Sharif Digital Repository

Automatic recognition of facial expression and facial identity from visual data are two challenging problems that are tied together. In the past decade, researchers have mostly tried to solve these two problems separately to come up with face identification systems that are expression-independent and facial expressions recognition systems that are person-independent. This paper presents a new framework using sparse representation for simultaneous recognition of facial expression and identity. Our framework is based on the assumption that any facial appearance is a sparse combination of identities and expressions (i.e., one identity and one expression). Our experimental results using the CK+... 

Users often have very specific visual content in mind that they are searching for. The most natural way to communicate this content to an image search engine is to use key-words that specify various properties or attributes of the content. A naive way of dealing with such multi-attribute queries is the following: train a classifier for each attribute independently, and then combine their scores on images to judge their fit to the query. We argue that this may not be the most effective or efficient approach. Conjunctions of attribute often correspond to very characteristic appearances. It would thus be beneficial to train classifiers that detect these conjunctions as a whole. But not all... 

In this paper, we present a new method to solve tracking problem. The proposed method combines sparse representation and motion estimation to track an object. Recently. sparse representation has gained much attention in signal processing and computer vision. Sparse representation can be used as a classifier but has high time complexity. Here, we utilize motion information in order to reduce this computation time by not calculating sparse codes for all the frames. Experimental results demonstrates that the achieved result are accurate enough and have much less computation time than using just a sparse classifier  

In this work we present a sparse dictionary learning method, specifically tuned to solve the tracking problem. Recently, sparse representation has drawn much attention because of its genuineness and strong mathematical background. In this paper we present an online method for dictionary learning which is desirable for problems such as tracking. Online learning methods are preferable because the whole data are not available at the current time. The presented method tries to use the advantages of the generative and discriminative models to achieve better performance. The experimental results show our method can overcome many tracking challenges  

Since tracking algorithms should be robust with respect to appearance changes, online algorithms has been investigated recently instead of offline ones which has shown an acceptable performance in controlled environments. The most challenging issue in online algorithms is updating of the model causing tracking failure because of introducing small errors in each update and disturbing the appearance model (drift). in this paper, we propose an online generative tracking algorithm in order to overcome the challenges such as occlusion, object shape changes, and illumination variations. In each frame, color distribution of target candidates is obtained and the candidate having the lowest distance... 

Integral imaging could be considered as one of the prospective methods for recording and displaying 3D images based on its distinct features. Some of the most important challenges with this approach are the field of view and resolution limitation. In this work we investigate using frequency division multiple access (FDMA) idea for solving this problem. Simulation results show an increase of more than ten percent in the performance of the 3D reconstructed images using the proposed method  

Since expertise elicited from past resolved cases plays an important role in medical application and images acquired from various cases have a great contribution to diagnosis of the abnormalities, Content based medical image retrieval has become an active research area for many scientists, In this article we proposed a new framework to retrieve visually similar images from a large database, in which visual relevance is regarded as much as the semantic category similarity, we used optimized wavelet transform as the multi-resolution analysis of the images and extracted various statistical SGLDM features from different resolutions then after reducing feature space we used error correcting codes... 

In this research, new software package for neutronic calculations, especially kinetic parameters of PWR reactors, has been developed. The program used to link the WIMS-D5, BORGES and CITVAP nuclear codes has been written in Visual C# programming language. This software was used for calculation of kinetic parameters of WER-1000 and NOK Beznau reaction The ration (βeff)i/(βeff)corewhich are an important input data for the reactivity accident analysis, were also calculated. The results were compared with final safety analysis report (FSAR) and published documents. Copyright  

Noise interference and data loss are two major problems that affect the processing results of image data transmission and storage. Restoration of the lost information of an image based on the existing information is the essence of inpainting. In this paper a new algorithm based on Sample and Hold interpolation and Iteration is proposed for reconstructing damaged images from existing regions and is compared to some other methods. The experimental results show the superiority of the visual quality and PSNR performance of the proposed method. It is observed that this approach can efficiently fill in the holes with visually plausible information  

This paper presents the design performance characteristics of a novel Robotic Mannequin, “RoMa”, developed for the fashion industry. RoMa, a full-body humanoid social robot platform, is currently in the final stages of development for visual merchandising to promote customer appeal. RoMa is characterized by nine features which are listed as: appealing appearance, avoiding the uncanny valley, easy maintenance, interactive, light weight, low developmental cost, suitable body movements, suitable color, and user friendly. In this paper, important design procedures and considerations are briefly presented and discussed. © 2017, Springer International Publishing AG  

Visual attributes are great means of describing images or scenes, in a way both humans and computers understand. In order to establish a correspondence between images and to be able to compare the strength of each property between images, relative attributes were introduced. However, since their introduction, hand-crafted and engineered features were used to learn increasingly complex models for the problem of relative attributes. This limits the applicability of those methods for more realistic cases. We introduce a deep neural network architecture for the task of relative attribute prediction. A convolutional neural network (ConvNet) is adopted to learn the features by including an... 

In this paper, a solution is proposed to render both rough and detailed images information using only audio-range sound. The procedure is implemented in consecutive stages with stage-dependent parameters selected by the user. The first stage consists of edge detection and tracking of the boundaries of the objects to obtain the sketch of the image. In the second stage, the user can selectively access the details of the image, such as brightness, texture and spatial position of the objects. Practical methods are proposed and tested which accelerate the training process. As a particular application, an educational module is presented which assists students of a special school for the blind by... 

One of the main properties of digital watermarking methods is their security against attacks. In this paper, a novel attack against an adaptive semi-fragile image watermarking is proposed. In this attack, watermarking key and watermark are estimated by using several watermarked images. In order to improve the watermarking scheme against the proposed attack, the entropy of image blocks is utilized. Using entropy is compatible with Human Visual System (HVS); therefore it is suitable to determine the weight of watermark in image blocks. Since entropy is a sensitive feature, it is used to improve the watermarking method performance against the proposed attack. It is shown that this modification... 

This paper, in the domain of digital media, introduces the theoretical basis of possibilistic art. It models the bases of visual art in the atmosphere of possibilistic thought and the fuzzy geometry by introducing meaningful forms and introduces a way for recording and displaying emotional-behavioral responses of artist in the visualcomputational space. Finally, as a function of presented concepts, the paper introduces a semi-algorithm for meaningful deformation. This article, by representation of a method based on the eastern thinking and a computational thinking of the west, make a step in the way of eliminating the theoretic and instrumental shortages of visual arts of Iran in the grounds... 

Automatic image annotation (AIA) is an image retrieval mechanism to extract relative semantic tags from visual content. So far, the improvement of accuracy in newly developed such methods have been about 1 or 2% in the F1-score and the architectures seem to have room for improvement. Therefore, the authors designed a more detailed architecture for AIA and suggested new algorithms for its main parts. The proposed architecture has three main parts: feature extraction, learning, and annotation. They designed a novel learning method using machine learning and probability bases. In the annotation part, they suggest a novel method that gains the maximum benefit from the learning part. The... 

Transmission of video over error prone and still bandwidth limited wireless channels demand high compression efficiency and resilience to packet losses and errors. Scalable or layered video coding applied to highly compression efficient codecs is an ideal solution to the problem. However, scalability reduces compression efficiency of the coders. In this paper we show how compression efficiency of two-layer SNR scalable video coders can be retained via joint base-enhancement layer optimization. Simulation results show that joint base-enhancement layer optimization significantly outperforms separate optimization of the layers, and it closely follows the compression performance of the... 

Real-time video transmission is considered as an important means for information distribution. One major application of it is E-learning, which requires real-time video processing and transmission. On the other hand, the process of cut detection is a fundamental component in automatic video browsing, indexing, searching, retrieval, and archiving. This paper introduces a new video cut detection technique that uses dominant lines and angles extracted from edge information of the video contents. To the best of our knowledge, it is the first works done for cut detection in E-learning application. This method is compatible with our application's requirements and has a low complexity and high... 

This paper presents a novel video encoding method for cooperative Educational Dissemination Systems. Taking into consideration the inherent characteristics of distance learning video streams, existing a few moving objects in the scene and objects having slow motions, we propose a novel content-based video encoding method which is very efficient on low bandwidth channels. In the encoding process, we apply a background subtraction algorithm for motion segmentation with a novel statistical background modeling. In each frame, the moving objects are extrapolated with rectangular bounding boxes which are the only data send over the low bandwidth channel. In the decoding process, we propose a new...