Automatic Facial Expression Classification Using Image Processing Technique ( Fear – disgust – sadness – Surprise – Anger – Happiness – Natural )

This paper presents an algorithm for designing a system that classifies standard human facial expressions which are fear, disgust, sad , surprise, anger, happiness, and the normal expression . The facial expression that is presented in the input image of the system can be classified depending on extracting appearance features then, it is entered into neural network to complete the classification process using Matlab as a programming language. Multiple stages completed the work, which are, (collection images, pre-processing of the images, feature extraction, training neural network, classification and testing). Our system has been able to achieve the highest rating when the expression of anger reached 100 %, while the lowest rating was at the expression of sad by 30%.

A Proposed Approach to Reduce Digital Image Processing Time in Real Time Applications

This research introduces a new approach to reduce time execution of processing programs, by reducing the amount of processed data, especially in applications where the priority is to the execution time of the program over the detailed information of captured pictures, such as detection and tracking systems.

Building Detection in the Satellite Image using Rectangle Hough Transform

In our research we studied and analyzed the different types of methods used for automatic building detection from the satellite images, then, we proposed a general methodology for building detection based on its geometrical boundary features using Hough transform for the rectangular forms.

Auto-Detection of Femur Length Using Ultrasound Image Processing Techniques

This Paper offers an effective method to measure the length of the femur in Fetal Ultrasound Images, it applies a series of steps starting with the reducing amount of noise in these images, and then converted them to a binary form and uses morphological operations to segment the femur and isolate it from the rest of the image objects, then it applies an Edge Detector in order to find the edges of the bone, then uses the Hough Transform to detect straight lines in the image. we apply overlapping for resulted lines on the original image, finally we choose the most significant and longest straight line which is corresponding to the length of the femur. The proposed method facilitates the measurement of the femur without the help of a physician through a series of steps.