The campaign against drug abuse is fought by all countries, most notably on ATS drugs. The identification process of ATS drugs depends heavily on its molecular structure. However, the process becomes more unreliable due to the... more
The campaign against drug abuse is fought by all countries, most notably on ATS drugs. The identification process of ATS drugs depends heavily on its molecular structure. However, the process becomes more unreliable due to the introduction of new, sophisticated, and increasingly complex ATS molecular structures. Therefore, distinctive features of ATS drug molecular structure need to be accurately obtained. This paper formulates a novel 3D orthogonal Fourier–Mellin moments-based molecular descriptor to represent the drug molecular structure. The performance of the proposed technique was analyzed using drug chemical structures obtained from UNODC for the ATS drugs, while non-ATS drugs are obtained randomly from ChemS pider database. The evaluation shows the proposed technique is qualified to be further explored and adapted in the future works to be fully compatible with ATS drug identification domain.
Support Vector Machines (SVM) is used for classification in pattern recognition widely. This paper applies this technique for recognizing handwritten numerals of Devanagari Script. Since benchmark database does not exist globally, this... more
Support Vector Machines (SVM) is used for classification in
pattern recognition widely. This paper applies this technique for
recognizing handwritten numerals of Devanagari Script. Since
benchmark database does not exist globally, this system is
constructed database by implementing Automated Numeral
Extraction and Segmentation Program (ANESP). Preprocessing is
manifested in the same program which reduces most of the
efforts. 2000 samples are collected from 20 different people
having variation in writing style. Moment Invariant and Affine
Moment Invariant techniques are used as feature extractor. These
techniques extract 18 features from each image which is used in
Support Vector Machine for recognition purpose. Binary
classification techniques of Support Vector Machine is
implemented and linear kernel function is used in SVM. This
linear SVM produces 99.48% overall recognition rate which is
the highest among all techniques applied on handwritten
Devanagari numeral recognition system.
SQL injection attacks are easy way to attack the web applications and gain access to the private data in a database. Using different types of the SQL attacks one can easily gain access, modify the database or can even remove it. Details... more
SQL injection attacks are easy way to attack the web applications and gain access to the private data in a database. Using different types of the SQL attacks one can easily gain access, modify the database or can even remove it. Details such as fields and table names are required for a hacker to modify a database.
Hence, to provide the increased amount of security to users and their data, different types of techniques are
used such as Random4 algorithm, which is based on randomization and used to encrypt the user input,
Hirschberg algorithm which is a divide and conquer technique used to match the query pattern. In this paper we are providing a comparative study and implementation of these prevention techniques using open source software.
El presente trabajo propone un método para caracterizar y reconocer objetos 3D, basado en la identificación de patrones obtenidos a partir del análisis de la deformación de una línea de luz láser proyectada sobre un objeto en rotación,... more
El presente trabajo propone un método para caracterizar y reconocer objetos 3D, basado en la identificación de patrones obtenidos a partir del análisis de la deformación de una línea de luz láser proyectada sobre un objeto en rotación, sin necesidad de una reconstrucción tridimensional. Las imágenes de líneas de luz láser fueron capturadas mediante una cámara CCD, posteriormente se pre-procesaron y segmentaron. Durante el proceso de caracterización se obtuvieron vectores característicos de dimensión R¹⁴⁴, por lo que se realizó un análisis de componentes principales para reducirlos a R⁶⁵ . Al final, los procesos de reconocimiento de patrones se llevaron a cabo mediante los algoritmos K-Nearest Neighbor, Näıve Bayes y una Red Neuronal Artificial feedforward, en donde se utilizó la validación cruzada para comparar el desempeño de cada uno, utilizando como instancias de prueba la base de datos original y la reducida por el análisis de componentes principales, logrando porcentajes de exactitud superiores al 88 %.
Face recognition is one of the most challenging problems in the domain of image processing and machine vision. Face recognition system is critical when individuals have very similar biometric signature such as identical twins. In this... more
Face recognition is one of the most challenging problems in the domain of image processing and machine vision. Face recognition system is critical when individuals have very similar biometric signature such as identical twins. In this paper, new efficient facial-based identical twins recognition is proposed according to the geometric moment. The utilized geometric moment is Pseudo-Zernike Moment (PZM) as a feature extractor inside the facial area of identical twins images. Also, the facial area inside an image is detected using Ada Boost approach. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian Twin Society which contain scaled, which contain the shifted and rotated facial images of identical twins in different illuminations. The results prove the ability of proposed method to recognize a pair of identical twins. Also, results show that the proposed method is robust to rotation, scaling and changing illumination.
The three dimensional shape information of teeth from cone beam computed tomography images provides important assistance for dentist performing implant treatment, orthodontic surgery. This paper describes the tooth root of both anterior... more
The three dimensional shape information of teeth from cone beam computed tomography images provides important assistance for dentist performing implant treatment, orthodontic surgery. This paper describes the tooth root of both anterior and posterior teeth from CBCT images of head. The segmentation is done using level set method with five energy functions. The edge energy used to move the curve towards border of the object. The shape prior energy provides the shape of the contour. The dentine wall energy provides interaction between the neighboring teeth and prevent shrinkage and leakage problem. The test result for both segmentation and 3D reconstruction shows that the method can visualize both anterior and posterior teeth with high accuracy and efficiency.
The proposed scheme uses the invariant features extracted from each block to detect the copy-move forgery regions in a digital image. In the proposed scheme, an image is first divided into overlapping blocks. Then, seven invariant moments... more
The proposed scheme uses the invariant features extracted from each block to detect the copy-move forgery regions in a digital image. In the proposed scheme, an image is first divided into overlapping blocks. Then, seven invariant moments of the maximum circle area in each block are calculated as moment features. Mean and variance of these seven moment features, as second feature set, are acquired for block comparison to reduce computation time. Thus, the proposed scheme outperforms previous schemes. The copy-move forgery regions can be found by matching the detected blocks with relative distance calculation. Experimental results show that the adopted moment features are efficient for detecting rotational or flipped duplicated regions.
