Influence of Sorting Measures on Similar Segment Grouping based Denoising Algorithms

Under many circumstances, a high spatial resolution image (HR) is greatly needed for modern applications nevertheless the HR image captured device is usually overpriced cost. Hence, Super Resolution Reconstruction (SRR) technique, which can reconstruct a HR image from a single LR image or many LR images by using algebraic formulation, is one of the modern research fields in digital image processing (DIP) and Computer Vision (CV). In this paper, an extra-rate spatial enhancement constructed by MSRR (Multi-frame Super Resolution Reconstruction) using regularized technique and SSRR (Single-frame Super Resolution Reconstruction) using high-frequency pre-forecasting is presented in order to enlarge up to 16x ratio rate. Initially, a group of captured images with low spatial resolution are mathematically fused by MSRR using regularized technique established on a recursive Maximum Likelihood and Tukey's Biweight norm in order to enlarge up to 4x ratio rate. Next, this 4x enhanced image is enlarged to be 16x spatial resolution image by SSRR established on the high-frequency pre-forecasting. In the verification experimentation section, the verification outcome demonstrates that the proposed spatial enhancement is successful for enlarging HR image with 16x ratio rate with finer quality.

In the Multimedia era, removal of the Noises from an image becomes a key challenge in the field of Digital Image Processing (DIP) and Computer Vision. Noise may be mixed with an image during capturing time, transmission time or due to dust particle on the screen of capturing device. Therefore, removal of these unwanted signals from the image is urgently required for the better analysis of the image and the de-noised image is more meaningful for Object detection, Edge detection and many more. There are various types of image noise, however, Gaussian Noise and Impulse Noise are commonly found in the image. This work focuses on the outliers and Mean Filter to improve the performance for Gaussian noise reduction from the image. In experimental assessments, artificial noise has been mixed using MATLAB to MSRA (10k images) dataset, this dataset is used to evaluate our proposed technique. The experiment results show that the proposed approach improves the performance in noise reduction over other filter approaches.

The processing history of images plays an important role in many fields of digital image processing and computer vision. In this paper, we focus on geometrical transformations and show that images that have undergone such transformations contain hidden cyclostationary features. This makes possible employing the well-developed theory and efficient methods of cyclostationarity for blind analyzing of history of images in respect to geometrical transformations. To verify this, we also propose a cyclostationarity detection method and show how the traces of geometrical transformations in an image can be detected and the specific parameters of the transformation estimated. The method is based on the fact that a cyclostationary signal has a frequency spectrum correlated with a shifted version of itself.

COVID-19 is a virus that has been declared an epidemic by the world health organization and causes more than 2 million deaths in the world. To achieve this, computer-aided automatic diagnosis systems are created on medical images. In this study, an image processing and machine learning-based method is proposed that enables segmenting of CT images taken from COVID-19 patients and automatic detection of the virus through the segmented images. The main purpose of the study is to automatically diagnose the COVID-19 virus. The study consists of three basic steps: preprocessing, segmentation and classification. Image resizing, image sharpening, noise removal, contrast stretching processes are included in the preprocessing phase and segmentation of images with Expectation–Maximization-based Gaussian Mixture Model in the segmentation phase. In the classification stage, COVID-19 is classified as positive and negative by using kNN, decision tree, and two different ensemble methods together with the kernel support vector machines method. In the study, two different CT datasets that are open to the public and a mixed dataset created by combining these datasets were used. The best accuracy values for Dataset-1, Dataset-2 and Mixed Dataset are 98.5%, 86.3%, 94.5%, respectively. The achieved results prove that the proposed approach advances state-of-the-art performance. Within the scope of the study, a GUI that can automatically detect COVID-19 has been created.

The rise of cultural tourism in India and massive digitization over the last decade has necessitated preserving Indian art forms. Recent advances in artificial intelligence (AI) have provided the tools and techniques to make these efforts more efficient. The potential research areas involved are digital image processing, computer vision, and machine intelligence. Since researchers have not much explored the scope of the digital preservation of Indian classical dance, this paper contributes a method to classify different Indian classical dance forms. Computerized dance modeling offers a wide range of uses, including cultural conservation, online learning, entertainment, and even remedial treatment. Because the dataset is small, the paper uses transfer learning models for feature extraction and deep neural networks for classification. The proposed method showed a remarkable accuracy of 97.05 percent.

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.

In recent years, the target of local feature extraction and recognition technology is a hot research topic in digital image processing and computer vision field.In target recognition, pest undetected phenomenon often happens due to occlusion, once the conditions are favorable, pests quickly multiply and spread, causing great harm to the stored product.we established a common stored product pests image database, proposed feature extraction and matching algorithms, based on the combination of improved SURF and multi-resolution histogram.Pests specified in the database for target identification, especially when the pest is partially obscured, or the shape and size changes,by finding matching number of feature points the description of the target for accurate identification .This method is efficient, time-saving,provides an important scientific basis for the entry-exit inspection and quarantine in rapid identification of stored product pests.It is more conducive to the subsequent classification.

We discuss the problem of estimating an arbitrary regular parameterized curve and its length from an ordered sample of interpolation points in n-dimensional Euclidean space. The corresponding tabular parameters are assumed to be unknown. In this paper the convergence rates for estimating both curve and its length with cumulative chord piecewise-quartics are established for different types of unparameterized data including ε-uniform samplings. The latter extends previous results on cumulative chord piecewise-quadratics and piecewise-cubics. The numerical experiments carried out for planar and space curves confirm sharpness of the derived asymptotics. A high quality approximation property of piecewise-quartic cumulative chords is also experimentally verified on sporadic data. Our results may be of interest in computer vision (e.g. in edge and range image segmentation or in tracking), digital image processing, computer graphics, approximation and complexity theory or digital and computational geometry.

This paper proposes an efficient histogram modification method for contrast enhancement, which plays a significant role in digital image processing, computer vision, and pattern recognition. We present an automatic transformation technique to improve the brightness of dimmed images based on the gamma correction and probability distribution of the luminance pixel. Experimental results show that the proposed method produces enhanced images of comparable or higher quality than previous state-of-the-art methods.

Stereoscopic imaging and reconstruction of turbulent impinging diffusion flames have been demonstrated. The 3D geometry of the flame surface is reconstructed from any pair of stereo images through digital image processing and computer vision. Only one camera is required to capture the pair of stereo images due to the use of a stereo adapter. As a result the pair of flame images is captured on a single CCD chip. They are then post-processed to produce a 3D geometry of the flame. The 3D results and surface area calculations demonstrate the potential of stereo visualisation, data extraction and the validity of the proposed methodology.

Circle detection is a critical issue in computer vision and image processing. Whether in natural images or industrial images, the accuracy of circle detection has a significant impact on advanced vision applications. Conventional methods, such as circle Hough transform, random circle detection, and EDCircles, only reach pixel-level edge accuracy. This article proposes a subpixel circle detection method based on subpixel edges with accuracy of one-tenth of one pixel. All candidate circles are first detected by EDCircles. The circle scoring formula based on polarity, radius, and contour is then proposed to sort the detected circles, and the circle with the highest score is selected as the target. The 2-D subpixel calculation problem is transformed into the 1-D fitting problem, and the subpixel edge region is selected according to the gradient direction of the circular edge. To reduce the error of the step model and the real edge, the blurred edge model is proposed to fit the region. Subsequently, the parameters of the edge model are transformed into subpixel coordinates. To solve the problem that the traditional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L2$ </tex-math></inline-formula> -loss function is not robust to outliers, the Huber loss function is finally applied to the circle fitting, and the gradient descent method is adopted to calculate the circle parameters. Experiments on natural and industrial images show that the proposed method has good performance on robustness and accuracy.

Spectral imaging has received a great deal of attention recently. Spectral reflectance observed from object surfaces provides crucial information in computer vision and image analysis which include the essential problems of feature detection, image segmentation, and material classification. The estimation of spectral reflectance is affected by several illumination factors such as shading, gloss, and specular highlight. The spectral invariant representations for dielectric materials only, for these factors, are inadequate for other characteristic materials like metal. In this chapter, a spectral invariant representation is introduced for obtaining reliable spectral reflectance images. The invariant formulas for spectral images of natural objects preserve spectral information and are invariant to highlights, shading, surface geometry, and illumination intensity. As an application, a material classification method is presented based on the invariant representation, which results in reliable segmentations for natural scenes and raw circuit boards spectral images.

The growing demand for sustainable and energy-efficient buildings has highlighted the need for reliable and accurate methods to detect fenestration deterioration and assess UV radiation transmission. Traditional detection techniques, such as spectrophotometers and radiometers, discussed in Part I, are often expensive and invasive, necessitating more accessible and cost-effective solutions. This study, which is Part II, provides an in-depth exploration of the concepts and methodologies underlying UV bandpass-filtered imaging, advanced image processing techniques, and the mechanisms of pixel transformation equations. The aim is to lay the groundwork for a unified approach to detecting ultraviolet (UV) radiation transmission in fenestration glazing. By exploiting the capabilities of digital imaging devices, including widely accessible smartphones, and integrating them with robust segmentation techniques and mathematical transformations, this research paves the way for an innovative and potentially democratized approach to UV detection in fenestration glazing. However, further research is required to optimize and tailor the detection methods and approaches using digital imaging, UV photography, image processing, and computer vision for specific applications in the fenestration industry and detecting UV transmission. The complex interplay of various physical phenomena related to UV radiation, digital imaging, and the unique characteristics of fenestration glazing necessitates the development of a cohesive framework that synergizes these techniques while addressing these intricacies. While extensively reviewing existing techniques, this paper highlights these challenges and sets the direction for future research in the UV imaging domain.

Text segmentation in digital images is requisite for many image analysis and interpretation tasks. In this article, we have proposed an effective binarization method towards text segmentation in digital images. This method produces a number of connected components consisting of text as well as non-text. Next, it is required to identify the possible text components from the obtained connected components. Further, to distinguish between text and non-text components, a set of features are identified. Then, during training, we consider the two feature files namely text and non-text prepared by us. Here, K-Nearest Neighbour (K-NN) classifier is considered for the present two class classification problem. The experiments are based on ICDAR 2011 Born Digital Dataset. We have accomplished in binarization and as well as segmenting between text and non-text.

In recent years, video surveillance systems evolve a great interest as the application area. Recent studies have proven integration of digital image processing, computer vision, artificial intelligence and data analytics into video surveillance application. This paper provides a systematic review of the state-of-the-art video analysis techniques available in machine learning and deep learning. Video processing research trends illustrate a survey on practices like object detection, object recognition, object tracking, traffic control and monitoring, action and behaviour recognition, disaster management and so on. We further present focus on computational approaches for various challenges of video processing techniques, objectives of those techniques and evaluation based on multiple problems. In the end, we have discussed emerging issues in the field of video analytics and how machine learning and deep learning contribute to those issues.KeywordsVideo analysisObject detectionObject tracking and object classificationMachine learning and deep learning