Cemil Oz

A study of neural network based inverse kinematics solution for a three-joint robot

Abstract A neural network based inverse kinematics solution of a robotic manipulator is presented in this paper. Inverse kinematics problem is generally more complex for robotic manipulators. Many traditional solutions such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. In this study, a three-joint robotic manipulator simulation software, developed in our previous studies, is used. Firstly, we have generated many initial and final points in the work volume of the robotic manipulator by using cubic trajectory planning. Then, all of the angles according to the real-world coordinates (x, y, z) are recorded in a file named as training set of neural network. Lastly, we have used a designed neural network to solve the inverse kinematics problem. The designed neural network has given the correct angles according to the given (x, y, z) cartesian coordinates. The online working feature of neural network makes it very successful and popular in this solution.

A practical license plate recognition system for real-time environments

A computer vision system to recognize license plates of vehicles in real-time environments is presented in this study. The images of moving vehicles are taken with a digital camera and analyzed in real-time. An artificial neural network (ANN) system is used to locate the area and position of the license plate. The system has the following stages: (i) Image acquisition and determination of the location of the vehicle license plate (VLP), (ii) segmentation of the VLP into separate characters using image processing techniques, and (iii) recognition of each symbol in VLP using a feedforward artificial neural network (ANN) and assembly of the characters. Performance results are presented at the end.

Signature recognition and verification with artificial neural network using moment invariant method

In this paper, we present off-line signature recognition and verification system which is based on image processing, moment invariant method and ANN. Two separate sequential neural networks are designed; one for signature recognition, and another for verification (i.e. for detecting forgery). Verification network parameters which are produced individually for every signature are controlled by a recognition network. The System overall performs is enough to signature recognition and verification.

Effects of the Trajectory Planning on the Model Based Predictive Robotic Manipulator Control

In this study, the application of the single input single output (SISO) neural generalized predictive control (NGPC) and SISO generalized predictive control (GPC) of a three joint robotic manipulator are presented. The sinusoidal and cubic trajectory principles were used for position reference and velocity reference trajectories. NGPC-SISO algorithm performs better than GPC-SISO algorithm for both trajectories. The GPC-SISO robotic manipulator control results have better values in the case of the sinusoidal trajectory, but the NGPC-SISO robotic manipulator control results for both the cubic and sinusoidal trajectory are almost similar.

Automatic Vehicle License Plate Recognition using Artificial Neural Networks

In this study, we present an artificial neural network based computer vision system which can analyze the image of a car taken by a camera in real-time, locates its license plate and recognizes the registration number of the car. The model has four stages. In the first stage, vehicle license plate (VLP) is located. Second stage performs the segmentation of VLP and produces a sequence of characters. An ANN runs in the third stage of the process and tries to recognize these characters which form the VLP.

iFER: facial expression recognition using automatically selected geometric eye and eyebrow features

Abstract. Facial expressions have an important role in interpersonal communications and estimation of emotional states or intentions. Automatic recognition of facial expressions has led to many practical applications and became one of the important topics in computer vision. We present a facial expression recognition system that relies on geometry-based features extracted from eye and eyebrow regions of the face. The proposed system detects keypoints on frontal face images and forms a feature set using geometric relationships among groups of detected keypoints. Obtained feature set is refined and reduced using the sequential forward selection (SFS) algorithm and fed to a support vector machine classifier to recognize five facial expression classes. The proposed system, iFER (eye–eyebrow only facial expression recognition), is robust to lower face occlusions that may be caused by beards, mustaches, scarves, etc. and lower face motion during speech production. Preliminary experiments on benchmark datasets produced promising results outperforming previous facial expression recognition studies using partial face features, and comparable results to studies using whole face information, only slightly lower by   ∼  2.5  %   compared to the best whole face facial recognition system while using only   ∼  1  /  3 of the facial region.

Leaf recognition using K-NN classification algorithm

Plants play a crucial role in terms of the lives of human and other creatures since the existence of the universe. Despite the studies of plant scientists, there are many undiscovered and unidentified species in our environment. This paper is aimed to add the leaves, whose images have been clearly attained, to the system and to provide a proper analysis of those leaves. The images could be either the ones taken before or the ones obtained by means of a camera that is connected transiently. Leaf images went through pretreatment phases first, and then their features were extracted. Finally, classification processing was accomplished by using K-NN algorithm. The System is working successfully.

PROSES: A Web Server for Sequence-Based Protein Encoding

Recently, the number of the amino acid sequences shared in online databases is growing rapidly in huge amounts. By using sequence-derived features, machine learning algorithms are successfully applied to prediction of protein functional classes, protein-protein interactions, subcellular location, and peptides of specific properties in many studies. Protein Sequence Encoding System (PROSES) is a web server designed as freely and easily accessible for all researchers who want to use computational methods on protein sequence data. That is, PROSES provides users to encode their protein sequences easily without writing any programming code.

Surface inspection system for industrial components based on shape from shading minimization approach

Abstract. An inspection system using estimated three-dimensional (3-D) surface characteristics information to detect and classify the faults to increase the quality control on the frequently used industrial components is proposed. Shape from shading (SFS) is one of the basic and classic 3-D shape recovery problems in computer vision. In our application, we developed a system using Frankot and Chellappa SFS method based on the minimization of the selected basis function. First, the specialized image acquisition system captured the images of the component. To eliminate noise, wavelet transform is applied to the taken images. Then, estimated gradients were used to obtain depth and surface profiles. Depth information was used to determine and classify the surface defects. Also, a comparison made with some linearization-based SFS algorithms was discussed. The developed system was applied to real products and the results indicated that using SFS approaches is useful and various types of defects can easily be detected in a short period of time.