This document discusses image retrieval techniques for content-based image retrieval systems. It begins with an introduction to the growth of digital image collections and the need for large-scale image retrieval systems. It then reviews different features used for image retrieval, such as color histograms, color moments, color coherence vectors, and discrete wavelet transforms. Edge features and corner features are also discussed. The document concludes that using only one feature type such as color or texture is not sufficient, and the best approach is to extract multiple high-quality features and combine them for image retrieval.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
Query Image Searching With Integrated Textual and Visual Relevance Feedback f...IJERA Editor
There are many researchers who have studied the relevance feedback in the literature of content based image
retrieval (CBIR) community, but none of CBIR search engines support it because of scalability, effectiveness
and efficiency issues. In this, we had implemented an integrated relevance feedback for retrieving of web
images. Here, we had concentrated on integration of both textual features (TF) and visual features (VF) based
relevance feedback (RF), simultaneously we also tested them individually. The TFRF employs and effective
search result clustering (SRC) algorithm to get salient phrases. Then a new user interface (UI) is proposed to
support RF. Experimental results show that the proposed algorithm is scalable, effective and accurated
A Review of Feature Extraction Techniques for CBIR based on SVMIJEEE
As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
Query Image Searching With Integrated Textual and Visual Relevance Feedback f...IJERA Editor
There are many researchers who have studied the relevance feedback in the literature of content based image
retrieval (CBIR) community, but none of CBIR search engines support it because of scalability, effectiveness
and efficiency issues. In this, we had implemented an integrated relevance feedback for retrieving of web
images. Here, we had concentrated on integration of both textual features (TF) and visual features (VF) based
relevance feedback (RF), simultaneously we also tested them individually. The TFRF employs and effective
search result clustering (SRC) algorithm to get salient phrases. Then a new user interface (UI) is proposed to
support RF. Experimental results show that the proposed algorithm is scalable, effective and accurated
A Review of Feature Extraction Techniques for CBIR based on SVMIJEEE
As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase.
The content based Image Retrieval is the restoration of images with respect to the visual appearances
like texture, shape and color.The methods, components and the algorithms adopted in this content based
retrieval of images were commonly derived from the areas like pattern identification, signal progressing
and the computer vision. Moreover the shape and the color features were abstracted in the course of
wavelet transformation and color histogram. Thus the new content based retrieval is proposed in this
research paper.In this paper the algorithms were required to propose with regards to the shape, shade and
texture feature abstraction .The concept of discrete wavelet transform to be implemented in order to
compute the Euclidian distance.The calculation of clusters was made with the help of the modified KMeans
clustering technique. Thus the analysis is made in among the query image and the database
image.The MATLAB software is implemented to execute the queries. The K-Means of abstraction is
proposed by performing fragmentation and grid-means module, feature extraction and K- nearest neighbor
clustering algorithms to construct the content based image retrieval system.Thus the obtained result are
made to compute and compared to all other algorithm for the retrieval of quality image features
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
relevant image from an outsized database. Textile images showed the way for the development of CBIR. It
establishes the efficient combination of color, shape and texture features. Here the textile image is given as
dataset. The images in database are loaded. The resultant image is given as input to feature extraction
technique which is transformation of input image into a set of features such as color, texture and shape.
The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color
feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel
technique. These algorithms are used to calculate the similarity between extracted features. These features
are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification
techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by
splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
The development of multimedia system technology in Content based Image Retrieval (CBIR) System is
one in every of the outstanding area to retrieve the images from an oversized collection of database. The feature
vectors of the query image are compared with feature vectors of the database images to get matching images.It is
much observed that anyone algorithm isn't beneficial in extracting all differing kinds of natural images. Thus an
intensive analysis of certain color, texture and shape extraction techniques are allotted to spot an efficient CBIR
technique that suits for a selected sort of images. The Extraction of an image includes feature description and
feature extraction. During this paper, we tend to projected Color Layout Descriptor (CLD), grey Level Co-
Occurrences Matrix (GLCM), Marker-Controlled Watershed Segmentation feature extraction technique that
extract the matching image based on the similarity of Color, Texture and shape within the database. For
performance analysis, the image retrieval timing results of the projected technique is calculated and compared
with every of the individual feature.
Design of an Effective Method for Image RetrievalAM Publications
Image retrieval has been highly concussed in major sectors and systems. The relevance feedback technique is
commonly used for analyzing the images for maintaining secure systems. It covers the wide range of techniques in
supporting user information and improving the facilities for image retrieval. In this paper, relevance feedback with respect
to Self-Organizing Maps (SOM) has been discussed .Comparative results show the high impact of relevance feedback in image retrieval.
This is a paper I wrote as part of my seminar "Inverse Problems in Computer Vision" while pursuing my M.Sc Medical Engineering at FAU, Erlangen, Germany.
The paper details a state-of-the-art method used for Single Image Super Resolution using Deep Convolutional Networks and the possible extensions to the original approach by considering compression and noise artifacts.
A comparative analysis of retrieval techniques in content based image retrievalcsandit
Basic group of visual techniques such as color, shape, texture are used in Content Based Image
Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in
image database. To improve query result, relevance feedback is used many times in CBIR to
help user to express their preference and improve query results. In this paper, a new approach
for image retrieval is proposed which is based on the features such as Color Histogram, Eigen
Values and Match Point. Images from various types of database are first identified by using
edge detection techniques .Once the image is identified, then the image is searched in the
particular database, then all related images are displayed. This will save the retrieval time.
Further to retrieve the precise query image, any of the three techniques are used and
comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as
compared with other two techniques.
I MAGE S UBSET S ELECTION U SING G ABOR F ILTERS A ND N EURAL N ETWORKSijma
An automatic method for the selection of subsets of
images, both modern and historic, out of a set of
landmark large images collected from the Internet i
s presented in this paper. This selection depends o
n the
extraction of dominant features using Gabor filteri
ng. Features are selected carefully from a prelimin
ary
image set and fed into a neural network as a traini
ng data. The method collects a large set of raw lan
dmark
images containing modern and historic landmark imag
es and non-landmark images. The method then
processes these images to classify them as landmark
and non-landmark images. The classification
performance highly depends on the number of candida
te features of the landmark.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
A COMPARATIVE ANALYSIS OF RETRIEVAL TECHNIQUES IN CONTENT BASED IMAGE RETRIEVALcscpconf
Basic group of visual techniques such as color, shape, texture are used in Content Based Image Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in image database. To improve query result, relevance feedback is used many times in CBIR to help user to express their preference and improve query results. In this paper, a new approach for image retrieval is proposed which is based on the features such as Color Histogram, Eigen Values and Match Point. Images from various types of database are first identified by using edge detection techniques .Once the image is identified, then the image is searched in the particular database, then all related images are displayed. This will save the retrieval time. Further to retrieve the precise query image, any of the three techniques are used and comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as compared with other two techniques.
Due to recent development in technology, there is an increase in the usage of digital cameras, smartphones, and Internet. The shared and stored multimedia data are growing, and to search or to retrieve a relevant image from an archive is a challenging research problem. The fundamental need of any image retrieval model is to search and arrange the images that are in a visual semantic re- lationship with the query given by the user Content Based Image Retrieval Project.
http://takeoffprojects.com/content-based-image-retrieval-project
We are providing you with some of the greatest ideas for building Final Year projects with proper guidance and assistance
The content based Image Retrieval is the restoration of images with respect to the visual appearances
like texture, shape and color.The methods, components and the algorithms adopted in this content based
retrieval of images were commonly derived from the areas like pattern identification, signal progressing
and the computer vision. Moreover the shape and the color features were abstracted in the course of
wavelet transformation and color histogram. Thus the new content based retrieval is proposed in this
research paper.In this paper the algorithms were required to propose with regards to the shape, shade and
texture feature abstraction .The concept of discrete wavelet transform to be implemented in order to
compute the Euclidian distance.The calculation of clusters was made with the help of the modified KMeans
clustering technique. Thus the analysis is made in among the query image and the database
image.The MATLAB software is implemented to execute the queries. The K-Means of abstraction is
proposed by performing fragmentation and grid-means module, feature extraction and K- nearest neighbor
clustering algorithms to construct the content based image retrieval system.Thus the obtained result are
made to compute and compared to all other algorithm for the retrieval of quality image features
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
relevant image from an outsized database. Textile images showed the way for the development of CBIR. It
establishes the efficient combination of color, shape and texture features. Here the textile image is given as
dataset. The images in database are loaded. The resultant image is given as input to feature extraction
technique which is transformation of input image into a set of features such as color, texture and shape.
The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color
feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel
technique. These algorithms are used to calculate the similarity between extracted features. These features
are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification
techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by
splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
The development of multimedia system technology in Content based Image Retrieval (CBIR) System is
one in every of the outstanding area to retrieve the images from an oversized collection of database. The feature
vectors of the query image are compared with feature vectors of the database images to get matching images.It is
much observed that anyone algorithm isn't beneficial in extracting all differing kinds of natural images. Thus an
intensive analysis of certain color, texture and shape extraction techniques are allotted to spot an efficient CBIR
technique that suits for a selected sort of images. The Extraction of an image includes feature description and
feature extraction. During this paper, we tend to projected Color Layout Descriptor (CLD), grey Level Co-
Occurrences Matrix (GLCM), Marker-Controlled Watershed Segmentation feature extraction technique that
extract the matching image based on the similarity of Color, Texture and shape within the database. For
performance analysis, the image retrieval timing results of the projected technique is calculated and compared
with every of the individual feature.
Design of an Effective Method for Image RetrievalAM Publications
Image retrieval has been highly concussed in major sectors and systems. The relevance feedback technique is
commonly used for analyzing the images for maintaining secure systems. It covers the wide range of techniques in
supporting user information and improving the facilities for image retrieval. In this paper, relevance feedback with respect
to Self-Organizing Maps (SOM) has been discussed .Comparative results show the high impact of relevance feedback in image retrieval.
This is a paper I wrote as part of my seminar "Inverse Problems in Computer Vision" while pursuing my M.Sc Medical Engineering at FAU, Erlangen, Germany.
The paper details a state-of-the-art method used for Single Image Super Resolution using Deep Convolutional Networks and the possible extensions to the original approach by considering compression and noise artifacts.
A comparative analysis of retrieval techniques in content based image retrievalcsandit
Basic group of visual techniques such as color, shape, texture are used in Content Based Image
Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in
image database. To improve query result, relevance feedback is used many times in CBIR to
help user to express their preference and improve query results. In this paper, a new approach
for image retrieval is proposed which is based on the features such as Color Histogram, Eigen
Values and Match Point. Images from various types of database are first identified by using
edge detection techniques .Once the image is identified, then the image is searched in the
particular database, then all related images are displayed. This will save the retrieval time.
Further to retrieve the precise query image, any of the three techniques are used and
comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as
compared with other two techniques.
I MAGE S UBSET S ELECTION U SING G ABOR F ILTERS A ND N EURAL N ETWORKSijma
An automatic method for the selection of subsets of
images, both modern and historic, out of a set of
landmark large images collected from the Internet i
s presented in this paper. This selection depends o
n the
extraction of dominant features using Gabor filteri
ng. Features are selected carefully from a prelimin
ary
image set and fed into a neural network as a traini
ng data. The method collects a large set of raw lan
dmark
images containing modern and historic landmark imag
es and non-landmark images. The method then
processes these images to classify them as landmark
and non-landmark images. The classification
performance highly depends on the number of candida
te features of the landmark.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
A COMPARATIVE ANALYSIS OF RETRIEVAL TECHNIQUES IN CONTENT BASED IMAGE RETRIEVALcscpconf
Basic group of visual techniques such as color, shape, texture are used in Content Based Image Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in image database. To improve query result, relevance feedback is used many times in CBIR to help user to express their preference and improve query results. In this paper, a new approach for image retrieval is proposed which is based on the features such as Color Histogram, Eigen Values and Match Point. Images from various types of database are first identified by using edge detection techniques .Once the image is identified, then the image is searched in the particular database, then all related images are displayed. This will save the retrieval time. Further to retrieve the precise query image, any of the three techniques are used and comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as compared with other two techniques.
Due to recent development in technology, there is an increase in the usage of digital cameras, smartphones, and Internet. The shared and stored multimedia data are growing, and to search or to retrieve a relevant image from an archive is a challenging research problem. The fundamental need of any image retrieval model is to search and arrange the images that are in a visual semantic re- lationship with the query given by the user Content Based Image Retrieval Project.
http://takeoffprojects.com/content-based-image-retrieval-project
We are providing you with some of the greatest ideas for building Final Year projects with proper guidance and assistance
WEB IMAGE RETRIEVAL USING CLUSTERING APPROACHEScscpconf
Image retrieval system is an active area to propose a new approach to retrieve images from the
large image database. In this concerned, we proposed an algorithm to represent images using
divisive based and partitioned based clustering approaches. The HSV color component and Haar wavelet transform is used to extract image features. These features are taken to segment an image to obtain objects. For segmenting an image, we used modified k-means clustering algorithm to group similar pixel together into K groups with cluster centers. To modify Kmeans, we proposed a divisive based clustering algorithm to determine the number of cluster and get back with number of cluster to k-means to obtain significant object groups. In addition, we also discussed the similarity distance measure using threshold value and object uniqueness to quantify the results.
A novel Image Retrieval System using an effective region based shape represen...CSCJournals
With recent improvements in methods for the acquisition and rendering of shapes, the need for retrieval of shapes from large repositories of shapes has gained prominence. A variety of methods have been proposed that enable the efficient querying of shape repositories for a desired shape or image. Many of these methods use a sample shape as a query and attempt to retrieve shapes from the database that have a similar shape. This paper introduces a novel and efficient shape matching approach for the automatic identification of real world objects. The identification process is applied on isolated objects and requires the segmentation of the image into separate objects, followed by the extraction of representative shape signatures and the similarity estimation of pairs of objects considering the information extracted from the segmentation process and shape signature. We compute a 1D shape signature function from a region shape and use it for region shape representation and retrieval through similarity estimation. The proposed region shape feature is much more efficient to compute than other region shape techniques invariant to image transformation.
SIGNIFICANCE OF DIMENSIONALITY REDUCTION IN IMAGE PROCESSING sipij
The aim of this paper is to present a comparative study of two linear dimension reduction methods namely
PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The main idea of PCA is to
transform the high dimensional input space onto the feature space where the maximal variance is
displayed. The feature selection in traditional LDA is obtained by maximizing the difference between
classes and minimizing the distance within classes. PCA finds the axes with maximum variance for the
whole data set where LDA tries to find the axes for best class seperability. The neural network is trained
about the reduced feature set (using PCA or LDA) of images in the database for fast searching of images
from the database using back propagation algorithm. The proposed method is experimented over a general
image database using Matlab. The performance of these systems has been evaluated by Precision and
Recall measures. Experimental results show that PCA gives the better performance in terms of higher
precision and recall values with lesser computational complexity than LDA
An Impact on Content Based Image Retrival A Perspective Viewijtsrd
The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content based image retrieval CBIR , which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content based image retrieval in the last decade. We conclude with several promising directions for future research. Shivanshu Jaiswal | Dr. Avinash Sharma ""An Impact on Content Based Image Retrival: A Perspective View"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd29969.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/29969/an-impact-on-content-based-image-retrival-a-perspective-view/shivanshu-jaiswal
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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"Trans Failsafe Prog" on your BMW X5 indicates potential transmission issues requiring immediate action. This safety feature activates in response to abnormalities like low fluid levels, leaks, faulty sensors, electrical or mechanical failures, and overheating.
𝘼𝙣𝙩𝙞𝙦𝙪𝙚 𝙋𝙡𝙖𝙨𝙩𝙞𝙘 𝙏𝙧𝙖𝙙𝙚𝙧𝙨 𝙞𝙨 𝙫𝙚𝙧𝙮 𝙛𝙖𝙢𝙤𝙪𝙨 𝙛𝙤𝙧 𝙢𝙖𝙣𝙪𝙛𝙖𝙘𝙩𝙪𝙧𝙞𝙣𝙜 𝙩𝙝𝙚𝙞𝙧 𝙥𝙧𝙤𝙙𝙪𝙘𝙩𝙨. 𝙒𝙚 𝙝𝙖𝙫𝙚 𝙖𝙡𝙡 𝙩𝙝𝙚 𝙥𝙡𝙖𝙨𝙩𝙞𝙘 𝙜𝙧𝙖𝙣𝙪𝙡𝙚𝙨 𝙪𝙨𝙚𝙙 𝙞𝙣 𝙖𝙪𝙩𝙤𝙢𝙤𝙩𝙞𝙫𝙚 𝙖𝙣𝙙 𝙖𝙪𝙩𝙤 𝙥𝙖𝙧𝙩𝙨 𝙖𝙣𝙙 𝙖𝙡𝙡 𝙩𝙝𝙚 𝙛𝙖𝙢𝙤𝙪𝙨 𝙘𝙤𝙢𝙥𝙖𝙣𝙞𝙚𝙨 𝙗𝙪𝙮 𝙩𝙝𝙚 𝙜𝙧𝙖𝙣𝙪𝙡𝙚𝙨 𝙛𝙧𝙤𝙢 𝙪𝙨.
Over the 10 years, we have gained a strong foothold in the market due to our range's high quality, competitive prices, and time-lined delivery schedules.
Symptoms like intermittent starting and key recognition errors signal potential problems with your Mercedes’ EIS. Use diagnostic steps like error code checks and spare key tests. Professional diagnosis and solutions like EIS replacement ensure safe driving. Consult a qualified technician for accurate diagnosis and repair.
What Exactly Is The Common Rail Direct Injection System & How Does It WorkMotor Cars International
Learn about Common Rail Direct Injection (CRDi) - the revolutionary technology that has made diesel engines more efficient. Explore its workings, advantages like enhanced fuel efficiency and increased power output, along with drawbacks such as complexity and higher initial cost. Compare CRDi with traditional diesel engines and discover why it's the preferred choice for modern engines.
Things to remember while upgrading the brakes of your carjennifermiller8137
Upgrading the brakes of your car? Keep these things in mind before doing so. Additionally, start using an OBD 2 GPS tracker so that you never miss a vehicle maintenance appointment. On top of this, a car GPS tracker will also let you master good driving habits that will let you increase the operational life of your car’s brakes.