Nowadays, with the extensive use of cameras in many areas of life, every day millions of videos are uploaded on the internet. In addition, with rapidly developing video editing software applications, it has become easier to forge any video. These software applications have made it challenging to detect forged videos, especially with forged videos have duplication of three-dimensional (3-D) regions. Recently, there has been increased interest in detecting forged videos, but there are very limited studies to detect forged videos which were duplicated 3-D regions. So, our research focused on this weakness and proposed a new method, which can be used for detecting and locating 3-D duplicated regions in videos based on the phase-correlation of 3-D regions residual more efficiently. To evaluate the efficiency of the proposed method, we experimented with two realistic datasets VFDD-3D and REWIND-3D. The results of the experiments proved that the proposed method is efficient and robust for detecting small 3-D regions duplication and frame sequences duplication, especially localization of duplication forgery in videos has shown impressive results.

Face Aging is an important and challenging application in computer vision. This is an application of conditional image generation. Until recently generative model was not good enough to generate considerable good resolution images. A generative model called generative adversarial network has introduced impressive capabilities in generating realistic images in both unconditional and conditional settings. Still, the task of generating images of different age conditioning on a given image is a very challenging task. Because there are two constraints to satisfy here in the generated images. The generated image must preserve the identity of the person in the source image and the image must have the features of the target age. In this work, we have applied the generative adversarial network in conditional settings along with custom loss function to satisfy the two mentioned constraints. The experiment has shown improved performance both in preserving the person’s identity and classification accuracy of generated images in the target class compared to previous known approach to this problem. 

Nowadays by growing the number of available medical ‎imaging data, there is a great demand towards ‎computational systems for image processing which can ‎help with the task of detection and diagnosis. Early detection of abnormalities using computational systems can help doctors to plan an effective treatment program for the patient. The main ‎challenge of medical image processing is the automatic ‎computerized detection of a region of interest. In recent years ‎in order to improve the detection speed and increase the ‎accuracy rate of ROI detection, different models based on the human vision ‎system, have been introduced. In this paper, we have provided a brief description of recent works which mostly used visual ‎models, in medical image processing and finally, ‎a conclusion is drawn about open challenges and required research in this field.‎

This paper focuses on classification of varieties of plants’, animals’ and minerals’ origin fabric materials from images. The morphological operations, namely, erosion and dilation are used. ANN classifier is used to predict the classification rates and the rates of 89%, 87% and 88% are obtained for plants’, animals’ and minerals’ origin fabric images respectively. The overall classification rate of 88% is obtained. 

License Plate Detection (LPD) system is the application of computer vision and image processing technology. LPD system is the first and main step of License Plate Recognition (LPR) system. So, it performs as the main driver of the LPR system. License plate detection step is always performed in front of the license plate recognition step. LPD system takes the vehicle images as input, follows with the general steps: such as reprocessing, localization, region extraction, and region detection, and the detected image are the output of the system. There are many algorithms for LPD while detecting a license plate in different conditions is still a complex task. For the LPD system, morphological operation and deep learning model are mostly used. This paper presents the critical study of the license plate detection system and also examines the implementation of new technologies of the license plate detection system.