S. Shirani

McMaster University, Hamilton, Ontario, Canada

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Publications (156)135.33 Total impact

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    ABSTRACT: Microarray technology is a powerful tool in molecular biology which is used for concurrent monitoring of large number of genes expressions. Each microarray experiment produces hundreds of images. Each digital image requires a large storage space. Hence, real-time processing of these images and transmission of them necessitates efficient and custom-made lossless compression schemes. In this paper, we present a hardware scheme for lossless compression scheme of 16-bit microarray images. The most significant part of the image pixels are compressed by classifying them into foreground and background regions. The foreground regions are packed together using a novel compaction unit. Same is performed for the background part. It is shown that a prediction process and a subsequent entropy coding of the packed pixels result in lower entropy. The segmentation map is also compressed with the same proposed compaction unit. The least significant part of the pixels is transmitted without compression. Real-time compression of these images is achieved while the bit-per-pixel values are comparable with the standard offline compression tools.
    Computer Standards & Interfaces 12/2014; · 0.98 Impact Factor
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    ABSTRACT: With the increasing demand for image-based applications, the efficient and reliable evaluation of image quality has increased in importance. Measuring the image quality is of fundamental importance for numerous image processing applications, where the goal of image quality assessment (IQA) methods is to automatically evaluate the quality of images in agreement with human quality judgments. Numerous IQA methods have been proposed over the past years to fulfill this goal. In this paper, a survey of the quality assessment methods for conventional image signals, as well as the newly emerged ones, which includes the high dynamic range (HDR) and 3-D images, is presented. A comprehensive explanation of the subjective and objective IQA and their classification is provided. Six widely used subjective quality datasets, and performance measures are reviewed. Emphasis is given to the full-reference image quality assessment (FR-IQA) methods, and 9 often-used quality measures (including mean squared error (MSE), structural similarity index (SSIM), multi-scale structural similarity index (MS-SSIM), visual information fidelity (VIF), most apparent distortion (MAD), feature similarity measure (FSIM), feature similarity measure for color images (FSIMC), dynamic range independent measure (DRIM), and tone-mapped images quality index (TMQI)) are carefully described, and their performance and computation time on four subjective quality datasets are evaluated. Furthermore, a brief introduction to 3-D IQA is provided and the issues related to this area of research are reviewed.
    06/2014;
  • M. Dadkhah, M. Deen Jamal, S. Shirani
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    ABSTRACT: An implementation of the compressive sensing (CS) method with a CMOS image sensor is presented. The conventional three-transistor active pixel sensor (APS) structure and switched capacitor circuits are exploited to develop an analog implementation of the CS encoding in a CMOS sensor. With the analog implementation, the sensing and encoding are performed in the same time interval and making a real-time encoding process to optimize the frame rate of the imager. A block readout strategy is proposed to capture the required CS measurements for different blocks of the image, rather than the common column-row readout method. All measurement circuits are placed outside the array by this readout strategy, and the imager becomes scalable for larger array sizes. Because there is no extra in-pixel element for the CS measurement process, the fill factor of the imager is the same as its corresponding APS imager without CS. The proposed structure is designed and fabricated in 0.13- (mu ) m CMOS technology for a (2times 2) array. The experimental results confirm the validity of the design in making monotonic and appropriate CS measurements. The functionality of the block readout method and the scalability of the imager are confirmed by fabrication of a (4times 4) block and a (16times 16) array.
    IEEE Sensors Journal 01/2014; 14(8):2897-2909. · 1.85 Impact Factor
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    ABSTRACT: One of the main tools for early diagnosis of breast cancer is digital mammography. These images require large storage space and are difficult to be transmitted over communication links. In this paper we propose a context‐based method for lossless compression of these images. Some modifications are performed to customize the activity level classification model (ALCM) predictor to work best in mammograms. The function of the modified predictor changes for different main regions of these images. Also, best qualities of two other predictors are exploited and the results of the fittest predictor are selected adaptively for the prediction of a pixel. Moreover, context modeling is used for a better categorization of the prediction errors. The proposed algorithm was tested using images from a well‐known database and the results were compared with two standard compression methods of lossless mode of JPEG2000 and JPEG‐LS. The proposed method was proved to produce better compression results than those of the standard algorithms. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
    IEEJ Transactions on Electrical and Electronic Engineering 09/2013; 8(5). · 0.34 Impact Factor
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    Dataset: ICME-poster
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    N Karimi, S Samavi, S Shirani
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    ABSTRACT: RNA interference (RNAi) is considered one of the most powerful genomic tools which allows the study of drug discovery and understanding of the complex cellular processes by high-content screens. This field of study, which was the subject of 2006 Nobel Prize of medicine, has drastically changed the conventional methods of analysis of genes. A large number of images have been produced by the RNAi experiments. Even though a number of capable special purpose methods have been proposed recently for the processing of RNAi images but there is no customized compression scheme for these images. Hence, highly proficient tools are required to compress these images. In this paper, we propose a new efficient lossless compression scheme for the RNAi images. A new predictor specifically designed for these images is proposed. It is shown that pixels can be classified into three categories based on their intensity distributions. Using classification of pixels based on the intensity fluctuations among the neighbors of a pixel a context-based method is designed. Comparisons of the proposed method with the existing state-of-the-art lossless compression standards and well-known general-purpose methods are performed to show the efficiency of the proposed method.
    IEEE journal of biomedical and health informatics. 03/2013; 17(2):259-68.
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    ABSTRACT: Visual surveillance of a designated air space can be achieved by a randomly distributed camera sensor network spread over a large area. The location and field of view of each battery operated sensor, after a calibration phase, will be known to a central processing node. To increase the lifetime of the network, the density of distributed sensors could be such that a subset of sensors can cover the required air space. As a sensor dies another sensor should be selected to compensate for the dead one and reestablish the complete coverage. This process should be continued until complete coverage is not achievable by the existing sensors. Thereafter, a graceful degradation of the coverage is desirable. The goal is to elongate the lifetime of the network while maintaining a maximum possible coverage of the designated air space. Since the selection of a subset of sensors for complete coverage of the target area is an NP-complete problem, we present a number of heuristics for this case. The proposed methods are categorized in two groups. In one category, the sensors are prioritized based on their visual and communicative properties and the selection is performed according to the prioritizing function. In the other group, we propose traditional evolutionary and swarm intelligence algorithms. The performance of the proposed methods is evaluated through extensive simulations.
    Journal of Network and Computer Applications 01/2013; 36(1):409–419. · 1.77 Impact Factor
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    ABSTRACT: Determining an object location in a specific region is an important task in many machine vision applications. Different parameters affect the accuracy of the localization process. The quantization process in charge-coupled device of a camera is one of the sources of error that causes estimation rather than identifying the exact position of the observed object. A cluster of points, in the field of view of a camera are mapped into a pixel. These points form an uncertainty region. In this paper, we present a geometrical model to analyze the volume of this uncertainty region as a criterion for object localization error. The proposed approach models the field of view of each pixel as an oblique cone. The uncertainty region is formed via the intersection of two cones, each emanating from one of the two cameras. Because of the complexity in modeling of two oblique cones' intersection, we propose three methods to simplify the problem. In the first two methods, only four lines are used. Each line goes through the camera's lens, modeled as a pinhole, and then passes one of the four vertices of a square that is fitted around the circular pixel. The first proposed method projects all points of these four lines into an image plane. In the second method, the line-cone intersection is used instead of intersection of two cones. Therefore, by applying line-cone intersection, the boundary points of the intersection of the two cones are determined. In the third approach, the extremum points of the intersection of two cones are determined by the Lagrangain method. The validity of our methods is verified through extensive simulations. In addition, we analyze effects of parameters, such as the baseline length, focal length, and pixel size, on the amount of the estimation error.
    IEEE Sensors Journal 01/2013; 13(11):4236-4246. · 1.85 Impact Factor
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    ABSTRACT: Increasing multimedia content production and exchange has increased the need for better protection of copyright by means of watermarking. Different methods have been proposed to satisfy the tradeoff between imperceptibility and robustness as two important characteristics in watermarking while maintaining proper data-embedding capacity. Recently watermarking methods based on contourlet transform (CT) have demonstrated promising results. In this paper we have improved CT-based watermarking by overcoming some of previous methods' deficiencies. Our method cascades CT and Discrete Cosine Transform (DCT) to embed binary logos as watermark. Image blocks local entropy variations are used to adaptively control the strength factor of watermarking. This has enabled our method to take advantage of all image blocks to elevate the embedding capacity while preserving imperceptibility. Experimental results show the efficiency of the proposed method and better results comparing to similar works in this domain.
    Multimedia and Expo Workshops (ICMEW), 2013 IEEE International Conference on; 01/2013
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    ABSTRACT: The compressive sensing (CS) paradigm uses simultaneous sensing and compression to provide an efficient image acquisition technique. The main advantages of the CS method include high resolution imaging using low resolution sensor arrays and faster image acquisition. Since the imaging philosophy in CS imagers is different from conventional imaging systems, new physical structures have been developed for cameras that use the CS technique. In this paper, a review of different hardware implementations of CS encoding in optical and electrical domains is presented. Considering the recent advances in CMOS (complementary metal-oxide-semiconductor) technologies and the feasibility of performing on-chip signal processing, important practical issues in the implementation of CS in CMOS sensors are emphasized. In addition, the CS coding for video capture is discussed.
    Sensors 01/2013; 13(4):4961-78. · 2.05 Impact Factor
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    ABSTRACT: Nowadays there are many types of display devices with different resolutions and device adaptation is a great challenge in networking. This requires a powerful method for fast resize of images with acceptable quality. Amongst all available methods, such as cropping or scaling, Seam Carving (SC) is a simple and efficient content aware image resizing technique. This technique is inherently a sequential process, which translates into long execution time. In this paper we improve SC by proposing a trellis-based method that finds and removes multiple non-conflicting seams. Better preservation of visually important contents of an image is also achieved by using a new saliency measure called noticeability map. Experiments showed that this approach is up to 13.8 times faster than the original SC, while the produced images are of the same or better visual quality comparing to the original SC or similar methods.
    Multimedia and Expo Workshops (ICMEW), 2013 IEEE International Conference on; 01/2013
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    ABSTRACT: Motion estimation plays a vital role in reducing temporal correlation in video codecs but it requires high computational complexity. Different algorithms have tried to reduce this complexity. However these reduced-complexity routines are not as regular as the full search algorithm (FSA). Also, regularity of an algorithm is very important in order to have a hardware implementation of that algorithm even if it leads to more complexity burden. The goal of this paper is to develop an efficient and regular algorithm which mimics FSA by searching a small area exhaustively. Our proposed algorithm is designed based on two observations. The first observation is that the motion vector of a block falls within a specific rectangular area designated by the prediction vectors. The second observation is that in most cases, this rectangular area is smaller than one fourth of the FSA’s search area. Therefore, the search area of the proposed method is adaptively found for each block of a frame. To find the search area, the temporal and spatial correlations among motion vectors of blocks are exploited. Based on these correlations, a rectangular search area is determined and the best matching block in this area is selected. The proposed algorithm is similar to FSA in terms of regularity but requires less computational complexity due to its smaller search area. Also, the suggested algorithm is as simple as FSA in terms of implementation and is comparable with many of the existing fast search algorithms. Simulation results show the claimed performance and efficiency of the algorithm.
    Multimedia Tools and Applications 12/2012; · 1.01 Impact Factor
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    ABSTRACT: Population of old generation is growing in most countries. Many of these seniors are living alone at home. Falling is amongst the most dangerous events that often happen and may need immediate medical care. Automatic fall detection systems could help old people and patients to live independently. Vision based systems have advantage over wearable devices. These visual systems extract some features from video sequences and classify fall and normal activities. These features usually depend on cameras view direction. Using several cameras to solve this problem increases the complexity of the final system. In this paper we propose to use variations in silhouette area that are obtained from only one camera. We use a simple background separation method to find the silhouette. We show that the proposed feature is view invariant. Extracted feature is fed into a support vector machine for classification. Simulation of the proposed method using a publicly available dataset shows promising results.
    IEEE transactions on bio-medical engineering 11/2012; · 2.15 Impact Factor
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    Reza Soroushmehr, Shadrokh Samavi, Shahram Shirani
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    ABSTRACT: In this paper a new prediction method is proposed for compression of RNAi images. The large number of RNAi images that are produced, from experiments on biological cells for diagnosis and prognosis of diseases, require special compression methods. Images are segmented so that the boundaries of the cells are recognized from the smooth areas. The proposed scheme adaptively changes its function to exploit the spatial features of the cell boundaries and the smooth regions. The proposed predictor has either better performance and comparable complexity, or it has lower complexity and comparable performance, when compared to the existing predictors for this specific application.
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    ABSTRACT: Motion estimation is a critical part of any video coding scheme. Block matching schemes, though being easy to implement, could produce poor results when multiple moving objects exist in one block. In this paper a new mesh-based algorithm is presented which uses affine transforms. The algorithm searches for codirectionality among pixels of a mesh element. When objects pass each other in a scene the superiority of the proposed algorithm is more apparent. The results from implementation of the proposed algorithm show its overall advantage in most instances with respect to comparable algorithms of its class.
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    ABSTRACT: A new compression algorithm is proposed in this paper which uses the contourlet transform. Unlike contourlet-based non-linear approximation (NLA) compression algorithms, the proposed algorithm modifies the coefficients in a controlled manner. The modification is performed so that the difference between a modified coefficient and its original value is within a certain range. To achieve higher compression, the modifications are performed with the goal of minimizing the entropy of the coefficients. The implementation results show that our algorithm produces images with higher PSNRs, for similar bit-rate conditions, as compared to NLA compression algorithms. Furthermore, the visual quality of the images produced by our algorithm is higher than the mentioned NLA algorithms. The implementation results also show the superiority of our algorithm over WBCT algorithm which is based on the joint application of wavelet and contourlet transforms.
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    ABSTRACT: Population of old generation that live alone is growing in most countries. Surveillance systems help them stay home and reduce the burden on the healthcare system. Automatic visual surveillance systems have advantages over wearable devices. They extract features from video sequences and use them for event classification. But these features are dependent on the position of cameras relative to the person. Therefore they need multi-camera for more accuracy that increases cost and complexity. In this paper we propose using silhouette area combined with inclination angle as robust features that can be measured using only one camera with an arbitrary direction. Through rigorous simulations on a publicly available dataset the error rate of the system is found to be less than 1%.
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Publication Stats

850 Citations
135.33 Total Impact Points

Institutions

  • 2000–2013
    • McMaster University
      • Department of Electrical and Computer Engineering
      Hamilton, Ontario, Canada
  • 2007–2009
    • Isfahan University of Technology
      • Department of Electrical and Computer Engineering
      Eşfahān, Ostan-e Esfahan, Iran
    • LSI Corporation
      San Jose, California, United States
  • 1999–2000
    • University of British Columbia - Vancouver
      • Department of Electrical and Computer Engineering
      Vancouver, British Columbia, Canada