Bio‐Inspired Artificial Vision and Neuromorphic Image Processing Devices

The last technological evolutions of imaging techniques and the appearance of new kinds of TV cameras lead us to think of new image acquisition and processing devices. Several kinds of cameras are operated in our laboratories with different video output signals (200 ms frame, CCIR and RS 170 standards, 40 ms image mode, sampled CCD output), so no existing acquisition device was easy to transfer between different experiments. Our new image memory is then designed to be linked to any TV camera. It can be connected to our computers too, through its IEEE 488 interface, although it may work on its own. Its three main purposes are - signal acquisition, managed by a 8086 microprocessor, which can be externally triggered ; - memorizing of four 512 x 512 x 8 bits images on which some pre-programmed processes can be performed ; - bi-directionnal communication of data with a computer or an image processing device, through three kinds of links. We tested that image memory on a laboratory setup, on which we stored images delivered by a CCD camera. We present here a comparison between those results and those obtained on the same setup with a Pericolor 1000, and we emphasize on the automatic conversion threshold adaptation, and on the possibilities of adjusting the conversion scale, allowed by this device.

The digital image acquisition and processing technology already mature also in domains and so on visual communication obtains day by day[1]. However, the image acquisition and processing technology had many kinds, for example white balance processing and image denoising methods. The traditional image acquisition and processing systems are implemented using software by the PC or DSP chip[2], and sometimes software in series can not meet the requirement of high speed image processing. this article has given FPGA in the image acquisition and processing simple application, applied FPGA in the image acquisition and processing to be possible to enhance the image processing the speed, reduced takes the logical unit. The system designed by above-mentioned way achieves a satisfying effect by experimental verification.

The major studied in this dissertation is the non-destructive testing method of silkworm cocoon's quality, based on the digital image processing and photoelectricity technology. Through the images collection and the data analysis, procession and calculation of the tested silkworm cocoons with the non-destructive testing technology, internet applications automatically reckon all items of the classification indexes. Finally we can conclude the classification result and the purchase price of the silkworm cocoons. According to the domestic classification standard of the silkworm cocoons, the author investigates various testing methods of silkworm cocoons which are used or have been explored at present, and devices a non-destructive testing scheme of the silkworm cocoons based on the digital image processing and photoelectricity technology. They are dissertated about the project design of the experiment. The precisions of all the implements are demonstrated. I establish Manifold mathematic models, compare them with each other and analyze the precision with technology of databank to get the best mathematic model to figure out the weight of the dried silkworm cocoon shells. The classification methods of all the complementary items are designed well and truly. The testing method has less error and reaches an advanced level of the present domestic non-destructive testing technology of the silkworm cocoons.

In general, monitoring of animal growth and health is done directly by farmers (invasive measurement methods) which can cause cows to be injured or experience stress. To avoid this, several studies have been conducted on non-invasive methods using image processing technology. In this study, we systematically reviewed several works of literature to identify and synthesize published articles on image processing technology and image processing applications related to weight estimation and individual cattle identification. Analysis of image processing technologies used for weight estimation and individual cattle identification is the main objective of this article. Articles were searched through several databases and studies that met the inclusion criteria were analyzed and used in the review. The studies were divided into three main themes: image processing technologies, applications using image processing, and image processing research on cattle growth and health. It can be concluded that deep learning approaches are increasingly being studied, tested and considered as a viable and promising approach to monitor cattle weight and health in several aspects

There has been significant advances in the field of image and video processing over the past few decades. The term "image processing" is used to describe multiple signal-processing methodologies where images (such as video or picture frames) serves as the input, resulting to another image or a collection of image-related parameters or features. The majority of methodologies to image processing include reducing the picture to a two-dimensional signal and processing it in the same way as any other signal. The term "video processing" on the other hand is used to describe a particular type of signal processing where video files or video streams are utilized as output or input signals. Video recorders, televisions, video codecs, digital versatile, disc players, and other devices all utilize video processing algorithms. This paper provides a survey of the components of Digital Image Processing (DIP) as well as the recent developments in Image Processing technology and DIP applications.

There has been significant advances in the field of image and video processing over the past few decades. The term "image processing" is used to describe multiple signal-processing methodologies where images (such as video or picture frames) serves as the input, resulting to another image or a collection of image-related parameters or features. The majority of methodologies to image processing include reducing the picture to a two-dimensional signal and processing it in the same way as any other signal. The term "video processing" on the other hand is used to describe a particular type of signal processing where video files or video streams are utilized as output or input signals. Video recorders, televisions, video codecs, digital versatile, disc players, and other devices all utilize video processing algorithms. This paper provides a survey of the components of Digital Image Processing (DIP) as well as the recent developments in Image Processing technology and DIP applications.

The purpose of this paper is to analyze the application of image processing in UAV and autopilot. UAV and autonomous driving technology are important development directions in the field of intelligent transportation and agricultural production, and image processing, as one of the core technologies supporting its perception and decision-making, plays a vital role. For UAV, in agricultural plant protection and natural disaster monitoring, the rapid evaluation and identification of farmland growth status and disaster situation can be realized through image processing technology, which provides important support for crop production and rescue work. For the automatic driving system, image processing technology can detect and track the road environment, traffic signs and pedestrians, and improve the driving safety and comfort of vehicles. However, image processing technology also faces some challenges in application, such as complex environmental conditions and real-time requirements. In the future, through the improvement of sensor technology, algorithm optimization and data sharing, the application of image processing technology in UAV and autonomous driving will be continuously improved, bringing more innovation and development opportunities for intelligent transportation and agricultural production.

Exploiting biomimetic perception of invisible spectra in flexible artificial human vision systems (HVSs) is crucial for real-time dynamic information processing. Nevertheless, the fast processing of motion objects in natural environments poses a challenge, necessitating that these artificial HVSs simultaneously have swift photoresponse and nonvolatile memory. Here, inspired by the human retina, we propose a flexible UV neuromorphic visual synaptic device (NeuVSD) based on GaOx@GaN-composited nanowires for dynamic visual perception. Benefiting from the combined action of oxygen adsorption kinetics on the GaOx shell and photoelectronic excitation in the GaN core, the NeuVSD can achieve rapid photoresponse and long-term plasticity simultaneously, a feature that surpasses traditional devices based on persistent photoconductivity mechanisms. Beyond the optoelectronic synaptic plasticity, the flexible NeuVSD on the PET/PI substrate exhibits good performance stability after 1000 bending cycles, profiting from the high adaptability of nanowires. Furthermore, target recognition and motion detection with weak light intensities are achieved through the establishment of neuromorphic visual neural networks, relying on dynamic exposures and edge information processing, respectively. Real-time edge detection can still be realized under a 40% noise factor and effectively remove the noise background. The flexible NeuVSD array-based artificial visual system can enhance the capability of dynamic visual information processing in low-light and high-noise conditions, thereby fostering the evolution of in-sensor computing and artificial intelligence.

The human–machine interface with head control can be applied in many domains. This technology has the valuable application of helping people who cannot use their hands, enabling them to use a computer or speak. This study combines several image processing and computer vision technologies, a digital camera, and software to develop the following system: image processing technologies are adopted to capture the features of head motion; the recognized head gestures include forward, upward, downward, leftward, rightward, right-upper, right-lower, left-upper, and left-lower; corresponding sound modules are used so that patients can communicate with others through a phonetic system and numeric tables. Innovative skin color recognition technology can obtain head features in images. The barycenter of pixels in the feature area is then quickly calculated, and the offset of the barycenter is observed to judge the direction of head motion. This architecture can substantially reduce the distraction of non-targeted objects and enhance the accuracy of systematic judgment.

This paper gives a point to detect transmission line broken stocks defect problems by using image processing and recognition technology, which has obtained the good effect in tests. By comparing with a set of template, the wire broken stocks defects can be extracted from the complex background identify. The method can be applied to power line inspection by helicopters and unmanned aerial vehicle (uav) in a variety of ways.

In film and television production, efficient and precise image processing is vital for achieving realistic visual effects. Therefore, exploring and applying advanced image processing technologies has become an essential method for elevating the production quality of film and television projects. This work investigates the application of artificial intelligence (AI) technology in the processing and production of animated images in film and television scenarios. By comparing the performance of standard Generative Adversarial Network (GAN), DenseNet, and CycleGAN models under different noise conditions, it is found that CycleGAN performs the best in image denoising and detail restoration. Experimental results demonstrate that CycleGAN achieves a Peak Signal-to-noise Ratio (PSNR) of 30.1dB and a Structural Similarity Index Measure (SSIM) of 0.88 under Gaussian noise conditions. Moreover, CycleGAN achieves a PSNR of 29.5dB and an SSIM of 0.85 under salt-and-pepper noise conditions. It outperforms the other models in both conditions. Additionally, CycleGAN’s mean absolute error is significantly lower than that of the other models. This work demonstrates that CycleGAN can more effectively handle complex noise and generate high-quality images under unsupervised learning conditions. These findings provide new directions for future image processing research and offer important references for model selection in practical applications. This work not only offers new perspectives on the development of animation image processing technology but also establishes a theoretical foundation for applying advanced AI techniques in film and television production. Through comparative analysis of various deep learning models, this work highlights the superior performance of CycleGAN under complex noise conditions. This advancement not only drives progress in image processing technology but also provides effective solutions for efficient production and quality enhancement of future film and television works.

With the convenience brought by the rapid development of information, teaching reform has also set off a heat wave. The emergence of Internet+ , multimedia technology, virtual reality technology and artificial intelligence has made education continue to develop towards informatization, and mobile learning has become a way for students to learn. This paper provides a reference for the reform of exercise classes in colleges and universities, enriches the teaching methods, improves the learning quality of students, and makes the action image processing technology better applied to physical education classrooms. In this paper, according to the characteristics of the human motion system, the training actions of sports are collected. After the 3D action data is properly calibrated and normalized, the motion vector is used as the training input method. This method conforms to the laws of the human motion system. In terms of temporal feature extraction, this paper proposes a multi-level long short-term memory neural network structure for action recognition. The action image processing technology improves the accuracy of movements more significantly, and proves that it has a certain promotion effect on the self-editing ability and innovation ability of students'. The final results of the research show that the accuracy of action recognition has been improved to a certain extent after image data processing. The accuracy of action recognition in images has increased from 67.13% to 91.23%, and the accuracy rate has remained above 90% after action image processing which proves the effectiveness of the neural network algorithm processing method.

Images are signals produced by the imaging devices and can be digitized and processed by using the modern digital computers. Image processing is a subject that deals with the various mathematical and computing techniques/algorithms and the devices that can be used for improvement of image quality, processing of images and analysis. Image processing has a wide variety of applications in day-to-day activities and has gained popularity commercially and also in academic. In this paper, an introduction to the importance of images and imaging devices and their history and applications before the advent of digital computers and digital camera are provided in brief. The advantages of digital computers for digital image processing, uses and applications of image processing and the common image processing operations/techniques that are in use and practice are mentioned. Finally, the future scope and the research work that can be carried out in this area are highlighted for the prospective academicians, industrialists, and researchers, for the welfare of the society and mankind.KeywordsImagesImaging devicesDigital cameraDigital computerImage processingImage analysis and applications

On-line measurement of deposit dimension in spray forming using image processing technology

The fast advancement of computer science and information technology has resulted in a significant revolution in the commercial painting sector. The professional painting industry has experienced a dramatic breakthrough with the rapid expansion of computer science and technology. With the rapid development and comprehensive popularization of computer technology, more and more people use computer image processing technology in oil painting created as a new way of creation. Some artists use a variety of ways to study the way of oil painting creation and use image processing technology to stimulate the creator’s imagination and improve the efficiency of drawing sketches in optimizing creative ideas, drawing, and processing original works. However, the value of art comes from creation. Oil painting creation based on computer image processing is a new type of artistic production that still requires the integration of different artists’ ideas and creative concepts. The image processing technology is a tool and a way for artists to make art while also realizing the convergence of science, technology, and art. This paper uses computer image processing technology to study oil painting creation. First, it briefly discusses computer image processing technology, focusing on picture preprocessing and image graying, and then uses this technique in formation of oil painting. Second, it establishes the oil painting mathematical, direction field, and color conversion models completing the oil painting image creation by using this model. Finally, the advantages of this technology are analyzed from the two aspects of oil painting creation, painting technique, and brush direction. The results show that 75% of artists are very satisfied with the digital painting technique, and 65% of artists are very satisfied with the painting brush direction, which shows that the application effect of computer image processing technology in oil painting creation is remarkable.