Research on Saliency Prior Based Image Processing Algorithm

Applications of Noncausal Gauss–Markov Random Field Models in Image and Video Processing

The theoretical outcomes and experimental results of new color model implemented in algorithms and software of image processing are presented in the paper. This model, as it will be shown below, may be used in modern real time video processing applications such as radar tracking and communication systems. The developed model allows carrying out the image process with the least time delays (i.e. it speeding up image processing). The proposed model can be used to solve the problem of true color object identification Experimental results show that the time spent during RGI color model conversion may approximately four times less than the time spent during other similar models. .

Mathematical morphology is a powerful tool for image processing and analysis of binary, greyscale and colour images. In the paper, an efficient new fuzzy model for morphological colour image processing is introduced. A new vector-ordering scheme that uses fuzzy if–then rules is proposed, and then the basic morphological operations of erosion and dilation are defined. The morphological operators presented, which are vector preserving, are less sensitive to image distortion and perform significantly better in noise removal problems than other reported morphological operators. Experimental results demonstrate these advantageous characteristics on real images. Additionally, illustrative examples that exhibit the applicability of the proposed framework to edge-detection problems are given.

The study aimed to apply to Machine Learning (ML) researchers working in image processing and biomedical analysis who play an extensive role in comprehending and performing on complex medical data, eventually improving patient care. Developing a novel ML algorithm specific to Diabetic Retinopathy (DR) is a challenge and need of the hour. Biomedical images include several challenges, including relevant feature selection, class variations, and robust classification. Although the current research in DR has yielded favourable results, several research issues need to be explored. There is a requirement to look at novel pre-processing methods to discard irrelevant features, balance the obtained relevant features, and obtain a robust classification. This is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classifier (SKPD-PSC) method. The novelty of this method relies on the appropriate non-linear classification of exclusive image processing models in harmony with the Platt Scale Classifier (PSC) to improve the accuracy of DR detection. First, a Steerable Filter Kernel Pre-processing (SFKP) model is applied to the Retinal Images (RI) to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians (DDG). Next, the Partial Derivative Image Localization (PDIL) model is applied to the extracted features to localize candidate features and suppress the background noise. Finally, a Platt Scale Classifier (PSC) is applied to the localized features for robust classification. For the experiments, we used the publicly available DR detection database provided by Standard Diabetic Retinopathy (SDR), called DIARETDB0. A database of 130 image samples has been collected to train and test the ML-based classifiers. Experimental results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accuracy rate with minimum time and complexity compared to the state-of-the-art methods. The accuracy and speed of DR detection for numerous types of images will be tested through experimental evaluation. Compared to state-of-the-art methods, the method increases DR detection accuracy by 24% and DR detection time by 37.

: Neurodegenerative diseases can make life difficult and lead to death in many cases. They also can be difficult, time-consuming, and costly to diagnose with enough accuracy/certainty. Artificial intelligence (AI) has shown promise in tackling some of the challenges present in medical imaging and is anticipated to become a crucial tool in health care applications in the near future. In particular, deep learning methods have displayed great performance in various subfields of image processing, including but not limited to image segmentation, image synthesis, and image reconstruction. In this paper, many state-of-the-art applications of deep learning models in image processing were reviewed.

The use of color image processing is divided by two factors. First color is used in object identification and simplify extraction from a scene and color is powerful descriptor.Second, humans can used thousands of color shades and intensities.Color Image Processing is divided into two areas full color and pseudocolor processing.In this processing various color models are used that are based on color recognition,color components etc. A few papers on various applications such as lane detection, face detection, fruit quality evaluation etc based on these color models have been published. A survey on widely used models RGB,HSI, HSV, RGI etc is represented in this paper.

This paper explores the use Mathematical models play a crucial role in image processing, offering a powerful framework for analysing, manipulating, and understanding digital images.This abstract emphasizes the significance of mathematical models in image processing and their potential to enhance accuracy and efficiency in this domain.Image processing techniques aim to extract meaningful information from images, enabling applications such as object recognition, medical imaging, and video surveillance.However, raw image data often contain noise, irrelevant details, or complex patterns that hinder accurate interpretation.

: We are developing a general theory for multi scale signal and image modeling, processing, and analysis that matched to singularity-rich data, such as transients and images with edges. Using a linguistic analogy, our model can be interpreted as grammars that constrain the wavelet vocabulary. Our investigation focuses on probabilistic graph models (tree-based hidden Markov models) that can accurately, realistically, and efficiently represent singularity structure in the wavelet domain. Grammar design is being guided by a detailed study of the final structure of singularities using Besov spaces and multifractal analysis.

In order to overcome the difficulty of massive high-dimensional image processing, a massive high-dimensional image processing system based on big data technology is proposed in this paper. The system fully combines the big data technology, analyzes the differences between the current massive high-dimensional image processing work and the traditional image processing work, and discusses the difficulties of the current massive high-dimensional image processing work. Based on this, the system also includes parallel image processing model and low delay dynamic processing model, which greatly improves the accuracy and efficiency of static image processing and dynamic image processing. The results demonstrate that the system has strong feasibility, and can indeed process large-scale high-dimensional image data and save the time and cost of data processing.

Deep Learning (DL) revolutionized a wide variety of research fields and applications of Artificial Intelligence (AI) in the last years. Computer vision is among those fields that witnessed a stronger impact, especially in some tasks like image equalization, classification and segmentation, that have several applications in medical imaging. Such applications often focus on microscopy images, but they can be successfully extended to other fields, like the detection of diseases involving the ocular surface and the cornea.One of the reasons of the success of deep learning applied to image processing relies in the end‐to‐end data‐driven approach. This means that the whole image processing and classification/segmentation pipeline can be adapted to the data being processed. This is particularly powerful when dealing with medical images, whose processing is often based on the detection of low‐ and mid‐level patterns that might be difficult to define in a formal manner. This means that deep learning has the chance to select and consider some image elements that would be nearly impossible to define using standard computer vision techniques. However, this becomes possible if large amounts of data are available for training the network, which became reality in the last years thanks to the wide digitalization of medical systems, including ophthalmology equipment.Based on a substantial amount of literature that has rapidly grown in the last years, this talk will focus on the new perspectives of eye disease diagnosis that were opened by the advent of deep learning. At the same time, the limitations and risks that must be considered in the development of DL‐based medical systems, like overfitting and lack of generalization, will be highlighted and discussed. Keeping track of such limitations is crucial in order not to consider DL as a sort of magic box, but rather, as powerful image analysis and processing tool.The talk will also provide a qualitative and quantitative evaluation of the diagnosis systems currently available that exploit DL and AI systems. The current and future impact of automatic image processing systems dedicated to ophthalmology will also be discussed. When automatic medical image analysis will be widely available, the patterns of medical diagnosis will need to change, taking advantage of the possibility to perform larger screenings that will be automatically processed.

This paper presents a summary of operations of the logarithmic image processing (LIP) model and its applications. The LIP model is a mathematical framework which provides a set of generalised version of addition, subtraction, multiplication, convolution and so on, for signal processing. First, we briefly describe the LIP model and point out its distinctive properties for image processing. Then we summarize the current applications of the LIP model. A novel image filtering algorithm and its extension to multiscale processing along with the LIP model based Sobel operator for edge detection are discussed in detail. Finally, we suggest further research area of the LIP model. >

This paper proposes a design and experimentally implements of a multimodal machine learning (ML)-based indoor fire detection and alarm system which combines image processing and Internet-of-Things (IoT)-based environmental sensing for mitigating false alarm. The proposed system consists of two fire ML-based detecting models: the image processing model used for detecting a fire based on fire images captured by a camera, a sensing data processing model used for predicting a fire based on room environmental data. Additionally, a communication module (i.e. 4G/Wi-Fi) is equipped to use for interacting with the home-owner and the fire station to get a confirmation before activating any further actions. In the proposed model, detected results of both abovementioned detecting models are combined to enhance the reliability of the final decision (i.e. whether it is a true fire or false fire). A true fire is recognized when two detecting modules both detected that it is a fire. Otherwise, when only one detecting module recognized that it is a false fire, it is recognized as uncertain. In this case, it needs to be confirmed by the home-owner based on fire images captured by the camera and sent over the communication module. If it is recognized as a true fire, a true alarm is raised and an alarm message containing house’s location information is sent to the fire station for activating the emergency case of fire. Otherwise, the fire is recognized as a false alarm and then no action is taken. The numerical and experimental results show that the proposed system achieves a high accuracy of fire detection and avoids false alarms.

In spatial data modeling, the topological notion “continuous function” can link the spatial data with the modeled real world. In this article, we study fuzzy weakly continuous function and fuzzy weakly irresolute functions from set theoretic point of view. These generalizations of continuous functions in fuzzy setting will bring new practically relevant models in image processing.

This chapter introduces applications of visual attention models in image processing related areas: just noticeable difference (JND) modelling, visual quality assessment, image and video coding, visual signal retargeting and compressive sensing. Section 8.1 illustrates the combination of visual attention and the JND model towards a complete visibility threshold model which can be used in a wide spectrum of uses. The application of attention models in quality assessment (QA) of images and video is presented in Section 8.2, and a typical QA index, SSIM, is weighted by visual attention in different ways. The use of attention models to explore visual redundancy for better image coding is introduced in Section 8.3. The applications for image retargeting and compressive sensing are presented in Sections 8.4 and 8.5, respectively.

Regularization is a key element of variational models in image processing. To overcome the weakness of models based on total variation, various high order (typically second order) regularization models have been proposed and studied recently. Among these, Euler's elastica energy based regularizer is perhaps the most interesting in terms of both mathematical and physical justifications. More importantly its success has been proven in applications; however it has been a major challenge to develop fast and effective algorithms. In this paper we propose a new idea for deriving a primal dual algorithm, based on Legendre–Fenchel transformations, for representing the elastica regularizer. Combined with an augmented Lagrangian for-mulation, we are able to derive an equivalent unconstrained optimization that has fewer variables to work with than previous works based on splitting methods. We shall present our algorithms for both the image restoration problem and the image segmentation model. The idea applies to other models where the elastica regularizer is required. Numerical experiments show that the proposed method can produce highly competitive results with better efficiency.