use Of Global Features Research Articles

FANet: Fast and Accurate Robotic Grasp Detection Based on Keypoints

In practice, the real-time and accuracy of robotic grasp detection are two very important metrics. In the past, researchers had to sacrifice the real-time nature of the detection network in order to obtain higher detection accuracy. How to make the real-time and accuracy of the network co-exist is a problem worth studying. In order to solve this problem, this paper proposes a network, FANet, based on grasp keypoints, which improves the accuracy of grasp detection while ensuring the real-time performance. The key of this paper is how to quickly and accurately detect grasped keypoints. To this end, this paper proposes a local refinement module that optimizes and de-duplicates each feature of the multi-scale feature map, enabling the network to make full use of the multi-scale features. We also propose a global feature refinement module that allows the network to make better use of global features. We also propose a grasp keypoint optimization module that predicts the offset between the actual keypoints and the predicted keypoints, enabling the network to predict the keypoints more accurately. Moreover, we develop two FANets specifically for grasp detection on CPU and GPU, both of which can accomplish real-time grasp detection in real-world scenes. We complete the training and testing of FANet on the Cornell dataset and the Jacquard dataset, achieving SOTA results on the Jacquard dataset. We also test FANet on a dataset of unknown objects, all with good results. Finally, we use the FANet in grasping experiments with an actual Baxter robot and achieve an average grasping success rate of 96%. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Real-time and accuracy are two very important metrics in robotic grasping detection. To achieve high accuracy, more time is often consumed for feature extraction. Similarly, in order to improve the real time performance, we need to reduce the time consumed in the feature extraction process, which may result in a drop in detection accuracy. How to coordinate the relationship between them, so as to have both, is a problem worth investigating. Current methods tend to focus on obtaining higher accuracy and they are willing to spend more time to achieve higher accuracy. But in some practical scenarios, such as on factory assembly lines, objects move fast, and the network needs to be able to detect the grasping position quickly, the real-time performance is more important, which makes some methods difficult to use. In addition, most of the current methods tend to focus on GPU-based robotic grasp detection methods, and in real-world scenarios we may not have such a powerful processing GPU available. In contrast, the CPU is an indispensable unit of the computer that we can use to process images without a high-performance GPU. However, compared to GPUs, the CPUs’ image processing capability is poor, making it difficult to achieve real-time processing. Faced with this situation, the problem of how to achieve real-time and high accuracy in a CPU-only robotic grasp detection network is worth studying, but most of the existing methods ignore this problem. To address these problems, we propose a Fast and Accurate robotic grasp detection Network (FANet), which not only enables the network to combine real-time and accuracy, but also enables real-time detection on CPU or GPU.

Improved Asian food object detection algorithm based on YOLOv5

An improved model called TR-YOLO is employed for Asian food object detection. Firstly, the ViT module is introduced into the model to make better use of global features. Secondly, the Swin Transformer module is introduced on the three detection branches to output the features. Finally, the Mconcat feature fusion method is proposed, which enables the model to learn the feature weights to assign feature channels independently. The experimental results show that the TR-YOLO model further improves the detection accuracy.

Optimization of face recognition algorithm based on deep learning multi feature fusion driven by big data

Today, with the rapid development of science and technology, the era of big data has been proposed and triggered reforms in all walks of life. Face recognition is a biometric recognition method with the characteristics of non-contact, non mandatory, friendly and harmonious, which has a good application prospect in the fields of national security and social security. With the deepening of the research on face recognition, small-scale face recognition has achieved good recognition results, but in the era of big data, the existing small-scale face recognition methods have gradually failed to meet the social needs, and how to get a good face recognition effect in the era of big data has become a new research hotspot. Based on this, this paper aims to optimize the existing face recognition algorithm, study the face recognition method driven by big data, and propose a deep learning multi feature fusion face recognition algorithm driven by big data. First, for the problem that 2DPCA (Two-dimensional Principle Component Analysis) can well extract the global features of the face under large samples, but the local features of the face are difficult to process, this paper uses the LBP (Local Binary Pattern, LBP) algorithm to extract the texture features of the face, and the extracted texture features are integrated with the global features extracted by 2DPCA to multi-feature fusion, so that the fused features can take into account both global and local features, and have better recognition results. Then using the obtained fusion features as input, training in a convolutional neural network, and measuring the similarity based on the feature vectors of the sample set and the training set after the training, can realize multi-feature fusion face recognition. Through the analysis of simulation experiments, it is found that, compared with the use of global features or local features alone, the fusion features obtained by multi-feature fusion of global features extracted by 2DPCA and local features extracted by LBP algorithm have better recognition effect in the big data environment. After convolutional neural network trains and recognizes this feature, a high recognition accuracy rate is obtained, which can show that the face recognition method designed in this paper has good application potential in the era of big data. In the background of big data, the accuracy of face recognition can reach more than 90%, which can meet the needs of society well.

Multiwavelet-Based Image Fusion Method Using Unit-Linking Pulse Coupled Neural Networks

To simulate biological activities of human visual system to details and make full use of global features of source images, we propose a multiwavelet-based image fusion method using unit-linking pulse coupled neural networks (ULPCNNs) model. After motivated by external stimuli from images, ULPCNNs can produce series of binary pulses containing much global information. Then we employ the first firing time of each neuron as the salience measure. Experimental results demonstrate that, for multifocus images, remote sensing images, and infrared and visible images, our proposed method always generates satisfying fusion results.

Shape-Based Classification of 3D Facial Data to Support 22q11.2DS Craniofacial Research

3D imaging systems are used to construct high-resolution meshes of patient's heads that can be analyzed by computer algorithms. Our work starts with such 3D head meshes and produces both global and local descriptors of 3D shape. Since these descriptors are numeric feature vectors, they can be used in both classification and quantification of various different abnormalities. In this paper, we define these descriptors, describe our methodology for constructing them from 3D head meshes, and show through a set of classification experiments involving cases and controls for a genetic disorder called 22q11.2 deletion syndrome that they are suitable for use in craniofacial research studies. The main contributions of this work include: automatic generation of novel global and local data representations, robust automatic placement of anthropometric landmarks, generation of local descriptors for nasal and oral facial features from landmarks, use of local descriptors for predicting various local facial features, and use of global features for 22q11.2DS classification, showing their potential use as descriptors in craniofacial research.

VISUAL SPEECH RECOGNITION USING DYNAMIC FEATURES AND SUPPORT VECTOR MACHINES

This paper presents a vision based technique to identify the unspoken phones using a small camera that is located on the headset of the speaker. The system is based on temporal integration of the video data to generate motion history image (MHI). The paper proposes the use of global features to classify the MHI and compares the use of image moments with Discrete Cosine Transform (DCT). A comparison between Zernike moments (ZM) with DCT indicates that while the accuracy of classification for both techniques is very comparable (96% for ZM and 94% for DCT) when there is no relative motion between the camera and the mouth, ZM is resilient to rotation of the camera and continues to gives good results despite rotation but DCT is sensitive to rotation. Based on the accuracy of the system and its resilience to movement artefacts such as rotation, the authors propose the use of such a system for human computer interface. Such a system could be invaluable when it is important to communicate without making a sound, such as giving passwords when in an open office or in public spaces.