Integrated Image Processing Research Articles

Enhancement of SVM based MRI Brain Image Classification using Pre-Processing Techniques

Background/Objectives: The cases related to Brain Tumor has increased with respect to time owing to various reasons. One of the major challenging issues can be defined by integrating image processing along with data mining algorithms such as classification and clustering. Methods/Statistical Analysis: Artificial Intelligence and Machine Learning techniques are very useful for identifying and visualizing the tumor in the MRI brain image, which can be classified using Support Vector Machines (SVM). Findings: In this paper, we proposed SVM algorithm for classifying the images into two categories Benign and Malignant. SVM classifier model is implemented with RBF and SVM Kernels like linear and non-linear techniques. Application/Improvements: We also proposed for enhancing SVM based MRI Brain image classification by identifying the location and size of the tumor by using different segmentation techniques. The experiments are conducted for evaluating accuracy and the results are compared with existing and proposed work.

INDUSTRIE 4.0 - Automation in weft knitting technology

Industry 4.0 applies to the knitting industry. Regarding the knitting process retrofitting activities are executed mostly manually by an operator on the basis on the operator's experience. In doing so, the knitted fabric is not necessarily produced in the most efficient way regarding process speed and fabric quality aspects. The knitting division at ITA is concentrating on project activities regarding automation and Industry 4.0. ITA is working on analysing the correspondences of the knitting process parameters and their influence on the fabric quality. By using e.g. the augmented reality technology, the operator will be supported when setting up the knitting machine in case of product or pattern change - or in case of an intervention when production errors occur. Furthermore, the RFID-Technology offers great possibilities to ensure information flow between sub-processes of the fragmented textile process chain. ITA is using RFID-chips to save yarn production information and connect the information to the fabric producing machine control. In addition, ITA is currently working on integrating image processing systems into the large circular knitting machine in order to ensure online-quality measurement of the knitted fabrics. This will lead to a self-optimizing and selflearning knitting machine.

Using multi-remote sensing data to assess Phragmites invasion of the Detroit river international wildlife refuge

Purpose This paper aims to develope an integrated image processing method to investigate the spatiotemporal dynamics of Phragmites invasion in the Detroit River International Wildlife Refuge on the basis of publically available sources. Design/methodology/approach This new approach integrates the standard time-series analysis of Landsat images with USDA National Agriculture Imagery Program (NAIP) imagery and USGS Digital Orthophoto Quarter Quads (DOQQ) datasets, which are either classified or manually interpreted with the aid of ground control points. Three different types of spatiotemporal dimensions are designed to test this integrated time-series image analysis method: the selected sites and selected time-points with high spatial resolution and sufficient validation data points, the intermediate time-series with continued yearly images and periodical validation data, and the long time-series with periodical images without enough validation data. The support vector machine (SVM) method was used to classify the Landast TM sequence images to detect the Phragmites invasion. Findings The habitat map produced by NAIP images and field collection data shows that the total Phragmites area of DRIWR in 2010 is 4221.87 acres without treatment areas and similar with the removed non-vegetation method. It is confirmed that the pre-classification method can obtain more accurate results. Originality value The test results show that the Landsat-5 data can be used for long-term environmental management and monitoring of Phragmites invasion and can achieve rehabilitation of invasion areas.

A fourth-order seven-point cubature on regular hexagons

We investigate the central moments of (regular) hexagons and derive accordingly a discrete approximation to definite integrals on hexagons. The seven-point cubature rule makes use of interior and neighbor center nodes, and is of fourth order by construction. The result is expected to be useful in two-dimensional (open-field) applications of integral equations or image processing.

LithoVue™: A new single-use digital flexible ureteroscope.

LithoVue™: A new single-use digital flexible ureteroscope.

A Remote-Sensing-Driven System for Mining Marine Spatiotemporal Association Patterns

Remote sensing is widely used to analyze marine environments. While many effective and advanced methods have been developed, they are generally used independently of each other, despite the potential advantages of combining different modules into an integrated system. We develop here an image-driven remote-sensing mining system, RSMapMining (Remote Sensing driven Marine spatiotemporal Association Pattern Mining system), which consists of three modules. The image preprocessing module integrates image processing techniques and marine extraction methods to build a mining database. The pattern mining module integrates popular algorithms to implement the mining process according to the mining strategies. The third module, knowledge visualization, designs a series of interactive interfaces to visualize the marine data at a variety of scales, from global to grid pixel. The effectiveness of the integrated system is tested in a case study of the northwestern Pacific Ocean. The main contribution of this study is the development of a mining system to deal with marine remote sensing images by integrating popular techniques and methods ranging from information extraction, through visualization, to knowledge discovery.

An android based ecodriving assistance system to improve safety and efficiency of internal combustion engine passenger cars

Recent improvements of communication technologies leads to several innovations in road vehicles energy consumption. As an example, several ecodriving applications already appeared on all smartphone application markets. Using embedded smartphone signals, such applications provide real time feedback to drivers according to their performances. However most of these applications does not take into account upcoming events such as curves, slopes or crossings to advise the driver on the best actions to undertake to lower energy consumption. Furthermore, they do not analyze data coming from vehicle sensors. In this paper, we present an android application, developed within the FP7 European project ecoDriver, which provides several innovative properties: advice according to upcoming events, a real time evaluation of the driving behavior, the analysis of past actions, an interface with OBD2 connector and some more. This paper further develops the complete architecture and links between each innovative function. Future works will concentrate on integrating image processing in this application in order to detect the possible presence of a front vehicle.

Development of a Text-Data Based Learning Tool That Integrates Image Processing and Displaying

Development of a Text-Data Based Learning Tool That Integrates Image Processing and Displaying

Proses Produksi Pembuatan Tekstur Material pada Desain 3d Karakter Menggunakan Perangkat Lunak Maxon 3D Bodypaint

Digital production proses using integrated image editor software, which has own drawing tools function, makes easier producing textures material that applied in 3D model. The feature of image editor combined with 3D Editor Software makes the easier adjustment of 3D model needs when we see the visible improvement, so the software utilization will be more efficient. In the discussion, this study is done by making the production of 3D model, that is the 3D Character that has material texturing from utilizing the available image editor software features, so the alternative production by using the integrated image editor is possibly to be done. The discussion can be utilized as an insight into the manufacture of technical design in determining the design workflow of 3D models. Utilization of software take one example of software Maxon Cinema 4D version 14, which is used as a reference as software that integrates image processing therein.

Full Parallax Integral 3D Display and Image Processing Techniques

Purpose – Full parallax integral 3D display is one of the promising future displays that provide different perspectives according to viewing direction. In this paper, the authors review the recent integral 3D display and image processing techniques for improving the performance, such as viewing resolution, viewing angle, etc.Design/methodology/approach – Firstly, to improve the viewing resolution of 3D images in the integral imaging display with lenslet array, the authors present 3D integral imaging display with focused mode using the time-multiplexed display. Compared with the original integral imaging with focused mode, the authors use the electrical masks and the corresponding elemental image set. In this system, the authors can generate the resolution-improved 3D images with the n×n pixels from each lenslet by using n×n time-multiplexed display. Secondly, a new image processing technique related to the elemental image generation for 3D scenes is presented. With the information provided by the Kinect device, the array of elemental images for an integral imaging display is generated.Findings – From their first work, the authors improved the resolution of 3D images by using the time-multiplexing technique through the demonstration of the 24 inch integral imaging system. Authors’ method can be applied to a practical application. Next, the proposed method with the Kinect device can gain a competitive advantage over other methods for the capture of integral images of big 3D scenes. The main advantage of fusing the Kinect and the integral imaging concepts is the acquisition speed, and the small amount of handled data.Originality / Value – In this paper, the authors review their recent methods related to integral 3D display and image processing technique.Research type – general review.

Agricultural Plant Leaf Disease Detection and Diagnosis Using Image Processing Based on Morphological Feature Extraction

Leaf spots can be indicative of crop diseases, where leaf batches (spots) are usually examined and subjected to expert opinion. In our proposed system, we are going to develop an integrated image processing system to help automated inspection of these leaf batches and helps identify the disease type. Conventional Expert systems mainly those which used to diagnose the disease in agriculture domain depends only on textual input. Usually abnormalities for a given crop are manifested as symptoms on various plant parts. To enable an expert system to produce correct results, end user must be capable of mapping what they see in a form of abnormal symptoms to answer to questions asked by that expert system. This mapping may be inconsistent if a full understanding of the abnormalities does not exist. The proposed system consists of four stages; the first is the enhancement, which includes HIS transformation, histogram analysis, and intensity adjustment. The second stage is segmentation, which includes adaptation of fuzzy c-means algorithm. Feature extraction is the third stage, which deals with three features, namely color size and shape of spot. The fourth stage is classification, which comprises back propagation based neural networks.

Long-distance precision inspection method for bridge cracks with image processing

Abstract The detection of cracks is the most important step during the inspection of bridge substructures. However, traditional crack detection methods are subjective and expensive. Therefore, crack detection techniques based on image processing have been proposed. In this paper, a long-distance image acquisition device and an integrated image processing method are proposed for precisely extracting cracks. The proposed method consists of three parts. Firstly, general steps for crack extraction are realized which include image clipping, enhancement, smoothing, segmentation, crack marking and rotation. Secondly, the electronic distance measurement algorithm is approved to calculate the crack width in millimeters. Finally, an improved image segmentation algorithm based on C–V model is provided for crack extraction. We evaluate the proposed method on a collection of 1000 bridge images which have been gathered under different conditions. Experimental results show that the modified algorithm could effectively improve the detection precision rate and reduce the running time.

Contribution to the design of a CMOS image sensor with low-complexity video compression for wireless sensor networks

Demand for low-power sensing devices with integrated image processing capabilities is increasing, especially for resource-constrained systems such as WSNs. CMOS technology enables the integration of image sensing and image processing, which improves the overall system performance. The aim of this paper is to present and evaluate a smart image sensor integrating a user-driven video compression scheme designed to respect the energy constraints of image processing and transmission over WSNs. The interest of our solution is twofold. First, compression settings can be changed at run time depending on video characteristics. Second, compression is applied only on blocks that change significantly over time. This paper presents in details the internal hardware architecture of the proposed system and describes its performance, with relevant comparisons to some related works.

Integrated Image Processing and Path Planning for Robotic Sketching

Since the beginning of the development of machine vision, researchers have realized its importance in the robotics field, as it provides a useful tool for both the environment detection and decision making during the automation process. At the same time, path planning for robots influences many in the robotics and automation field and it has remained active for both methodology research and system implementation. This research combines machine vision with robot path planning with an aim of programming-free robotic applications. Particularly as a proof of concept, a programming-free robotic sketching prototype is developed as a case study. Within the context, this paper consists of three parts. The first part covers the processing of a facial image taken by a webcam to identify the contours that represent the image; the second part converts these contours to paths for an industrial robot to follow; and the third part controls the robot adaptively for sketching including auto-generation of control codes and self-calibration. The developed prototype is a closed-loop system with networked camera and robot. Intelligent computation is applied to identify the contours of the image with minimum representation of points and with the correct sequence of points for each curve (path); the sequence of the output robot paths represents the near-optimal sequence to preserve the minimum travelling time for the robot. The robot control module can also retrieve the TCP of the robot for off-site monitoring. The ultimate goal of this research is future applications of robot path following, e.g. ad-hoc robotic cutting or welding where the paths can be specified by hand-drawings of an operator on the target workpiece with zero programming for the operator.

Reverse Design and Application of Irregular Planar Part Based on Image Processing

By using a computer vision detection system to obtain high resolution images of a machine part, a kind of reverse design method of solid modeling of irregular planar part with aided implementation of computer vision was proposed in this paper, which integrates image processing function of Matlab software with solid modeling function of computer aided design (CAD) software. The method used a calibrated digital camera to get the image of the tested part, a three-dimensional entity vector model may be built up after image inversion, edge detection, vectorization process of binary image and size matching were operated sequentially. The results of image reverse design showed that it is an easy and convenient way to reverse irregular planar parts based on image processing. One of its remarkable advantages is the saving of design period and the reduction of design cost. Its measurement error can be controlled within 0.1 mm, and can meet general precision requirement of application occasions. Reversed parts may provide a model basis for further analysis on mechanism assembling and motion simulation.

Lucrative Method for License Plate Recognition

Recent research initiatives have addressed the need for improved performance of license plate recognition accuracy that would profit many applications, ITS in particular. Different image processing techniques have been implemented for this purpose specifically edge detection, binarization, segmentation algorithm and tesseract. Each of these steps has its own strengths and weaknesses and it has been proved that using each of these methods individually does not provide a suitable solution that answers today's character recognition needs. This project endeavors to provide improved license plate recognition accuracy by using a novel approach that involves integrating image processing techniques and tesseract according to some constraints. Experimental results prove that this integration provides better recognition accuracy than using traditional techniques for license plate recognition.

Robust integral image rectification framework using perspective transformation supported by statistical line segment clustering

In most integral image analysis and processing tasks, accurate knowledge of the internal image structure is required. In this paper we present a robust framework for the accurate rectification of perspectively distorted integral images based on multiple line segment detection. The use of multiple line segments increases the overall fault tolerance of our framework providing strong statistical support for the rectification process. The proposed framework is used for the automatic rectification, metric correction, and rotation of distorted integral images. The performance of our framework is assessed over a number of integral images with varying scene complexity and noise levels.

ApoTome to visualize E‐cadherin and p63 expression in oral pre‐cancer

Precise histopathological localization of E-cadherin and p63 is of immense importance in understanding the integrity of oral mucosal stratified epithelium in normal and diseased conditions. Necessarily immunohistochemical imaging should have minimum bleaching impact on the dyes and ability to produce clear and crisp images. Here ApoTome provides an alternative with metal halide light source and structured illumination under the assistance of grids, along with integrated image processing modality to generate crisp images with digital interface. The current study demonstrates the applicability of such microscopic system in capturing fluorescence images of immunohistochemical sections of normal and precancerous biopsies in respect to the expression of p63 and E-cadherin in the epithelial cells. The ApoTome images localize the nuclear and membranous expressions of p63 and E-cadherin, respectively, with remarkable specificity. The findings on E-cadherin expression have enormous diagnostic significance as these images clearly differentiate the early and advanced stages of oral submucous fibrosis based on their cytoplasmic and membranous location. Thus, this study clearly depicts a remarkable performance of ApoTome with diagnostic significance.

Detection of pore space in CT soil images using artificial neural networks

Abstract. Computed Tomography (CT) images provide a non-invasive alternative for observing soil structures, particularly pore space. Pore space in soil data indicates empty or free space in the sense that no material is present there except fluids such as air, water, and gas. Fluid transport depends on where pore spaces are located in the soil, and for this reason, it is important to identify pore zones. The low contrast between soil and pore space in CT images presents a problem with respect to pore quantification. In this paper, we present a methodology that integrates image processing, clustering techniques and artificial neural networks, in order to classify pore space in soil images. Image processing was used for the feature extraction of images. Three clustering algorithms were implemented (K-means, Fuzzy C-means, and Self Organising Maps) to segment images. The objective of clustering process is to find pixel groups of a similar grey level intensity and to organise them into more or less homogeneous groups. The segmented images are used for test a classifier. An Artificial Neural Network is characterised by a great degree of modularity and flexibility, and it is very efficient for large-scale and generic pattern recognition applications. For these reasons, an Artificial Neural Network was used to classify soil images into two classes (pore space and solid soil). Our methodology shows an alternative way to detect solid soil and pore space in CT images. The percentages of correct classifications of pore space of the total number of classifications among the tested images were 97.01%, 96.47% and 96.12%.

Toward a Fully Automated High-Throughput Phototransfection System

We have designed and implemented a framework for creating a fully automated high-throughput phototransfection system. Integrated image processing, laser target position calculation, and stage movements show a throughput increase of > 23X over the current manual phototransfection method while the potential for even greater throughput improvements (> 110X) is described. A software tool for automated off-line single cell morphological measurements, as well as real-time image segmentation analysis, has also been constructed and shown to be able quantify changes in the cell before and after the process, successfully characterizing them, using metrics such as cell perimeter, area, major and minor axis length, and eccentricity values.