In recent years, the computational power of handheld devices has increased rapidly to the point of parity with computers of only a generation ago. The multiple tools integrated into these devices and the progressive expansion of cloud storage have created a need for novel compressing techniques for both storage and transmission. In this work, a novel L1 principal component analysis (PCA) informed K-means approach is proposed. This new technique seeks to preserve the color definition of images through the application of K-means clustering algorithms. Assessment of the efficacy is carried out utilizing the structural similarity index (SSIM).
As one of the classic fields of computer vision, image classification has been booming with the improvement of chip performance and algorithm efficiency. With the rapid progress of deep learning in recent decades, remote sensing land cover and land use image classification has ushered in a golden period of development. This paper presents a new deep learning classifier to classify remote sensing land cover and land use images. The approach first uses multi-layer convolutional neural networks to extract the image features, attached through a fully-connected neural network to generate the sample loss. Then, a hard sample memory pool is created to collect the samples with large losses during the training. A batch of hard samples is randomly extracted from the memory pool to participate in the training of the convolutional fully connected model so that the model becomes more robust. Our method is validated by testing the classic remote sensing land cover and land use dataset. Compared with the previous popular classification algorithm, our algorithm can classify images more accurately with a shorter training iteration.
The fusion of multispectral sensor data techniques for sets containing complementary information about the subject of observation leads to the visualization of data into a form more easily interpreted by both humans and algorithms. Many applications of feature-level fusion seek to combine edges and textures, across the bandwidth of the sensory spectrum. Visualization techniques can be skewed by the introduction of corruption and redundancies induced by harmonics. A majority of image fusion techniques rely on intensity hue saturation (IHS) transforms, principal component analysis (PCA), and Gram Schmidt. PCA’s ability to remove the redundancy from a set of correlated data while preserving the variance and its resistance to color distortion lends itself to this application. PCA also has a lower spectral distortion as compared to IHS and has been found to create superior image fusion. The application of neural network control techniques has been shown to more accurately recreate results similar to those found by human inference. Over the years, increased computation power has given rise to the spread of neural networks into roles previously carried out by humans. Select advanced image processing techniques have benefited greatly from their implementation. We propose a novel method of utilizing PCA in conjunction with a neural network to achieve a higher quality of image fusion. Implementation of an autoencoder neural network to fuse this information creates a higher level of data visualization when compared to traditional weighted fusion techniques.
Living in a constant news cycle creates the need for automated tracking of events as they happen. This can be achieved through the investigation of broadcast overlay textual content. There exists a great amount of information to be deciphered via these means before further processing, with applications spanning from politics to sports. We utilize image processing to create mean cropping masks based on binary slice clustering from intelligent retrieval to identify areas of interest. This data is handed off to CEIR, based on the connectionist text proposal network (CTPN) to fine-tune the text locations and an advanced convolutional recurrent neural networks (CRNN) system to carry out text recognition to recognize the text strings. In order to improve the accuracy and reduce processing time, this novel approach utilizes a preprocessing mask identification and cropping module to reduce the amount of data being processed by the more finely tuned neural network.
There has been a sharp rise in the amount of data available for analysis in many professional fields in recent years. In the medical sector, this significant increase in data can help detect and confirm underlying symptoms in patients that would otherwise remain undetected. Machine learning techniques have been applied in the medical sector and can help diagnose irregularities when data is provided for the specific area on which the system has been trained. Leveraging the newfound amount of big data and advanced diagnostic techniques, higher dimensional data feature extraction can be better analyzed. The algorithm presented in this paper utilizes a convolutional neural network to categorize electrocardiogram (ECG) data by processing the original data implementing the fast Fourier transform (FFT) and principal component analysis (PCA) to reduce dimensionality while maintaining performance. The paper proposes three intelligent identification algorithms that can be fed into another specialized machine learning system or analyzed using traditional diagnostic procedures.
Big data has been driving professional sports over the last decade. In our data-driven world, it becomes important to find additional methods for the analysis of both games and athletes. There is an abundance of videos taken in professional and amateur sports. Player datasets can be created utilizing computer vision techniques. We propose a novel approach by creating an autonomous masking algorithm that can receive live or previously recorded video footage of sporting events. This procedure can identify graphical overlays to optimize further processing by tracking and text recognition algorithms for real-time analysis.
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