Developing a new color model for image analysis and processing

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.

Chromatic and achromatic (AC) assessments of camouflage textiles have been critical to the defense researchers for concealment, detection, recognition, and identification (CDRI) of target signature against multidimensional combat background (CB). AC assessment and camouflage measurement techniques are simulated and experimented for assessment of camouflage textiles against CB. This model has been demonstrated for color measurement spectrophotometer, scanning electron microscopy (SEM), digital imaging, hyperspectral imaging, and image processing software (ImageJ) for the advancement and establishment of AC camouflage textiles assessment. The chromatic variations of 48 artificial target objects (TOBs) have been synthesized by image processing; the technique can be implemented for defense CB-CDRI assessment. Microstructural variation versus optical signal of woodland, desertland and stoneland CB materials have been elucidated by SEM magnification. The achromatic variation of CB materials have been demonstrated for the replacement of optical signal against modern remote sensing device to the imaging sensor. Color difference (Δ E), microstructural variations, pixel variations to imaging signal and standard deviation of CB materials have been represented for remote sensing surveillance of defense applications against TOB-CB-CDRI. Technical simulation of color, texture, gloss, and pixel intensity has been derived for AC-CDRI assessment of camouflage textiles in TOBs-CB environment.

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

Purpose The dicentric chromosome assay (DCA), recognized as the gold standard for biological dosimetry in emergency radiation exposure, involves multiple key steps. Among these, metaphase image selection remains ambiguous and particularly challenging for beginners. This study evaluates the impact of metaphase image selection on dicentric (Dic) frequency during initial training. Materials and methods Peripheral blood (PB) from three healthy donors was irradiated with 2 Gy X-rays and incubated at 37 °C for 2 h for DNA repair. PB samples were then cultured for 48 h with phytohemagglutinin and colcemid. After fixation, metaphase spreads were prepared, Giemsa-stained, and images were captured by specific microscope imaging and image processing software. Three beginners independently selected and analyzed 50 metaphase images each. A trainer with experience in performing radiation emergency related DCA then reviewed and removed unsuitable images. The beginners reanalyzed Dic frequencies, supplementing with additional images as needed to reach 50 metaphases. Dic frequencies were compared before and after image refinement. Results Dic frequencies were lower in metaphase image pools selected by beginners compared to those refined by the trainer. The novices’ selections included numerous over-condensed metaphases, making Dic detection difficult. Conclusion Blood culture for 48 h permanent colcemid treatment increases the occurrence of over-condensed chromosomes, affecting Dic scoring. This study highlights the importance of proper training in metaphase image selection and structured education on quality assessment, including the use of specific microscope imaging and image processing software to ensure accurate biological dosimetry.

We present MOPEX - a software package for astronomical image processing and display. The package is a combination of command-line driven image processing software written in C/C++ with a Java-based GUI. The main image processing capabilities include creating mosaic images, image registration, background matching, point source extraction, as well as a number of minor image processing tasks. The combination of the image processing and display capabilities allows for much more intuitive and efficient way of performing image processing. The GUI allows for the control over the image processing and display to be closely intertwined. Parameter setting, validation, and specific processing options are entered by the user through a set of intuitive dialog boxes. Visualization feeds back into further processing by providing a prompt feedback of the processing results. The GUI also allows for further analysis by accessing and displaying data from existing image and catalog servers using a virtual observatory approach. Even though originally designed for the Spitzer Space Telescope mission, a lot of functionalities are of general usefulness and can be used for working with existing astronomical data and for new missions. The software used in the package has undergone intensive testing and benefited greatly from effective software reuse. The visualization part has been used for observation planning for both the Spitzer and Herschel Space Telescopes as part the tool Spot. The visualization capabilities of Spot have been enhanced and integrated with the image processing functionality of the command-line driven MOPEX. The image processing software is used in the Spitzer automated pipeline processing, which has been in operation for nearly 3 years. The image processing capabilities have also been tested in off-line processing by numerous astronomers at various institutions around the world. The package is multi-platform and includes automatic update capabilities. The software package has been developed by a small group of software developers and scientists at the Spitzer Science Center. It is available for distribution at the Spitzer Science Center web page.

While emphasizing the intensity or saturation component in order to obtain high-quality color images, keeping the hue component unchanged is important; thus, perceptual color models such as HSI and HSV were used. Hue-Saturation-Intensity (HSI) is a public color model, and many color applications are commonly based on this model. However, the transformation from the HSI model to RGB model after the processing intensity and saturation in the HSI model usually generates the gamut problem, because, the HSI gamut is larger than the RGB gamut. Nevertheless, the common area of RGB gamut and HSI gamut is large to the middle value of the intensity. In this paper, we propose a new color image processing scheme in HSI color model. In the proposed scheme, the positive image processing and the negative image processing are performed in parallel. Since we can realize the negative processing system which is equivalent to the normal (i.e., positive) processing system, the proposed scheme is same degree-of-freedom of the normal processing scheme. In the negative image processing, color images are treated as CMY color space. The common area of CMY gamut and HSI gamut is large from the middle value of the intensity. Therefore, if the intensity value until middle, we choose the positive image processing result as the final result of our scheme. On the other hand, if the intensity value larger than middle value the result of negative image processing result is chosen as the final result. Due to that, we can almost avoid the gamut problem by the proposed scheme.

: 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.

This research explores the design and implementa- tion of an image and video processing platform using recon- figurable FPGA technology. With the increasing demand for real- time, high-performance multimedia processing in modern applications, a flexible and efficient solution is essential. The project utilizes an FPGA-based platform to handle image and video data processing through edge detection, image scaling, and real-time motion detection techniques. By integrating custom- built hardware components, this study aims to provide a low- power, high-throughput solution that meets the requirements of both image and video processing tasks. Materials and methods involved using Xilinx Virtex-II Pro FPGA for hardware imple- mentation, alongside embedded memory processors and multi- functional design elements such as zoom-in/out utilities. The system was evaluated through several experimental procedures, using various image and video datasets, to measure performance in terms of speed, resource utilization, and power consumption. Results indicated that the system achieved notable improvements in terms of efficiency, utilizing 25% of logic resources, 55% of on- chip memory, and 180mW of power. The discussion focuses on the adaptability of FPGA-based systems in meeting the diverse needs of multimedia processing, emphasizing scalability and low power consumption. In conclusion, the reconfigurable FPGA platform provides a versatile and efficient environment for image and video processing. The system’s resource efficiency, scalability, and potential for further enhancements make it a promising candidate for future multimedia applications. Keywords-Reconfigurable FPGA Architecture, Video and Image Processing, Edge Detection, Image Rescaling.

The medical far infrared (FIR) imaging system has recently been modified to be more sensitive, rapid and cheap. Also, the image format for processing on a personal computer and output format from integrated circuit of a FIR sensor have gradually been standardized. The objective of the report is to develop an application software to analyze clinical FIR images. We developed new programs which are written as macro programs on commercially available image processing software and by which any digital image data with known data format becomes applicable to the standard FIR image analysis system. NIH Image was introduced as the image processing software. Sequences of calls or routines for medical oriented image processing were written with macro codes in Macintosh computers. We aimed to obtain the same image processing functions which had been developed for medical computer thermography systems. Every source FIR image was converted into temperature images (thermograms). Marking on the same temperature region (isotherm), making temperature profiles (line scan) and temperature histograms of region of interest (ROI), drawing temporal temperature difference images (dT thermogram), comparing the temperature difference of two ROI (asymmetry detection thermogram), and displaying thermography index thermogram (TI thermogram) have been developed.

The resource limitation of mobile commerce systems cause the problem that the existing image processing software based on the centralized computing mode had difficulty in running in mobile commerce systems. This paper gives a solution for the problem by adopting web service-based image processing approach. The solution reduces the resource consumption of mobile commerce systems by distributing image processing tasks to service providers, service registry centers and service requesters. Furthermore, a web service discovery algorithm called WBSD is presented. Service providers adopt the WBSD to provide efficient services and increase image processing speed. Compared with traditional methods of image processing, Web service-based image processing method has the advantages of loose coupling and component oriented and can take full advantage of the computing resources in heterogeneous network. Thus web service-based image processing approach can effectively solve the resource bottle-neck that traditional image processing software had.

Over the past few years, the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by AI technology. These developments are made to better the standard of medical decision making and the standard of treatment given to patients. Fuzzy boundaries, shifting sizes, and aberrations like hair or ruler lines all provide difficulties for automatic detection of skin lesions in dermoscopic images, slowing down the otherwise efficient process of diagnosing skin cancer. However, these difficulties may be conquered by employing image processing software. To address these issues, the authors of this paper provide a novel IMLT-DL model for intelligent dermoscopic image processing. Multi-level thresholding and deep learning are brought together in this model. Top hat filtering and inpainting have been included into IMLT-DL for use in image processing. In addition, Mayfly Optimization has been used in tandem with multilayer Kapur's thresholding to identify specific trouble spots. For further investigation, it uses an Inception v3-based feature extractor, and for data classification, it makes use of gradient boosting trees (GBTs). On the ISIC dataset, this model was shown to outperform state-of-the-art alternatives by a margin of 0.992% over the duration of trial iterations. These advances are a major step forwards in the quest for faster and more accurate skin lesion detection.

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.

According to high development of digital technologies, image processing is more and more import in various fields, such as robot navigator, images classifier. Current image processing model still need large amount of training data to tune processing model and can’t process large images effectively. The recognition successful rate was still not very satisfied. Therefore, this paper researched saliency prior based image processing model, present the Gaussian mixture process and design the feature point based classifier, and then evaluate the model by supervised learning process. Finally, a set of experiments were designed to demonstrate the effectiveness of this paper designed saliency prior based image processing model. The result shows the model works well with a better performance in accurate classification and lower time consumption

: 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.

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.