ORIGINAL_ARTICLE
Automatic mineral segmentation in petrographic thin sections using image processing and clustering algorithms
Mineral segmentation in thin sections based on image processing algorithms is one of the popular research topics geosciences. Rocks are the main information resource for geological studies, and mounting thin section from rocks is the most popular method for studying them. Mineral segmentation in thin sections is also the pre-step for further studying on thin sections such as mineral identification and measuring the size of minerals. In this paper, a new method for mineral segmentation based on image processing and clustering algorithms is proposed for mineral segmentation in thin sections. In order to segment minerals, using a polarizer microscope, two images in plane and cross polarized lights are captured from each thin sections, and by extracting the color features from the images, minerals inside each thin section are segmented. Therefore, initially, the color features including RGB and HSI components are extracted for each pixels for both images, and then using image processing and clustering algorithms the pixels are clustered and each cluster is related to a segmented mineral. Experimental results indicate that the proposed algorithm produces accurate and reliable results, especially for those thin sections containing altered minerals. The proposed algorithm can be used in such applications as petroleum geology, mineralogy training and NASA mars exploration.
https://jmvip.sinaweb.net/article_10452_d6a4f3499be1c48230cebffe004a2555.pdf
2016-01-21
1
13
Mineral segmentation
Thin Sections
Color clustering
Digital Image Processing
RGB and HSI color spaces
Hossein
Izadi
hossein.izadi@ut.ac.ir
1
School of Mining Engineering, University of Tehran
LEAD_AUTHOR
Javad
Sadri
javad.sadri@cs.mcgill.ca
2
School of Electrical and Computer Engineering, University of Birjand
AUTHOR
Nosrat
Agha Mehran
namehran@birjand.ac.ir
3
School of Mining Engineering, University of Birjand
AUTHOR
ORIGINAL_ARTICLE
Adaptive Contrast Enhancement using Optimal Equalization of 2-Dimensional Histogram
In this paper, an adaptive image contrast enhancement algorithm based on an optimization problem in two dimensional histogram domain is presented. To reduce the unwanted effects of the histogram adjustment, through this optimization-similar to the other methods- the 2D histogram of enhanced image is found in close proximity to input image histogram and uniform distribution, simultaneously. In addition, different from the other methods, by adaptive adjusting the components of a weight matrix, local information is counted. Experimental results in the quantitative and qualitative assessments on a wide range of images demonstrate the performance of the proposed method. Tests have shown that with the addition of the adaptive adjusting the weights, the average performance in contrast enhancement increases 75 and 3 percent from the viewpoint of the AMBE_N and DE_N, respectively.
https://jmvip.sinaweb.net/article_10150_f604ff73ce123fec046088980f5c24fb.pdf
2016-01-21
15
24
Contrast Enhancement
2-Dimensional Histogram
Histogram Equalization
Sahar
Iravani
s.iravani@stu.ac.ir
1
Faculty of Electrical and Computer Engineering, Babol University of Technology
AUTHOR
Mehdi
Ezoji
m.ezoji@nit.ac.ir
2
Faculty of Electrical and Computer Engineering, Babol University of Technology
LEAD_AUTHOR
ORIGINAL_ARTICLE
Low bit rate compression of fingerprint images for maintaining or improving verification performance
In this paper we propose a fingerprint image compression method based on the wavelet transform and SPIHT coder. The proposed method employs the proposed technique of dynamic range reduction which benefits from the bimodality of fingerprint images in order to improve the compression efficiency. In addition, we utilized some image enhancement techniques to alleviate the leakage effect as well as further improve the compression efficiency. We have investigated the impacts of compression on recognition efficiency of compressed images. In this investigation we proposed two new measures of breakdown point and downfall slope based on the recognition accuracy curve versus compression bit rate, in order to evaluate the fingerprint image compression approaches more sophisticatedly. Experimental results show that the proposed technique of dynamic range reduction decreased the breakdown point by 0.05 bpp in average. The proposed image enhancement techniques improved the recognition accuracy up to 5% at all compression bit rates higher than the breakdown point. It also decreased the downfall slope. Regarding the PSNR performance, the proposed method outperformed the JPEG2000 and WSQ approaches by 0.8 dB, in average.
https://jmvip.sinaweb.net/article_11539_457ef5aafbc61f32a49512d8b471b008.pdf
2016-01-21
25
38
Fingerprint image compression
SPIHT
verification performance
compression performance
image enhancement
Hadi
Grailu
grailu@shahroodut.ac.ir
1
University of Shahrood, Electrical and Robotics Engineering Department
AUTHOR
ORIGINAL_ARTICLE
Improving the Performance of Predictive Encoder by Changing the Image Content Arrangement Using Genetic Algorithm
Image compressiontechniquescan bedividedinto two categoriesoflossyandlossless. Predictiveencoder isthe basis of many losslessimagecompression methods. Thisencoder predictsthe valuesofimage pixelsusing theirneighboringpixelsvalues. The difference betweenthe actual valueand thepredictedvalue of each pixelis consideredthe errorandthese error valuesarecoded.In this paper, a pre-processingmethodis proposed tochangethe image content arrangementsothatthe correlations between the neighboring pixelsincrease.By increasing the correlation between neighboring pixels, predictive encoder can more accurately predict the value of each pixel, as a result, entropy is reduced in the error image. According toinformation theory, thelower the imageentropy leads to the higher the capability of theentropy encoderinitscompression.In the proposed method,using the genetic algorithm, an appropriate geometrictransformationof rotationandreflectionis appliedoneach blockofthe image to strengthen the correlation between neighboring pixels.Inthispaper, twocompression methods,losslessJPEG andCALIC that are based on predictivecodingareevaluated.The evaluation results ofthe proposed methodonmultiple imagesshow thattheproposedpre-processingmethodimprovesthecompressionrate of these two methods.
https://jmvip.sinaweb.net/article_11633_4dd2a7700ee643572829b74a5ef7331d.pdf
2016-01-21
39
49
Image Compression
Lossless JPEG
CALIC
Geometric transform
Predictive encoder
Sekine
Asadi Amiri
asadi_amiri@yahoo.com
1
Faculty of Computer Engineering, University of Shahrood
LEAD_AUTHOR
Hamid
Hassanpour
h.hassanpour@shahroodut.ac.ir
2
Faculty of Computer Engineering, University of Shahrood
AUTHOR
ORIGINAL_ARTICLE
Optimal Multilevel image thresholding using the teaching-learning-based optimization
Image thresholding is a popular method for image segmentation. Histogram is used for image segmentation in image thresholding. In this paper, a multilevel image thresholding is proposed based on teaching-learning-based optimization (TLBO). TLBO is a new population-based metaheuristic inspired by learners and teacher in a classroom. The optimal thresholds are found by maximizing Kapur’s (entropy criterion) thresholding function. The performance of TLBO is explained by considering five images. In addition, the performance is compared with three well known population-based metaheuristics: particle swarm optimization(PSO), genetic algorithm (GA), and differential evolution (DE). Results show that TLBO presents the better performance in terms of fitness value, peak signal to noise ratio (PSNR), Structural-Similarity index (SSIM), and stability.
https://jmvip.sinaweb.net/article_12011_40079b7933c8837da373f0edbd4c865e.pdf
2016-01-21
51
62
Image Segmentation
multilevel image thresholding
Teaching-learning-based optimization
histogram
Kapur’s entropy
Seyed Jalaleddin
Mousavirad
jalalmoosavirad@gmail.com
1
1Department of Computer Engineering, Faculty of Computer and Electrical Engineering, University of Kashan
LEAD_AUTHOR
Hossein
Ebrahimpour-Komleh
ebrahimpour@kashanu.ac.ir
2
2Department of Computer Engineering, Faculty of Computer and Electrical Engineering, University of Kashan
AUTHOR
ORIGINAL_ARTICLE
Accelerating Face Detection in Static Images with Fusion of RGB and Depth Data
Face detection is an important part of many computer vision systems and has several applications in areas, such as face tracking, visual surveillance, video conferencing, face recognition, intelligent human-computer interfaces and content-based information retrieval. For use of face detection in this applications, need a fast and precise face detection algorithm. But Detection speed of traditional face detection method based on AdaBoost algorithm is slow since an exhaustive search in image. Over the past few years, the availability of color images with corresponding depth data has increased due to the popularity of low-cost RGB-Depth cameras, notably Kinect. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in face detection with intelligently constraining search over the image. In this paper, utilize additional depth data to reduce the computational cost of face detection. Leveraging the additional depth images from a Kinect camera, and use of Recurring in nature idea, we are able to accelerate the Viola-Jones face detector by 270%.
https://jmvip.sinaweb.net/article_12327_80c7463f421e9e5be1996f6212a4145d.pdf
2016-01-21
63
77
Face Detection
Data Fusion
Kinect
Depth Data
Ali
Salmani
alisalmaniali@gmail.com
1
Faculty of Electrical Engineering, Ferdowsi University of Mashhad
LEAD_AUTHOR
Morteza
Khademi
khademi@um.ac.ir
2
Faculty of Electrical Engineering, Ferdowsi University of Mashhad
AUTHOR