فهرست مطالب سجاد محمدزاده

Nowadays, one of the most principal processes of perceiving the content of videos is the moving object tracking, in which the tracking process of a moving object is implemented in each video frame. The use of filters in this field has been increased during the last decade. Correlation filters are one of the most widely used filters in this field, however, using this filter, as usual, may cause the problem of target drifting. The present study proposes a novel method to improve the performance of the correlation filter. The advanced searching strategy can greatly overcome to the problem of target drifting with examining a threshold level by calculating the average and variance in each frame. In this regard, if the level of the threshold is reduced, a mechanism will be activated to recover the target in the current frame. In order to describe the target, the histogram of the oriented gradients feature has been used because this feature shows the changes in illumination variation better than other features. The proposed method has been examined in single-camera mode on the TB50 and TB100 datasets. To evaluate the proposed method, several criteria including precision, correct detection rate (CDR), average location error (ALE), and frame per second are used. The obtained results on the TB50 show that the proposed method, compared to the KCF method, has achieved an improvement around 9% in the precision criterion, improvement 6% in the success rate criterion and a 50% reduction in the ALE criterion. Also, the obtained results on TB100 have increased the precision criterion by approximately 15%, the success rate by 12%, and a favorable reduction of 50% on the ALE.

Pathological analysis plays an important role in the diagnosis, prediction and planning of cancer treatment. Using digital pathology, ie, scanning and storing digital parts of patient tissue, tools for analyzing these complex images now can be developed. Doctors use a computer diagnostic system from an intelligent assistant to accurately diagnose. These systems have great benefits in improving treatment efficacy.

In this study, the deep neural network classifier has been used with the help of the Tensor Flow framework and the use of the Keras-library. Input images are initially transmitted from a low pass filter to reduce noise effects. The pre-processed images are then imported into a convolutional neural network.

The results of the research reveal a significant difference in the accuracy values between different methods with the proposed method, which in some cases indicates an increase of more than 14.18% in the accuracy of the diagnosis. Another advantage of the proposed method is to provide high sensitivity to histopathologic images, which shows an increase of 12 to 18 percent compared to other studies. The reason for this is the excellence of extracting high-level features through convolutional neural network, which is accompanied by a reduction in the size of the feature vector.

The results showed a accuracy of %98.6 for skin lesions and %96.1 accuracy for breast cancer histopathologic findings, which offers promising results compared to the results of other studies.

Salient object detection attracted the attention of researchers in various fields, so that it is used in many applications of the visual machine, such as object detection and tracking. Most of the work in this area is based on bottom-top saliency models and use low-level features to extract the final saliency map that these works do not have a significant accuracy in salient object detection. On the other hand, top-down visual models are used for the specific applications. In this paper, a different method has been proposed to extract the salient object map that uses low-up and top-down attributes for extracting the salient and it is based on the learning process. The simultaneous selection of these features makes the proposed algorithm for various applications and increases the accuracy of the salient object detection. The learning process is performed by using the and Convolutional Neural Network (CNN) structure. After the decomposition of image to its super pixels, different features of image are extracted. Then, the extracted features are normalized to have zero mean and unit variance, and CNN is used to train the features. The accuracy of the proposed method is improved by using of the 8 salient and CNN. The performance of the proposed method has been compared to twenty method by applying four new databases including MSRA-100, ECSSD, MSRA-10K and Paskal-S. The proposed method provides better results compared to the other methods in respect to prediction of salient object.

The importance and demand of visual scene understanding have been increasing because of autonomous systems development. Optical flow is known as an important tool for scene understanding. Current optical flow methods present general assumptions and spatial homogeneous for spatial structure of flow. In fact, the optical flow in an image depends on object class and the type of object movement. The first assumption in many methods in this field is the brightness constancy during movements of pixels between frames. This assumption is proven to be inaccurate in general. In this paper, we use recent development of deep convolutional networks in semantic segmentation of static scenes to divide an image in to different objects and also depends on type of the object different movement patterns are defined. Next, estimation of the optical flow is performed by using deep neural network for initial image which has been semantically segmented. The proposed method provides minimum error in optical flow measures for KITTI-2015 database and results in more accurate segmentation compared to state-of-the-art methods for several natural videos.

Detecting social groups is one of important and complex problems which has been concerned recently. Detecting social groups and relation between group members will be necessary for human robots in near future. Databases have some information including trajectories and also labels of members. The target is to detect social groups that contains at least two people or detecting individual motion of the persons. In the proposed method, for detecting social groups, physical distance, temporal causality and shape similarity features are used. The required time to extract these features is lower than the other suggested features. In addition to accuracy, the effectiveness of the proposed method in terms of required time for training and testing data is also examined. Lower required time provides greater ability to implement for human robots. The proposed method provides acceptable results in valid databases and is compared to existing methods in terms of statistical results and the required time.

Skin cancer has grown dramatically over the past decades, and the importance of early treatment is increasing day by day. The purpose of this study is to use deep neural networks to create an auto-diagnosis system for melanoma, in which data is directly controlled as part of a deep learning process. In this paper, studies on related pictures of skin cancer were performed.  For the diagnosis of benign or malignant skin cancer, the deep neural network classifier is used with the help of the Tensorflow framework and the use of the Keras libraries. The dataset which are used in this study consist 70 images of melanoma and 100 images of benign moles. In the proposed model, 80% of the database images are used for training and 20% of the database images are selected for testing. The proposed method offers a higher detection accuracy than other existing methods, which has increased the accuracy of diagnosis in most cases by more than 10%. The high accuracy of the diagnosis and classification and the speed of convergence to the final result are the characteristics of this Research Compared to other Research. An automatic system based on deep learning is presented to identify and categorize skin cancer which provides high accuracy and speed.

Abstract In recent years, interest in research into the application of intelligent algorithms for diagnosis and categorization of diseases, especially cancer has increased dramatically. Tumor classification is an important task in medical diagnosis. Technological calculations are important due to their classification function in diagnosis of medical illnesses. Diagnosing and classifying medical images is a challenging task. To detect the malignancy of prostate cancer and the opioid or malignant breast cancer, deep neural network classifier, which is based on Tensor flow framework and Keras library, is used. In the training phase, educational images are considered along with the output class for the network. During training, the weight of the filter is updated every time. However, after several replications, optimal weights are updated and the network is trained to extract the best feature from the images. In this research, the proposed method due to using deep neural network and accurate feature extraction provides detection accuracy about 95.83% and 99.5% for breast and prostate cancers, respectively, which is more than 7% compared to other methods. Cancer is one of the most prevalent diseases in the world. Cancer is started from the cells, which are the basic building blocks making the tissue. One of the challenges in medical diagnostic techniques is the difficulty in analyzing dense tissues. Since the detection of the diagnosis by human is time-consuming and has a higher probability of error, the researchers have been trying to detect it automatically by using different algorithms.