Color Clustering in the Metal Inscription Images Using ANFIS Filter

Ancient inscriptions are historical records of the past age made on stone or metal media. Currently many ancient inscriptions were damaged because it is too long buried in the ground. This research is the first step to repairing the damaged inscription using Image processing. Efforts to restorations using color clustering with ANFIS method are an early stage to perform letters segmentation in the ancient inscription. The Results of ANFIS clustering method are compared to the spatial fuzzy clustering method (SFCM). The clustering performance measurement is done by measuring root mean square error (RMSE). From RMSE measurements, the average values obtained with ANFIS clustering method is smaller 21.80% than with SFCM. This means there is an increase in clustering performance with ANFIS method compared to SFCM.

A cascaded classification-segmentation reversible system for computer-aided detection and cells counting in microscopic peripheral blood smear basophils and eosinophils images

Computer-aided image analysis has a pivotal role in automated counting and classification of white blood cells (WBCs) in peripheral blood images. Due to their different characteristics, our proposed approach is based on investigating the variations between the basophils and eosinophils in terms of their color histogram, size, and shape before performing the segmentation process. Accordingly, we proposed a cascaded system using a classification-based segmentation process, called classification-segmentation reversible system (CSRS). Prior to applying the CSRS system, a Histogram-based Object to Background Disparity (HOBD) metric was deduced to determine the most appropriate color plane for performing the initial WBC detection (first segmentation). Investigating the local histogram features of both classes resulted in a 92.4% initial classification accuracy using the third-degree polynomial support vector machine (SVM) method. Subsequently, in the proposed CSRS approach, transformation-based segmentation algorithms were developed to fit the specific requirements of each of the two predicted classes. The proposed CSRS system is used, where the images from an initial classification process are fed into a second segmentation process for each class separately. The segmentation results demonstrated a similarity index of 94.9% for basophils, and 94.1% for eosinophils. Moreover, an average counting accuracy of 97.4% for both classes was achieved. In addition, a second classification was carried out after applying the CSRS, achieving a 5.2% increase in accuracy compared to the initial classification process

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

A novel RGBW pixel for LED displays

In this work, a novel pixel configuration RGBW, consisting of red (R), green (G), blue (B), and white (W) LEDs, is employed and investigated for color generation. Energy consumption and various hues of new pixels are compared to standard pixels consisting of RGB LEDs. Human perception experiments are conducted in order to study the perceptual difference between the two architectures when the same colors are generated using RGBW vs. RGB. Power measurements for an 8x8 pixel LED display has demonstrated up to 49% power savings for gray scale, over 30% power savings for low saturated colors, and up to 12% for high saturated colors using RGBW as an alternative. Furthermore, human perception studies has shown that vast majority of test subjects could not distinguish between most colors displayed using RGB and RGBW showing that RGBW is an excellent substitute for RGB. Statistics has shown that 44% of test subjects found the colors in gray scale to be the same, whereas 82% and 95% of test subject found low saturated colors and high saturated colors, respectively, to be identical

An Overview of Advances of Pattern Recognition Systems in Computer Vision

26 pagesFirst of all, let's give a tentative answer to the following question: what is pattern recognition (PR)? Among all the possible existing answers, that which we consider being the best adapted to the situation and to the concern of this chapter is: "pattern recognition is the scientific discipline of machine learning (or artificial intelligence) that aims at classifying data (patterns) into a number of categories or classes". But what is a pattern? A pattern recognition system (PRS) is an automatic system that aims at classifying the input pattern into a specific class. It proceeds into two successive tasks: (1) the analysis (or description) that extracts the characteristics from the pattern being studied and (2) the classification (or recognition) that enables us to recognise an object (or a pattern) by using some characteristics derived from the first task

Video content analysis for intelligent forensics

The networks of surveillance cameras installed in public places and private territories continuously record video data with the aim of detecting and preventing unlawful activities. This enhances the importance of video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis. In this thesis, the primary focus is on four key aspects of video content analysis, namely; 1. Moving object detection and recognition, 2. Correction of colours in the video frames and recognition of colours of moving objects, 3. Make and model recognition of vehicles and identification of their type, 4. Detection and recognition of text information in outdoor scenes. To address the first issue, a framework is presented in the first part of the thesis that efficiently detects and recognizes moving objects in videos. The framework targets the problem of object detection in the presence of complex background. The object detection part of the framework relies on background modelling technique and a novel post processing step where the contours of the foreground regions (i.e. moving object) are refined by the classification of edge segments as belonging either to the background or to the foreground region. Further, a novel feature descriptor is devised for the classification of moving objects into humans, vehicles and background. The proposed feature descriptor captures the texture information present in the silhouette of foreground objects. To address the second issue, a framework for the correction and recognition of true colours of objects in videos is presented with novel noise reduction, colour enhancement and colour recognition stages. The colour recognition stage makes use of temporal information to reliably recognize the true colours of moving objects in multiple frames. The proposed framework is specifically designed to perform robustly on videos that have poor quality because of surrounding illumination, camera sensor imperfection and artefacts due to high compression. In the third part of the thesis, a framework for vehicle make and model recognition and type identification is presented. As a part of this work, a novel feature representation technique for distinctive representation of vehicle images has emerged. The feature representation technique uses dense feature description and mid-level feature encoding scheme to capture the texture in the frontal view of the vehicles. The proposed method is insensitive to minor in-plane rotation and skew within the image. The capability of the proposed framework can be enhanced to any number of vehicle classes without re-training. Another important contribution of this work is the publication of a comprehensive up to date dataset of vehicle images to support future research in this domain. The problem of text detection and recognition in images is addressed in the last part of the thesis. A novel technique is proposed that exploits the colour information in the image for the identification of text regions. Apart from detection, the colour information is also used to segment characters from the words. The recognition of identified characters is performed using shape features and supervised learning. Finally, a lexicon based alignment procedure is adopted to finalize the recognition of strings present in word images. Extensive experiments have been conducted on benchmark datasets to analyse the performance of proposed algorithms. The results show that the proposed moving object detection and recognition technique superseded well-know baseline techniques. The proposed framework for the correction and recognition of object colours in video frames achieved all the aforementioned goals. The performance analysis of the vehicle make and model recognition framework on multiple datasets has shown the strength and reliability of the technique when used within various scenarios. Finally, the experimental results for the text detection and recognition framework on benchmark datasets have revealed the potential of the proposed scheme for accurate detection and recognition of text in the wild

Modern Seed Technology

Satisfying the increasing number of consumer demands for high-quality seeds with enhanced performance is one of the most imperative challenges of modern agriculture. In this view, it is essential to remember that the seed quality of crops does not improve

Tow-row license plate extraction based on CIELab color space in a digital image

A vehicle license plate recognition system extracts plate information from a digital image through image processing technology. License plate extraction (LPE) is the premise and key concept of license plate recognition systems. In China, two-row license plates are yellow and dirty. Thus, a novel license plate extraction method based on CIELab color space is proposed in this paper. The method involves transforming a digital image from RGB color space to CIELab color space and using the b and a channels of CIELab color space to obtain the yellow areas of the digital image. Morphological operations are implemented to filter out noise and identify the license plate candidate areas. Lastly, texture features are utilized to locate the real license plate areas. Experiments indicate that the proposed method makes full use of digital image information to rapidly extract two-row license plate areas in a complicated environment