Automated seed identification with computer vision: challenges and opportunities

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Because it is so difficult to distinguish handwritten digits, digit identification is one of the most critical applications in computer vision. This is one of the reasons why it is so tough. The field of handwritten character recognition is one in which a great deal of application of numerous deep learning models has occurred. The startling parallels that can be drawn between deep learning and the brain are primarily responsible for its meteoric rise in popularity. In this study, the Artificial Neural Network and the Convolutional Neural Network, two of the most used Deep Learning algorithms, were investigated with an eye toward the recognition process's feature extraction and classification phases. With the assistance of the categorical cross-entropy loss and the ADAM optimizer, the models were trained on the MNIST dataset. Backpropagation and gradient descent are the two methods utilized during the training process of neural networks that contain reLU activations and carry out automatic feature extraction. In computer vision, one of the most common and widely used classifiers is the Convolution Neural Network, sometimes referred to as ConvNets or Convolutional neural networks. This network is used for the recognition and categorization of images.

A computer vision and robotic system for an intelligent workstation

The field of computer vision is now a multi-billion dollar enterprise, with its use in surveillance applications driving this large market share. In the last six years, computer vision researchers have started to discuss the risks and harms of some of these systems, mostly using the lens of fairness introduced in the machine learning literature to perform this analysis. While this lens is useful to uncover and mitigate a narrow segment of the harms that can be enacted through computer vision systems, it is only one of the toolkits that researchers have available to uncover and mitigate the harms of the systems they build. In this monograph, we discuss a wide range of risks and harms that can be enacted through the development and deployment of computer vision systems. We also discuss some existing technical approaches to mitigating these harms, as well as the shortcomings of these mitigation strategies. Then, we introduce computer vision researchers to harm mitigation strategies proposed by journalists, human rights activists, individuals harmed by computer vision systems, and researchers in disciplines ranging from sociology to physics. We conclude the monograph by listing principles that researchers can follow to build what we call community-rooted computer vision tools in the public interest, and give examples of such research directions. We hope that this monograph can serve as a starting point for researchers exploring the harms of current computer vision systems and attempting to steer the field into community-rooted work.

Recently, deep learning-based positioning systems have gained attention due to their higher performance relative to traditional methods. However, obtaining the expected performance of deep learning-based systems requires large amounts of data to train model. Obtaining this data is usually a tedious process which hinders the utilization of such deep learning approaches. In this paper, we introduce a number of techniques for addressing the data collection problem for deep learning-based cellular localization systems. The basic idea is to generate synthetic data that reflects the typical pattern of the wireless data as observed from a small collected dataset. Evaluation of the proposed data augmentation techniques using different Android phones in a cellular localization case study shows that we can enhance the performance of the localization systems in both indoor and outdoor scenarios by 157% and 50.5%, respectively. This highlights the promise of the proposed techniques for enabling deep learning-based localization systems.

Computer vision is an interdisciplinary scientific field that deals with how computers obtain, store, interpret and understand digital images or videos using artificial intelligence based on neural networks, machine learning and deep learning methodologies. They are used in countless applications such as image retrieval and classification, driving and transport monitoring, medical diagnostics and aerial monitoring. Written by a team of international experts, this edited book covers the state-of-the-art of advanced research in the fields of computer vision and recognition systems from fundamental concepts to methodologies and technologies and real world applications including object detection, biometrics, Deepfake detection, sentiment and emotion analysis, traffic enforcement camera monitoring, vehicle control and aerial remote sensing imagery. The book will be useful for industry and academic researchers, scientists and engineers in the fields of computer vision, machine vision, image processing and recognition, multimedia, AI, machine and deep learning, data science, biometrics, security, and signal processing. It will also make a great course reference for advanced students and lecturers in these fields of research.

Computer vision is one of the hottest research fields in deep learning. The emergence of generative adversarial networks (GANs) provides a new method and model for computer vision. The idea of GANs using the game training method is superior to traditional machine learning algorithms in terms of feature learning and image generation. GANs are widely used not only in image generation and style transfer but also in the text, voice, video processing, and other fields. However, there are still some problems with GANs, such as model collapse and uncontrollable training. This paper deeply reviews the theoretical basis of GANs and surveys some recently developed GAN models, in comparison with traditional GAN models. The applications of GANs in computer vision include data enhancement, domain transfer, high-quality sample generation, and image restoration. The latest research progress of GANs in artificial intelligence (AI) based security attack and defense is introduced. The future development of GANs in computer vision is also discussed at the end of the paper with possible applications of AI in computer vision.

Computer Vision, and hence Artificial Intelligence-based extraction of information from images, has increasingly received attention over the last years, for instance in medical diagnostics. While the algorithms' complexity is a reason for their increased performance, it also leads to the "black box" problem, consequently decreasing trust towards AI. In this regard, "Explainable Artificial Intelligence" (XAI) allows to open that black box and to improve the degree of AI transparency. In this paper, we first discuss the theoretical impact of explainability on trust towards AI, followed by showcasing how the usage of XAI in a health-related setting can look like. More specifically, we show how XAI can be applied to understand why Computer Vision, based on deep learning, did or did not detect a disease (malaria) on image data (thin blood smear slide images). Furthermore, we investigate, how XAI can be used to compare the detection strategy of two different deep learning models often used for Computer Vision: Convolutional Neural Network and Multi-Layer Perceptron. Our empirical results show that i) the AI sometimes used questionable or irrelevant data features of an image to detect malaria (even if correctly predicted), and ii) that there may be significant discrepancies in how different deep learning models explain the same prediction. Our theoretical discussion highlights that XAI can support trust in Computer Vision systems, and AI systems in general, especially through an increased understandability and predictability.

Poultry farming is an important unit in global agronomy, contributing immensely to the production of meat and eggs. Safeguarding the health and welfare of poultry is vital for ethical and financial reasons. In recent years, computer vision awareness has gained prominence as a powerful tool for poultry monitoring. This review paper provides an outline of the application of computer vision in poultry monitoring. We explore the different phases of this technology, with real-time image acquisition, object recognition, and behavior analysis. By connecting cameras and sophisticated algorithms, a computer vision system can distinguish unusual behavior, track poultry activities and observe its environmental conditions. Furthermore, this study discovers the benefits of computer vision in poultry farming, including early detection of disease, better production efficiency and improved animal welfare. In conclusion, the application of computer vision in monitoring poultry holds immense potential for the industry; by providing a corridor to a more sustainable and ethical poultry farming system with increased productivity. This paper equally discusses recent developments, challenges, forthcoming prospects in the field and academic research gaps identified.

Convolutional neural networks (CNNs) have performed exceptionally well in various computer vision tasks. Previously, researchers relied on feature extraction and then classification. With CNNs, feature extraction and classification are performed in a single step. Therefore, incorporating a set of convolutional neural networks, known as an ensemble, can help increase the effectiveness of their behavior. This paper presents an architecture that is the result of collaborative efforts, consisting of three types of CNN: Densenet121, Resnet101, and MobileNetV2. We also present experimental results using the Cifar10 and Cifar100 datasets, achieving impressive classification accuracy of 99% for Cifar10 and 86% for Cifar100. This case study contributes to deep learning optimization and benefits researchers and practitioners looking for optimal approaches in various computer vision applications. We also evaluate the scalability and robustness of our proposed method in the context of different CNN structures by using more than one model.

Computer vision has been transformed by deep learning, which has made it possible for robots to comprehend and interpret visual data at previously unheard-of levels. This study explores deep learning for computer vision systems: future trends and upcoming technology. Looking ahead, several significant advancements are reshaping this profession. First off, a viable approach is to combine deep learning architectures with generative models like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). This synergy makes activities like picture generation, style transfer, and data augmentation easier and enables a more nuanced interpretation of visual data. Second, a key component of deep learning for computer vision is now recognized to be attention mechanisms. These techniques, which are based on human visual attention, allow models to concentrate on particular areas of a picture, improving efficiency and interpretability. Tasks such as object detection, image segmentation, and image captioning demonstrate the critical role of attention mechanisms. Moreover, computer vision is reaching new heights thanks to the combination of deep learning and other cutting-edge technologies. Real-time processing on devices is made possible by the combination of deep learning and edge computing, which lowers latency and improves privacy. Furthermore, new opportunities for applications in augmented reality, robotics, and immersive experiences are being created by the combination of deep learning and 3D vision technologies. In the deep learning space for computer vision, transfer learning and meta-learning are likewise becoming more and more popular [1]. These methods increase performance on a different but related job by utilizing pre-trained models or knowledge from one task. This method minimizes the need for large datasets for training in situations where labeled data is rare. Deep learning in computer vision raises ethical questions that are becoming more and more significant. Concerns about decision interpretability, algorithmic bias, and privacy are becoming more prevalent. Fairness, accountability, and transparency must be given top priority in this field's future developments in order to guarantee the appropriate use of these technologies. In conclusion, there are a lot of promising things that lie ahead for deep learning in computer vision. Advances in transfer learning, attention mechanisms, and the integration of generative models are all leading to more complex and effective vision systems. As these technologies advance, it is critical to address ethical issues in order to build trust and guarantee that deep learning has a good social impact. This study offers a thorough analysis of these recent developments, providing insight into the future direction of deep learning for computer vision.

Collaborative robots using computer vision applications in a chemical laboratory

Deep learning based computer vision approaches for smart agricultural applications

The Remote Sensing Image Scene Classification (RSI-SC) is a fundamental and challenging task in a remote sensing community area and used for earth observation application such as agricultural monitoring, civilian monitoring in military, city planning, crop planning, geomorphology, Land Use and Land Cover (LULC), metrology and soil mapping. In remote sensing image analysis, satellite and drone-captured images are used to study the Earth’s surface. The goal of image classification systems in this field is to assign meaningful labels to captured images, allowing them to be organized in a semantic structure. Such semantic ordering of images has wide applications in digital image processing and computer vision, including remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. Feature extraction and representation at lower and intermediate levels was the mainstay of earlier approaches to remote sensing image analysis. The use of smaller image datasets hindered these approaches, but their great performance was demonstrated by combining numerous features and applying machine learning techniques. There has been a recent trend toward using deep learning models for remote sensing picture analysis, with hybrid deep learning approaches outperforming single models. This review article provides an in-depth examination of current deep learning methodologies, including low- and mid-level feature representation. It also provides a synopsis of image standards that are available to the public for use in remote sensing image analysis. We also compare different techniques that represent recent advances in deep learning models, addressed the challenges of remote sensing images and discussed about current applications in the field.

Machine learning is currently a prominent issue due to the direct training of machines with minimal human input. In the present period, the scenario of manual machine feeding has changed, it now learns automatically. Machine learning approaches are utilized for a variety of tasks, such as feature extraction, pattern recognition, object recognition, and classification. In computer vision, machine learning is used to extract important information from visual data. Computer vision contributes to a variety of disciplines, including surveillance, robotics, suspect detection, optical character recognition, and numerous other technologies. Computer vision helps in extracting metadata from humans, such as emotions, sentiments, personality, and so on. This paper presents a thorough yet current survey of human profiling based on computer vision. However, we present a taxonomy of data profiling and computer vision as well as how to profile humans from image data. As an experience, we will discuss our program about human faces recognising emotions from a person’s image using deep learning. In any event, the outcomes we have achieved are critical and important.