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A Survey on Performance Evaluation of Object Detection Techniques in Digital Image Processing
J. Komala Lakshmi,M. Punithavalli
International Journal of Computer Science Issues , 2010,
Abstract: In digital image processing , the performance evaluation means the analysis of parameters that improves the execution of the proposed system there by producing the optimized result. The image is defined as a Scene consists of objects of interest. To understand the contents of the image , one should know the objects that are located in the image. The shape of the object is a binary image representing the extent of the object. In Digital Image processing the shapes are represented and described in various methods .Shape representation method results in a non numeric representation of the original shape (e.g.) a graph. So that the important characteristics of the shape are preserved. The shape description refers to the methods that result in a numeric descriptor of the shape and is a step subsequent to shape representation .Skeletons are one such shape descriptors. The skeleton of a two-dimensional object is a transformation of the shape object into a one dimensional line introducing skeleton shape descriptors. Many operations like shape representation and deformation can be performed more efficiently on the skeleton than on the full object, as skeleton is simpler than the original object. The parameters such as thresholds, bounds and weights have to be tuned for the successful performance of the object recognition system. This paper provides an overview of estimating the parameters for performance evaluation of the object detection techniques, and a survey of Performance evaluation of junction detection schemes in digital image processing.
Segmentation Techniques Comparison in Image Processing
R.Yogamangalam,B.Karthikeyan
International Journal of Engineering and Technology , 2013,
Abstract: In day-to-day life, new technologies are emerging in the field of Image processing, especially in the domain of segmentation. This paper presents a brief outline on some of the most common segmentation techniques like thresholding, Model based, Edge detection, Clustering etc., mentioning its advantages as well as the drawbacks. Some of the techniques are suitable for noisy images. In that Markov Random Field (MRF) is the strongest method of noise cancellation in images whereas thresholding is the simplest technique for segmentation.
Lung Cancer Detection Using Image Processing Techniques
Mokhled S. AL-TARAWNEH
Leonardo Electronic Journal of Practices and Technologies , 2012,
Abstract: Recently, image processing techniques are widely used in several medical areas for image improvement in earlier detection and treatment stages, where the time factor is very important to discover the abnormality issues in target images, especially in various cancer tumours such as lung cancer, breast cancer, etc. Image quality and accuracy is the core factors of this research, image quality assessment as well as improvement are depending on the enhancement stage where low pre-processing techniques is used based on Gabor filter within Gaussian rules. Following the segmentation principles, an enhanced region of the object of interest that is used as a basic foundation of feature extraction is obtained. Relying on general features, a normality comparison is made. In this research, the main detected features for accurate images comparison are pixels percentage and mask-labelling.
Histopathological Image Analysis Using Image Processing Techniques: An Overview  [PDF]
A. D. Belsare,M. M. Mushrif
Signal & Image Processing , 2012,
Abstract: This paper reviews computer assisted histopathology image analysis for cancer detection and classification. Histopathology refers to the examination of invasive or less invasive biopsy sample by a pathologist under microscope for locating, analyzing and classifying most of the diseases like cancer. The analysis of histoapthological image is done manually by the pathologist to detect disease which leads to subjective diagnosis of sample and varies with level of expertise of examiner. The pathologist examine the tissue structure, distribution of cells in tissue, regularities of cell shapes and determine benign and malignancy in image. This is very time consuming and more prone to intra and inter observer variability. To overcome this difficulty a computer assisted image analysis is needed for quantitative diagnosis oftissue. In this paper we reviews and summarize the applications of digital image processing techniquesfor histology image analysis mainly to cover segmentation and disease classification methods.
Image Processing Techniques in Shockwave Detection and Modeling  [PDF]
Suxia Cui, Yonghui Wang, Xiaoqing Qian, Zhengtao Deng
Journal of Signal and Information Processing (JSIP) , 2013, DOI: 10.4236/jsip.2013.43B019
Abstract:

Shockwave detection is critical in analyzing shockwave structure and location. High speed video imaging systems are commonly used to obtain image frames during shockwave control experiments. Image edge detection algorithms become natural choices in detecting shockwaves. In this paper, a computer software system designed for shockwave detection is introduced. Different image edge detection algorithms, including Roberts, Prewitt, Sobel, Canny, and Laplacian of Gaussian, are implemented and can be chosen by the users to easily and accurately detect the shockwaves. Experimental results show that the system meets the design requirements and can accurately detect shockwave for further analysis and applications.

Diabetic Retinopathy-Early Detection Using Image Processing Techniques
V.Vijaya Kumari,,N.SuriyaNarayanan
International Journal on Computer Science and Engineering , 2010,
Abstract: Diabetic retinopathy is the cause for blindness in the human society. Early detection of it prevents blindness. Image processing techniquescan reduce the work of ophthalmologists and the tools used automatically locate the exudates. Early detection helps the patients to aware of the seriousness of the disease. In this paper we present amethod which is automatic and involves two steps: optic disk detection and exudates detection. The extraction of optic disk is done using propagation through radii method. Exudates detection is done using feature extraction, template matching and enhanced MDD classifiers and the methods are compared.
Image Processing Techniques for the Enhancement of Brain Tumor Patterns  [PDF]
KIMMI VERMA,ARU MEHROTRA,VIJAYETA PANDEY,SHARDENDU SINGH
International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering , 2013,
Abstract: Brain tumor analysis is done by doctors but its grading gives different conclusions which may vary from one doctor to another. So for the ease of doctors, a research was done which made the use of software with edge detection and segmentation methods, which gave the edge pattern and segment of brain and brain tumor itself. Medical image segmentation had been a vital point of research, as it inherited complex problems for the proper diagnosis of brain disorders. In this research, it provides a foundation of segmentation and edge detection, as the first step towards brain tumor grading. Current segmentation approaches are reviewed with an emphasis placed on revealing the advantages and disadvantages of these methods for medical imaging applications. The use of image segmentation in different imaging modalities is also described along with the difficulties encountered in each modality.
A Comparison of various Edge Detection Techniques used in Image Processing  [PDF]
G.T. Shrivakshan,C. Chandrasekar
International Journal of Computer Science Issues , 2012,
Abstract: In this paper the important problems are taken in this paper is to understand the fundamental concepts of various filters and apply these filters in identifying a shark fish type which is taken as a case study. In this the edge detection techniques are taken for consideration. The software is implemented using MATLAB. The main two operators in image processing are Gradient and Laplacian operators. The case study is taken for observation of Shark Fish Classification through Image Processing using the various filters which are mainly gradient based Roberts, Sobel and Prewitt edge detection operators, Laplacian based edge detector and Canny edge detector. The advantages and disadvantages of these filters are comprehensively dealt in this study
Image processing techniques for lemons and tomatoes classification
Lino, Antonio Carlos Loureiro;Sanches, Juliana;Fabbro, Inacio Maria Dal;
Bragantia , 2008, DOI: 10.1590/S0006-87052008000300029
Abstract: vegetable quality is frequently referred to size, shape, mass, firmness, color and bruises from which fruits can be classified and sorted. however, technological by small and middle producers implementation to assess this quality is unfeasible, due to high costs of software, equipment as well as operational costs. based on these considerations, the proposal of this research is to evaluate a new open software that enables the classification system by recognizing fruit shape, volume, color and possibly bruises at a unique glance. the software named imagej, compatible with windows, linux and mac/os, is quite popular in medical research and practices, and offers algorithms to obtain the above mentioned parameters. the software allows calculation of volume, area, averages, border detection, image improvement and morphological operations in a variety of image archive formats as well as extensions by means of "plugins" written in java.
Diagnosis of Iron Deficiency Anemia Using Image Processing Techniques  [cached]
Basim Alhadidi
Research Journal of Applied Sciences, Engineering and Technology , 2012,
Abstract: This study aims to implement an image processing algorithm for detection of both red and blue stained blood cells to help in diagnosis of iron deficiency anemia in a more effective and efficient way. Our approach allows us to obtain the exact number of each stained blood cells. The algorithm also calculates the percentage of blue and red stained cells in the given specimen image sample. This information is vital in detecting the disease and determining its severity. Although the algorithm is designed in such a manner to provide flexibility regarding the selection of either a particular region or a whole given image sample, the calculations are done for the desired region only. Images of villi cells taken from the small intestine of humans were used.
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