Content based image retrieval cbir

هدفنا من خلال هذه الدراسة في إطار المشروع الفصلي للسنة الرابعة إلى إلقاء الضوء على استرجاع الصور من مجموعة كبيرة بالاعتماد على محتوى صورة هدف , و قمنا بتدعيم هذه الدراسة بتطبيق ضمن بيئة الماتلاب لبرنامج بحث عن الصور المشابهة لصورة مدخلة . و قد تركز بحثنا على ميزتين هامتين يكاد لا يخلو منها أي نظام بحث عن الصور بالاعتماد على المحتوى و هما ميزتي الهيستوغرام اللوني و بنية الصورة texture , ووضحنا الخطوات التي يتم في ضوئها عملية الاسترجاع بدءاً من تحليل الصورة و استخلاص شعاع الواصفات الخاص فيها , و مطابقته مع أشعة الميزات الخاصة بالصور الموجودة في قاعدة البيانات ليتم ترتيب الصور بحسب مدى تشابهها من الصورة الهدف . و تطرقت الدراسة إلى استخدام الفضاء اللوني HMMD كبديل للفضاء اللوني RGB لاستخراج واصفات البنية اللونية على اعتبار أنه نموذج لوني موجه بالمستخدم user oriented و بالتالي نضمن أن نحصل على نتائج أفضل ترضي المستخدم . وقمنا بتدعيم الدراسة بعدد من الأشكال و الأمثلة و المخططات التي توضح محتوى الدراسة النظرية و ما قمنا بعمله في التطبيق ضمن بيئة الماتلاب .

Using The System of Identifying People Based on their Facial Expressions to Design the Automated Checking System for Students

The research presents a design for an automated checking system for students. The system takes a picture of the student, then it extracts his/her basic facial features. The network was trained using the reverse spreading algorithm. If a training database is generated for each student consisting of 15 training samples contained of the necessary facial expressions to identify the student for one time at the beginning of the semester, then the neural network will be trained on students database to obtain a trained neural network able to identify the students of each category depending on their physical appearance. That will result in knowing who attends and who does not attend the session. The system designed for this purpose was supplied with the trained network. The system provides the possibility of automated checking for students according to the content of the study giving the alarm in case of the existence of the picture of a student who does not belong to the same group.

Face Expression Classification Using Neural Network and PCA algorithm

The purpose of this article is to shed light on the mechanism and the procedures of a program that classifies an input face into any of the six basic facial expressions, which are Anger, Disgust, Fear, Happiness, Sadness and Surprise, in addition to normal face. This program works by apply PCA- principal component analysis algorithm, which is applied of one side of the face, and depends, on contrast to the traditional studies which rely on the whole face, on three components: Eyebrows, Eyes and Mouth. Those out-value are used to determine the facial feature array as an input to the neural network, and the neural network is trained by using the back-propagation algorithm. Note that the faces used in this study belong to people from different ages and races.

Study of the influence of removing speckle noise on the segmentation of uterus ultrasonic images based on Active shape contour algorithm

The study suggests a new approach to segment the ultrasound uterus images to obtain the fetus region. The approach consists of three stages. The first includes the preprocessing in which the speckle noise is removed from the ultrasound images depending on sequential filtering of Gabor filter and median filter. Second, an improved active shape contour independent of edges is applied to segment the uterus images. The last stage is the post processing which depends on the morphological operation to eliminate the undesired region and obtain the region of interest (fetus). The designed system has been tested by means of medical database of ultrasound uterus images downloaded from the ULTRASCAN CENTRE site in Kaloor (India). The experimental tests show that the proposed sequential filtering technique improves the active shape contour algorithm performance significantly, so the system segment the uterus images correctly even in the presence of speckle noise.

Handwritten Signature Verification using Statistical Functions for Signature Image

This research suggests a new method that aims to verify the manual signature image which is written by person, and specify whether this signature back to this person or that forged signature. This was done by extracting geometric features of the signature image and applying statistical functions on them as a way to verify the signature of that person. The features from the signature image have been extracted on many stages so a signature image has been transformed from the gray scale to binary format, and then extracting the statistical features from the original signature image which is the maximum value from the most repeated values in the ones' coordination line that determine the signature shape, in addition to the number of ones which also determine the signature shape. Finally two ranges have been identified for the values accepted for original signature image. By the same way, statistical features have been extracted from the foreign signature image and tested if they aggregate within the specified domain of acceptable values. This research also includes the results of the proposed approach that compared with the previous methods in this scope. The proposed method has been tested to the data base consisting of 16200 signatures back to 300 persons, and as a result the signature image has been verified with a good percentage.

Barrel Distortion Correction by Using Image Processing Techniques

The various types of radial distortions generated by digital cameras are presented in this paper, like Barrel Distortions and Pincushion Distortion. Image processing techniques are used to correct the barrel distortion generated by wide-angle lenses of digital cameras. A model for barrel distortions is founded. Moreover, an algorithm for correcting this distortion is developed. This algorithm depends on finding the right parameters of the model. The grid pattern is used to detect pixels that caused the distortion and reallocate these pixels back into their original locations, making the corrected photo as close as possible to the original.

Simulating the Behavior of an Integrate-and-Fire Neuron

Video image data can be analyzed and processed in many ways. This research explores the extent at which spiking neurons, which are designed along the Hodgkin-Huxley model, are suitable for this task. The simulations reported in this research consider integrate-and-fire neurons constant and alternating input currents, as well as pixel-intensity driven inputs. Currently, the simulation software employs 64 independently operating spiking neurons that process image data taken every 25 ms. In order to define the response of these neurons, the experiments were done on 100 digital images which include different illuminations, contrast, and saturation situations. The results show that the integrate-and-fire-neuron is highly sensitive to the changes in the intensity of pixels if its parameters are properly set. So in many applications, such as "Saliency Maps", which highly depend on the intensity values of a set of pixels, a neural network made of this neuron will perfectly fit.

Ear segmentation using likelihood skin detector and morphological operations

This paper proposes a new approach for the segmentation of the side face images to obtain the ear region. The proposed approach is divided into two basic steps: The first step classifies the image pixels into skin and non-skin pixels using likelihood skin detector. This likelihood image is processed by using morphological operations to detect the ear region. In the second step, image containing ear region is isolated from side face image by using one of two methods; the first is based on experiment, while the second is based measurements. The study includes a comparison of the results between the proposed study and previous ones to identify the differences. The proposed approach is applied on a database containing 146 images of 20 persons. These images were taken under different illumination, pose, day, and location variations. The partial occlusion by hair or earing was also taken in account. The results showed that the system achieved a correct segmentation with rate 95.8%.

Face Expression Classification Using Neuro-Fuzzy Controller

The purpose of this article is to shed light on the mechanism and the procedures of a neuro-fuzzy controller that classifies an input face into any of the four facial expressions, which are Happiness, Sadness, Anger and Fear. This program works according to the facial characteristic points-FCP which is taken from one side of the face, and depends, in contrast with some traditional studies which rely on the whole face, on three components: Eyebrows, Eyes and Mouth.