H.5. Image Processing and Computer Vision
1. Sparse Structured Principal Component Analysis and Model Learning for Classification and Quality Detection of Rice Grains

S. Mavaddati

Volume 8, Issue 2 , Spring 2020, , Pages 161-175

http://dx.doi.org/10.22044/jadm.2019.7402.1877

Abstract
  In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification ...  Read More

H.5. Image Processing and Computer Vision
2. Statistical Wavelet-based Image Denoising using Scale Mixture of Normal Distributions with Adaptive Parameter Estimation

M. Saeedzarandi; H. Nezamabadi-pour; S. Saryazdi

Volume 8, Issue 2 , Spring 2020, , Pages 289-301

http://dx.doi.org/10.22044/jadm.2020.7797.1920

Abstract
  Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the ...  Read More

H.5. Image Processing and Computer Vision
3. Segmentation Assisted Object Distinction for Direct Volume Rendering

A. Azimzadeh Irani; R. Pourgholi

Volume 8, Issue 1 , Winter 2020, , Pages 67-82

http://dx.doi.org/10.22044/jadm.2019.7207.1854

Abstract
  Ray Casting is a direct volume rendering technique for visualizing 3D arrays of sampled data. It has vital applications in medical and biological imaging. Nevertheless, it is inherently open to cluttered classification results. It suffers from overlapping transfer function values and lacks a sufficiently ...  Read More

H.5. Image Processing and Computer Vision
4. Video Abstraction in H.264/AVC Compressed Domain

A. R. Yamghani; F. Zargari

Volume 7, Issue 4 , Autumn 2019, , Pages 521-535

http://dx.doi.org/10.22044/jadm.2019.7850.1928

Abstract
  Video abstraction allows searching, browsing and evaluating videos only by accessing the useful contents. Most of the studies are using pixel domain, which requires the decoding process and needs more time and process consuming than compressed domain video abstraction. In this paper, we present a new ...  Read More

H.5. Image Processing and Computer Vision
5. Morphological Exudate Detection in Retinal Images using PCA-based Optic Disc Removal

J. Darvish; M. Ezoji

Volume 7, Issue 4 , Autumn 2019, , Pages 487-493

http://dx.doi.org/10.22044/jadm.2019.1488

Abstract
  Diabetic retinopathy lesion detection such as exudate in fundus image of retina can lead to early diagnosis of the disease. Retinal image includes dark areas such as main blood vessels and retinal tissue and also bright areas such as optic disk, optical fibers and lesions e.g. exudate. In this paper, ...  Read More

H.5. Image Processing and Computer Vision
6. Compressed Image Hashing using Minimum Magnitude CSLBP

V. Patil; T. Sarode

Volume 7, Issue 2 , Spring 2019, , Pages 287-297

http://dx.doi.org/10.22044/jadm.2018.6639.1787

Abstract
  Image hashing allows compression, enhancement or other signal processing operations on digital images which are usually acceptable manipulations. Whereas, cryptographic hash functions are very sensitive to even single bit changes in image. Image hashing is a sum of important quality features in quantized ...  Read More

H.5. Image Processing and Computer Vision
7. A Novel Face Detection Method Based on Over-complete Incoherent Dictionary Learning

S. Mavaddati

Volume 7, Issue 2 , Spring 2019, , Pages 263-278

http://dx.doi.org/10.22044/jadm.2019.7215.1857

Abstract
  In this paper, face detection problem is considered using the concepts of compressive sensing technique. This technique includes dictionary learning procedure and sparse coding method to represent the structural content of input images. In the proposed method, dictionaries are learned in such a way that ...  Read More

H.5. Image Processing and Computer Vision
8. Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers

A. Asilian Bidgoli; H. Ebrahimpour-Komle; M. Askari; Seyed J. Mousavirad

Volume 7, Issue 1 , Winter 2019, , Pages 97-108

http://dx.doi.org/10.22044/jadm.2017.4152.1503

Abstract
  This paper parallelizes the spatial pyramid match kernel (SPK) implementation. SPK is one of the most usable kernel methods, along with support vector machine classifier, with high accuracy in object recognition. MATLAB parallel computing toolbox has been used to parallelize SPK. In this implementation, ...  Read More

H.5. Image Processing and Computer Vision
9. Pedestrian Detection in Infrared Outdoor Images Based on Atmospheric Situation Estimation

Seyed M. Ghazali; Y. Baleghi

Volume 7, Issue 1 , Winter 2019, , Pages 1-16

http://dx.doi.org/10.22044/jadm.2018.5742.1696

Abstract
  Observation in absolute darkness and daytime under every atmospheric situation is one of the advantages of thermal imaging systems. In spite of increasing trend of using these systems, there are still lots of difficulties in analysing thermal images due to the variable features of pedestrians and atmospheric ...  Read More

H.5. Image Processing and Computer Vision
10. Multi-Focus Image Fusion in DCT Domain using Variance and Energy of Laplacian and Correlation Coefficient for Visual Sensor Networks

M. Amin-Naji; A. Aghagolzadeh

Volume 6, Issue 2 , Summer 2018, , Pages 233-250

http://dx.doi.org/10.22044/jadm.2017.5169.1624

Abstract
  The purpose of multi-focus image fusion is gathering the essential information and the focused parts from the input multi-focus images into a single image. These multi-focus images are captured with different depths of focus of cameras. A lot of multi-focus image fusion techniques have been introduced ...  Read More

H.5. Image Processing and Computer Vision
11. Automatic Optic Disc Center and Boundary Detection in Color Fundus Images

F. Abdali-Mohammadi; A. Poorshamam

Volume 6, Issue 1 , Winter 2018, , Pages 35-46

http://dx.doi.org/10.22044/jadm.2017.967

Abstract
  Accurately detection of retinal landmarks, like optic disc, is an important step in the computer aided diagnosis frameworks. This paper presents an efficient method for automatic detection of the optic disc’s center and estimating its boundary. The center and initial diameter of optic disc are ...  Read More

H.5. Image Processing and Computer Vision
12. Density-Based Histogram Partitioning and Local Equalization for Contrast Enhancement of Images

M. Shakeri; M.H. Dezfoulian; H. Khotanlou

Volume 6, Issue 1 , Winter 2018, , Pages 1-12

http://dx.doi.org/10.22044/jadm.2017.910

Abstract
  Histogram Equalization technique is one of the basic methods in image contrast enhancement. Using this method, in the case of images with uniform gray levels (with narrow histogram), causes loss of image detail and the natural look of the image. To overcome this problem and to have a better image contrast ...  Read More

H.5. Image Processing and Computer Vision
13. A stack-based chaotic algorithm for encryption of colored images

H. Khodadadi; O. Mirzaei

Volume 5, Issue 1 , Winter 2017, , Pages 29-37

http://dx.doi.org/10.22044/jadm.2016.735

Abstract
  In this paper, a new method is presented for encryption of colored images. This method is based on using stack data structure and chaos which make the image encryption algorithm more efficient and robust. In the proposed algorithm, a series of data whose range is between 0 and 3 is generated using chaotic ...  Read More

H.5. Image Processing and Computer Vision
14. Iris localization by means of adaptive thresholding and Circular Hough Transform

S. Memar Zadeh; A. Harimi

Volume 5, Issue 1 , Winter 2017, , Pages 21-28

http://dx.doi.org/10.22044/jadm.2016.731

Abstract
  In this paper, a new iris localization method for mobile devices is presented. Our system uses both intensity and saturation threshold on the captured eye images to determine iris boundary and sclera area, respectively. Estimated iris boundary pixels which have been placed outside the sclera will be ...  Read More

H.5. Image Processing and Computer Vision
15. Image authentication using LBP-based perceptual image hashing

R. Davarzani; S. Mozaffari; Kh. Yaghmaie

Volume 3, Issue 1 , Winter 2015, , Pages 21-30

http://dx.doi.org/10.5829/idosi.JAIDM.2015.03.01.03

Abstract
  Feature extraction is a main step in all perceptual image hashing schemes in which robust features will led to better results in perceptual robustness. Simplicity, discriminative power, computational efficiency and robustness to illumination changes are counted as distinguished properties of Local Binary ...  Read More

H.5. Image Processing and Computer Vision
16. Modified CLPSO-based fuzzy classification System: Color Image Segmentation

A.M. Shafiee; A. M. Latif

Volume 2, Issue 2 , Summer 2014, , Pages 167-179

http://dx.doi.org/10.22044/jadm.2014.356

Abstract
  Fuzzy segmentation is an effective way of segmenting out objects in images containing both random noise and varying illumination. In this paper, a modified method based on the Comprehensive Learning Particle Swarm Optimization (CLPSO) is proposed for pixel classification in HSI color space by selecting ...  Read More