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英国Paper代写:Visual intelligence

2019-10-18 | 来源:51Due教员组 | 类别:Paper代写范文

下面为大家整理一篇优秀的paper代写范文- Visual intelligence,供大家参考学习,这篇论文讨论了视觉智能。视觉智能,是一门计算机视觉与人工智能的科学,主要目的是让机器可以理解视觉场景。简单来说,研究者让计算机对于图片或者视频等视觉场景不断地学习,进而分析视觉信息,最终达到自主理解视觉场景的水平。视觉智能可以理解为计算机视觉研究与人工智能研究的结合,主要是针对图像、视频等视觉内容进行分析和理解。经过多年的发展,视觉智能领域相关技术不断地创新、优化、迭代、更新,大大地丰富和完善视觉智能研究体系,促使视觉智能在城市设计、智能交通等领域都有广泛的应用。

Visual intelligence,视觉智能,论文代写,essay代写,paper代写

Vision is the most direct way for humans to get in touch with nature, which can help humans understand and understand the external world. Visual intelligence has undoubtedly become the most important branch in the field of artificial intelligence. Visual intelligence is the fusion of computer vision and artificial intelligence, the main purpose of which is to make the machine understand the visual scene. In simple terms, the researchers let the computer continuously learn the visual scene such as pictures or video, and then analyze the visual information, and finally reach the level of self-understanding the visual scene.

Visual intelligence can be understood as the combination of computer vision research and artificial intelligence research, mainly for the image, video and other visual content analysis and understanding. In recent years, there have been numerous studies in the field of visual intelligence, including face recognition, pedestrian tracking, object classification and recognition, visual question-and-answer, semantic segmentation, gesture estimation, behavior recognition, scene understanding, etc.

As the basic research of visual intelligence, face recognition has quickly become one of the most popular researches due to its features of recognition concealment, portrait collection convenience and large data scale. It is widely applied in daily life and urban construction. Face recognition has experienced four development stages: research based on psychology and engineering, research based on face recognition, research based on human-computer interaction and research based on machine learning. Common data sets for face recognition include LFW, CMUPIE, YouTubeFace, PaSC, etc. With the development of large-scale face recognition data set, researchers have proposed a number of different algorithms. In the early years, face recognition was mainly based on histogram features of gradient orientation. HOG feature is the representation of the local image region by statistical information of gradient orientation in the local image region. This kind of algorithm is good for face recognition under constraint conditions, but when some factors change, the recognition rate decreases significantly. The LQP algorithm proposed by Hussain et al in 2012 and the DFD algorithm proposed by LeiZ et al in 2014 are typical face recognition algorithms based on superficial learning. Since these algorithms are based on real numerical features and are sensitive to faces without constraints, when the background environment of the input image changes greatly, the robustness and accuracy will be significantly reduced, which leads to poor face recognition performance. With the development of deep learning, DeepFace, DeepID series, center-loss and other face-detection algorithms based on deep learning have achieved excellent results.

Object detection is widely used in image retrieval, video monitoring, ocean monitoring, human behavior recognition, defense system and safe medical treatment, making it a central topic of discussion in the field of visual intelligence. Object detection can be understood as a study on the classification and recognition of real world objects such as people, animals, cars and furniture. Object detection can be divided into static object detection and dynamic object detection. Common static data sets include ImageNet, MSCOCO, PASCALVOC, KITTI, etc. Generally speaking, compared with static target detection, dynamic target detection needs to separate the dynamic target from the background environment of video frame sequence, and it needs to go through three stages of dynamic target recognition, target tracking and behavior analysis. Target detection by background subtraction and frame difference method, the time difference method represented, optical flow algorithm and the traditional RCNN, FasterRCNN, YOLO, SSD, represented by two kinds of algorithm is given priority to, the depth of the learning algorithms of target detection algorithm based on depth of convolution network and can be divided into R - CNN and SSD, YOLO algorithm, based on the deep learning algorithm in the detection performance and detection speed is superior to the traditional algorithm.

Visual q&a is a task that combines computer vision with natural language processing. The VQA system takes the image and the open questions based on the image as the input, and produces the answers composed of natural language as the output after reasonable analysis. In terms of technology, compared with other visual intelligence tasks, the VQA task integrates multiple complex disciplines and therefore faces more uncertainties and more challenges in functional implementation. VQA-abstract, Visual7W, coco-qa, DAQUAR, fm-iqa are typical VQA data sets. VQA feature extraction includes two parts: image and problem. The commonly used network for image feature extraction includes Resnet, VGG, etc., while the commonly used network for problem feature extraction includes GRU, LSTM, etc. There are four methods of visual q&a: joint embedding, attention mechanism, combination model and knowledge enhancement. The current research focus of existing visual intelligence is still based on images. There are only a few researches on video visual q&a, and there are many challenges in the future.

Gait recognition is an important research direction in the field of visual intelligence. Common databases for gait recognition include CASIA, ou-isir, USF, Southampton, etc. Gait recognition process usually needs motion detection, period detection, feature extraction and recognition processing. Gait feature extraction mainly includes model-based, non-model-based and fusion feature extraction. Methods of recognition process include Bayes, SVM, HMM, CNN, KNN, etc. Gait recognition technology is often combined with target detection, face recognition, fingerprint recognition and other technologies. Because gait recognition involves complex scenes, there are still many problems to be solved in the practical application process.

After years of development, related technologies in the field of visual intelligence continue to innovate, optimize, iterate and update, greatly enriching and improving the research system of visual intelligence, and promoting the wide application of visual intelligence in urban design, intelligent transportation, radio and television, medical image diagnosis, industrial visual inspection and other fields. At the same time, with the development of big data and the Internet of things, the application fields of visual intelligence in the future will continue to expand and enrich.

Media industry is mainly based on content production, information dissemination, terminal services, the rapid development of visual intelligence, will certainly set off a wave in the media industry. So far, in the field of visual intelligent face recognition and tracking, image retrieval technology has been successfully applied in special effects and short video content production and communication, such as face recognition successfully applied in trill, quickly, such as short video APP, behavior recognition and tracking in the 3 d movie, video analysis specific themes, scenes, objects, or face, these examples to prove that visual intelligence now has become an integral part of the media industry, "visual intelligence + media" pattern has become the inevitable development trend.

With the rise of computer vision and artificial intelligence, unmanned driving has attracted much attention in recent years. In August 2016, Google set up the driverless project team. In October, tesla became the first company to mass-produce self-driving hardware. In December of the same year, ford launched the second generation of self-driving cars. In addition to automobile manufacturers, ali, tencent, baidu and other leading domestic Internet companies have started to set up autonomous driving laboratories respectively, which promotes the vigorous development of the automobile industry with visual intelligence. In terms of traffic monitoring, pedestrian detection technology can assist the traffic police to monitor and track suspects, provide objective and fair evidence, and effectively curb the occurrence of illegal ACTS. Therefore, visual intelligence has always played a decisive role in the traffic industry.

In the 21st century, it is most noteworthy that visual intelligence has penetrated into various fields of medicine, and relevant technologies play an important role in the medical industry. For example, intelligent image detection and intelligent medical robots are common. Due to the characteristics of the medical industry, such as wide coverage, high complexity and high risk, numerous researchers begin to try the research model combining visual intelligence with traditional medicine. Visual intelligence will change the model of the traditional medical industry, reshape the medical industry and promote the development of medicine.

With the development of science and technology, although visual intelligence in all walks of life have achieved a lot of good results, but want to achieve a real sense of "intelligence" still has a long way to go. How to make the machine think and understand the visual scene autonomously is an important problem to be solved by visual intelligence.

The development of visual intelligence requires the combination of strong theoretical knowledge and excellent technical support. Researchers need to constantly explore new knowledge, work hard to open up new horizons, give full play to visual intelligence technology, and truly integrate into every aspect of human life.

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