Basic Concept Of Image Processing

The comprehensiveness and appropriateness in the data from exhibit 12a and 12b for the segmentation analysis are very accurate and on point. The questions asked in exhibit 12a, the segmentation variables, are really right

are the pixels used in the feature detection. The pixel at C is the centre of a detected

The number plates are varied from one and another such as different material use for number plate, blur captured image and the image is skew. Based on the results showed in table 4.6, the different types of material and blur image can be segment using bounding box without any distortions. The number plate used different types of material than actual material. The character and the background of number plate is very shinning and it is the modified number plate where the actual height is 5cm. Some of the number plate fails to recognize due to the luminance condition or problematic background of number plates Bounding box method manage to detect the height character and gives correct output in previous

Mohammed El-Helly et al. [8] proposed an approach for integrating image analysis technique into diagnostic expert system. A diagnostic model was used to manage cucumber crop. According to this approach, an expert system finds out the diseases of user observation. In order to diagnose a disorder from a leaf image, five image processing phases are used: image acquisition, enhancement, and segmentation, feature extraction and classification. Images were captured using a high resolution color camera and auto focus illumination light. Firstly they transformed the defected RGB image to the HSI color space then analyzed the histogram intensity channel then increased the contrast of the image. Fuzzy C Means (FCM) segmentation is used in this

After reading the digital image into internal memory, the program uses cvThreshold function to transfer the colorful image into a grayscale one. Then according to the optimal variance, transfer the grayscale image to a binary image. After that, the program uses cvDilate and cvErode functions to do dilation and erosion operation. Thus, in order to improve the accuracy rate, cvSmooth function is used to smooth the edges of the image. Then program uses canny operator to detect and outline edges. At the end, circle hough transform is used to detect the circles in the images, which are the sign of human head. So the program

(C) Identify and sort pictures of objects into conceptual categories (e.g., colors, shapes, textures); and

At first, the general objective of image segmentation in this project is to aid tumour detection by delineating it from normal brain structures. Segmentation aids to detect, to diagnose, to classify the type and to find the stage; thereby helping the treatment decision. In addition, it also helps to monitor the treatment either chemotherapy or surgery.

Normally, the automatic segmentation problem is very challenging and it is yet to be fully and satisfactorily solved. The aim of this tumor detection approach is to identify and segment the MRI tumor automatically. It takes into account the statistical features of the brain structure to represent it by significant feature points. Most of the early methods presented for tumor detection and segmentation may be broadly divided into three categories: region-based, edge-based and fusion of region and edge-based methods. Well known and widely used segmentation techniques are K-Means clustering algorithm, supervised method based on neural network classifier [4]. Also, the time spent to segment the tumor is getting reduced due to the detailed representation of the medical image by extraction of feature points. Region-based techniques look for the regions satisfying a given homogeneity criteria and edge based segmentation techniques look for edges between regions with different characteristics

Automating the segmentation process will favor faster and

T.F.Chen [9] segmentation is the process of portioning the images, where we need to find the particular portion, there are several methods segmentation such as active contour, etc. segmentation can be done both manually and automatically. Here the new technique of segmentation known as level sets segmentation are described, the level set segmentation reduces the problems of finding the curves which is enclose with respect to the region of interest. The implementation of this involves the normal speed and vector field, entropy condition etc. The implementation results produced was two different curves, which can be splitted.

One of the ultrasound image analysis is segmentation process to obtain the fetal biometric measurement. According to [4], an automatic segmentation technique on

2) Which research method was most helpful to you in developing and evaluating the segmentation options?

For this project what I am going to do is use image segmentation, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super-pixels). The way I am going to be storing my database and that is in a order of taxonomy, I'm going to be creating the database and the app itself using github to code. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Using the image segmentation will

segment further distinguishes itself by adding the advantage of quality. On the other hand, the

Segmentation is a tool; purpose is to choose target market.Segmentation comes prior to target market Many different tasks are involved other than segmentation when choosing target market Look at each segment on its own as an individual marketing opportunity. Potential worth of each segment To examine whether the whole market should be chosen or only few segments To find segments which are less satisfied in market from competitor brand.