Volume 1, Issue 1 (2018) Article No. 10 PP 1-10 1 www.viva-technology.org/New/IJRI A Survey of Image Processing and Identification Techniques Sahil V. Khedaskar 1 , Mohit A. Rokade 1 , Bhargav R. Patil 1 , Tatwadarshi P. N. 2 1 (B.E. Computer Engineering, VIVA Institute of Technology/ Mumbai University, India) 2 (Assistant Prof. Computer Engineering, VIVA Institute of Technology/ Mumbai University, India) Abstract : Image processing is always an interesting field as it gives enhanced visual data for human simplification and processing of image data for transmission and illustration for machine preception. Digital images are processed to give better solution using image processing. Techniques such as Gray scale conversion, Image segmentation, Edge detection, Feature Extraction, Classification are used in image processing. In this paper studies of different image processing techniques and its methods has been conducted. Image segmentation is the initial step in many image processing functions like Pattern recognition and image analysis which convert an image into binary form and divide it into different regions. The technique used for segmentation is Otsu’s method, K-means Clustering etc. For feature extraction feature vector in visual image is texture, shape and color. Edge detector with morphological operator enhances the clarity of image and noise free images. This paper also gives information about algorithm like Artificial Neural Network and Support Vector Mechanism used for image classification. The image is categorized into the receptive class by an ANN and SVM is used to compile all the categorized result. Overall the paper gives detail knowledge about the techniques used for image processing and identification. Keywords – Extraction, Segmentation, Otsu’s method, K-means, Edge detection, ANN, SVM, Active Shape model(ASM), GLCM, SIFT, Genetic algorithm, BIM, RGB Colour, BIM, Vein algorithm. 1. INTRODUCTION Image processing is a technique to translate an image into digital form and execute some operation on it, in order to get an improved image or to retrieve useful data from image. It is a procedure of signal distribution. The process takes input as an image and then apply efficient algorithms, and the results may be image, data or features associated with that image [15]. The processing stages start with image segmentation. There is some desire from image segmentation algorithms. first of them is speed. While processing for segmentations of an image, it does not want to spend much time. The second is good shape integration of the object. This will enhance results in picture acknowledgment. If the result of shape is incomplete, it need to take many properties to record the edge of the over-section results [2]. In computer vision, picture division is the way toward parceling an advanced picture into various sections. The objective of division is to disentangle or potentially change the portrayal of a picture into something that is more important and less demanding to examine. Picture division is regularly used to find articles and limits in pictures. All the more absolutely, picture division is the way toward allotting a mark to each pixel in a picture to such an extent that pixels with a similar name share certain attributes [1]. Division is generally the essential stage in any undertaking to analyze or interpret an image consequently [3]. Division conquers any hindrance between low-level picture preparing and abnormal state picture handling. A few sorts of division procedure will be found in any application including the discovery, acknowledgment, and estimation of items in pictures. Otsu's division strategy, in light of histogram examination, is extensively applied as a part of different applications [2]. The approach sections a picture by enhancing the change amongst fragments and, all the while, limits the difference inside the portions. Proposes an Otsu-strategy adjustment for dividing hand compositions from an uproarious foundation. In, the Otsu-technique is utilized to extract different focuses in the data pictures, proposes to extend Otsu 1D-histogrambased technique into a "2D-Otsu" for division. The first single-edge Otsu strategy expressions for one ideal limit for dividing the information image into "forefront" and "foundation".