Digital media image automatic recognition system based on mean clustering algorithm

Digital image recognition based on Fractional-order-PCA-SVM coupling algorithm

Based on chaos synchronization of two response lasers (RLs) injected by the common chaos signals from a driving laser (DL) with double optical feedback (DOF) and an additional neural network (NN), a bidirectional chaotic secure communication system with machine learning capability is proposed and the long-distance digital image transmission and recognition performances are numerically investigated. The simulated results show that the time delay signature (TDS) of the driving chaotic signals from DL with DOF is greatly weakened under suitable operation conditions. Two RLs can generate chaos signals with low TDS and broad bandwidth, and the high-quality chaos synchronization between two RLs can be achieved while the synchronization quality between DL and any one of RLs is extremely bad, which is helpful to realize the secure communication. Using the synchronized chaos output signals from two RLs as chaos carriers and chaos masking (CM) encoding method, the bidirectional 10 Gb/s secure communication over 140 km fiber channel can be realized and the effects of the fiber parameters on the communication performances are discussed. After adopting an additional NN, a long-distance digital image transmission and recognition based on this proposed secure communication system can be realized and the communication distance can be extended to 220 km.

This paper presents an automatic fruit recognition system for classifying and identifying fruit types. The work exploits the fruit shape and color, to identify each image feature. The proposed system includes three phases namely: pre-processing, feature extraction, and classification phases. In the pre-processing phase, fruit images are resized to 90 x 90 pixels in order to reduce their color index. In feature extraction phase, the proposed system uses scale invariant feature transform (SIFT) and shape and color features to generate a feature vector for each image in the dataset. For classification phase, the proposed model applies K-Nearest Neighborhood (K-NN) algorithm classification, and support vector machine (SVM) algorithm of different kinds of fruits. A series of experiments were carried out using the proposed model on a dataset of 178 fruit images. The results of carrying out these experiments demonstrate that the proposed approach is capable of automatically recognize the fruit name with a high degree of accuracy.

Human perception of sign language can serve as inspiration for the improvement of automatic recognition systems. Experiments with human signers show that sign language signs contain redundancy over time. In this article, experiments are conducted to investigate whether comparable redundancies also exist for an automatic sign language recognition system. Such redundancies could be exploited, for example, by reserving more processing resources for the more informative phases of a sign, or by discarding uninformative phases. In the experiments, an automatic system is trained and tested on isolated fragments of sign language signs. The stimuli used were similar to those of the human signer experiments, allowing us to compare the results. The experiments show that redundancy over time exists for the automatic recognizer. The central phase of a sign is the most informative phase, and the first half of a sign is sufficient to achieve a recognition performance similar to that of the entire sign. These findings concur with the results of the human signer studies. However, there are differences as well, most notably the fact that human signers score better on the early phases of a sign than the automatic system. The results can be used to improve the automatic recognizer, by using only the most informative phases of a sign as input.

To meet the testing requirements of optical automatic target recognition system for loitering munition, based on digital twin, a parallel test system is proposed. Using this parallel test system, single application tests in the design stage of optical automatic target recognition system are carried out first, and then the operational course of action of loitering munition is verified. Through test examples, the analytical method of the technical and tactical capacity of optical automatic target recognition system is introduced. Besides, by carrying out operational course of action verification tests, the effectiveness of designed trajectory planning is verified. With the help of parallel test, more trajectory planning options will be derived to provide reference for actual flight tests.

An automatic microscope image acquisition, evaluation, and recognition system was developed for the analysis of Utermöhl plankton chambers in terms of taxonomic algae recognition. The system called PLASA (Plankton Structure Analysis) comprises (1) fully automatic archiving (optical fixation) of aqueous specimens as digital bright field and fluorescence images, (2) phytoplankton analysis and recognition, and (3) training facilities for new taxa. It enables characterization of aqueous specimens by their populations. The system is described in detail with emphasis on image analytical aspects. Plankton chambers are scanned by sizable grids, divers objective(s), and up to four fluorescence spectral bands. Acquisition positions are focused and digitized by a TV camera and archived on disk. The image data sets are evaluated by a large set of quantitative features. Automatic classifications for a number of organisms are developed and embedded in the program. Interactive programs for the design of training sets were additionally implemented. A long-term sampling period of 23 weeks from two ponds at two different locations each was performed to generate a reliable data set for training and testing purposes. These data were used to present this system's results for phytoplankton structure characterization. PLASA represents an automatic system, comprising all steps from specimen processing to algae identification up to species level and quantification.

Face detection is a grave concern in digital image processing and automatic face recognition system. This research work proposed a complete mechanism for face detection using R, G, B color segmentation and search optimization scheme with Genetic Algorithm, also refer a discrete technique that is appropriate for combinatorial problems. In this paper we tried to build an R, G, B color range that will shelter a skin part from an image and handover a best fitted solution as fitness function for GA to perform further operation to detect images in complex background. In this paper our tryout are to enrich detection accuracy in lesser computational time. The evaluation shows that this algorithm is capable to detect the face from complex background conditions and for side faces too. This algorithm is tested on a wide number of test images. All the simulation has been done on MATLAB.

Likelihood ratio calibration is a very important step when applying an automatic speaker recognition system to forensic applications. This paper presents the process and evaluation of a speaker recognition system developed with spoken Indonesian database. The system is developed using MFCC feature, GMM-UBM modeling, and Z normalization. System performances were evaluated based on male and female genders, and two scenarios i.e., natural conversation and interview. System evaluations were done using performance measures for both discrimination and calibration abilities. Results show that based on various indicators, the system behaves very well. This is shown by the best achievable EER values of 4.66%, and Cmc values of 0.04. Therefore, the developed automatic speaker recognition system is now ready to be used for forensic applications in Indonesia.

In liver stereotactic body radiotherapy (SBRT) using fiducial markers, the accuracy of automatic image recognition of fiducial markers is important, and the imaging dose cannot be neglected in image-guided radiotherapy. Optimal imaging parameters of fiducial markers were investigated for automatic image recognition and imaging dose. We investigated automatic recognition with fiducial markers of different shapes and sizes. In addition, the optimum imaging conditions were examined based on the automatic recognition when the presence or absence of a filter, focal spot size, and phantom thickness were altered using the fiducial markers with a high automatic recognition. The results for different shapes and sizes of fiducial markers showed that larger markers were recognized more automatically, whereas shorter markers were recognized in the correct position. By using the filter, we were able to reduce the imaging dose by one third or one half compared to the case without the filter. The results for the focal spot size showed that using a larger size resulted in higher automatic recognition accuracy than using a smaller size. For the relationship between the automatically recognized imaging conditions and the air kerma when the phantom thickness was altered, it was necessary to keep the tube current-time product constant and increase the tube voltage in order to avoid poor recognition accuracy. The parameters we proposed are effective in shortening the treatment time and reducing the imaging dose because they allow us to acquire images with low doses and high accuracy of automatic recognition.

Background: With the rapid development of digital twinning technology, the compatibility of digital twinning technology to other technologies is continuously enhanced. It is because of this that the application of image recognition technology in digital twinning technology becomes a reality. However, the key technology of digital twin, the virtual-actual mutual control technology, is not mature enough, and the image recognition technology applied in digital twin technology also has the problem of coordinate system transformation, which becomes an important link of image recognition technology in digital twin. Methods: Based on the above two problems, we take the tangram splicing project as an example to realize a virtual-actual mutual control method, so that the digital twinning technology can be well presented. Furthermore, we implement an image recognition applied to the conversion of digital twinning technology, so that digital twinning technology and image recognition have a seamless connection, allowing the application range of digital twinning technology to be further expanded. Results: In this paper, image recognition technology is successfully applied to digital twin technology by adopting the conversion between different coordinate systems and the real and virtual real time control, which makes the applicability of digital twin technology in high-tech fields such as smart factories and smart manufacturing to a higher level. Conclusions: Finally, through the tangram splicing project, the motion trajectories of the robotic arm and the tangram are consistent with the virtual robotic arm and the tangram in the computer. It is proved that our method can well combine digital twinning technology and image recognition technology.

With the increasing improvement of living standards, healthy long-distance running has become one of the current ways of aerobic physical exercise. Long-distance running is beneficial to many bodily functions such as neck, spine, and heart, but inappropriate long-distance running can easily damage the joints of the human body. In order to make more fitness athletes run with a healthy and reasonable posture, this paper studies the automatic recognition and correction system of running movements based on computer vision technology. Based on the analysis of human motion detection methods and human motion recognition methods, an automatic recognition and correction system for running actions is designed. In order to understand the accuracy of the system in the recognition of wrong actions, this paper compares the designed system with the traditional system. The results show that the system designed in this paper has good accuracy and can control the deviation within a reasonable range.

This work presents a low-cost and fast-trainable automatic speaker-speech recognition (ASSR) system, by proposed binary halved clustering (BHC) method for human-machine interface (HMI) on an embedded platform, owing to the trait of low cost in ASSR system is essential and affordable for real-world application. In addition, fast-trainable ability can provide fast responding time. The reduction of waiting time makes the proposed HMI to be friendly for users. The speech recognition uses enhanced cross-word reference templates (ECWRTs) for template training type. The novel BHC method uses binary-halved splitting to generate speaker models for low complexity requirement. The regularity of binary halved behavior is beneficial for data scheduling and resource sharing in the embedded ASSR system. Compared with the conventional works, simulation results indicate that the proposed hardware accelerator achieves 28% less cost, 90% less responding time, an ASSR accuracy of 90%. Comparison exhibits that performance of the proposed system is greater than the conventional works, thereby demonstrating the friendly and affordable factor of the proposed HMI.

In order to overcome the limitations and inherent mode of traditional image art, this paper proposes an innovative system of image art based on digital media. Combined with the characteristics of digital media, the innovation system innovates the image art from three dimensions, namely, technological innovation, artistic innovation and concept innovation. Based on this, the innovation system combines a variety of science and technology, such as big data technology and virtual reality technology, and strengthens the deep integration and innovation development level of digital media and image art. The research results show that the innovation system can break through the limitations of traditional image art and realize the organic combination of image art with the current social development level and public aesthetic concept.

This paper considers automatic accent recognition system performance in relation to the specific nature of the accent data. This is of relevance to the forensic application, where an accent recogniser may have a place in casework involving various accent classification tasks with different challenges attached. The study presented here is composed of two main parts. Firstly, it examines the performance of five different automatic accent recognition systems when distinguishing between geographically-proximate accents. Using geographically-proximate accents is expected to challenge the systems by increasing the degree of similarity between the varieties we are trying to distinguish between. The second part of the study is concerned with identifying the specific phonemes which are important in a given accent recognition task, and eliminating those which are not. Depending on the varieties we are classifying, the phonemes which are most useful to the task will vary. This study therefore integrates feature selection methods into the accent recognition system shown to be the highest performer, the Y-ACCDIST-SVM system, to help to identify the most valuable speech segments and to increase accent recognition rates.

This paper investigates a fractionation method of cellulosic fibres in dry state. Although processing of cellulosic fibres in airstream has been utilised for decades in airlaid processes, no attempts to fractionate fibres in dry state have been reported. Our main goal is to determine how fibre properties and fibre consistency affect fibre flocculation and thus the behaviour of fibres in fractionation. Also evaluations of quality and capacity of fractionation operation are made. Pulp flocculation behaviour is measured with digital imaging and image recognition technique before and after the fractionation device. A doubleview, orthogonal imaging approach is applied obtaining statistics of three-dimensional shape, dimensions and coordinates of detected fibre flocs. Fraction and fibre properties are determined with Metso Fractionator and Metso Fiberlab. Results show that fibre behaviour in a given system strongly depends on the pulp properties. Flocculation was found to be strongly dependent on fibre consistency. Evaluation of fractionation operation was done with help of calculations of mass-reject rates and separation efficiencies. Calculations showed that highest separation efficiencies can be achieved with system when mass-reject rate is between 0.30 and 0.40.