Mr. Muhammad Imran Profile

Muhammad Imran

PhD Student at Mid Sweden Univ

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Publications (8)

SPIE Journal Paper | 2 May 2016

Exploration of preprocessing architectures for field-programmable gate array-based thermal-visual smart camera

Muhammad Imran, Bernhard Rinner, Sajjad Zandi Zand, Mattias O'Nils

JEI, Vol. 25, Issue 04, 041006, (May 2016) https://doi.org/10.1117/12.10.1117/1.JEI.25.4.041006

KEYWORDS: Cameras, Thermography, Visualization, Field programmable gate arrays, Imaging systems, Image compression, Image processing, Binary data, Sensors, Telecommunications

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Proceedings Article | 31 May 2013 Paper

Solution space exploration of volumetric surveillance using a general taxonomy

Naeem Ahmad, Muhammad Imran, Khursheed Khursheed, Najeem Lawal, Mattias O'Nils

Proceedings Volume 8713, 871317 (2013) https://doi.org/10.1117/12.2022478

KEYWORDS: Surveillance systems, Surveillance, Cameras, Sensors, Taxonomy, Systems modeling, Image resolution, Video surveillance, Optical sensors, Actinium

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Proceedings Article | 19 February 2013 Paper

Binary video codec for data reduction in wireless visual sensor networks

Khursheed Khursheed, Naeem Ahmad, Muhammad Imran, Mattias O'Nils

Proceedings Volume 8656, 86560L (2013) https://doi.org/10.1117/12.2003110

KEYWORDS: Image compression, Image segmentation, Binary data, Visualization, Video compression, Video, Sensor networks, Image processing algorithms and systems, Sensors, Image processing

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Wireless Visual Sensor Networks (WVSN) is formed by deploying many Visual Sensor Nodes (VSNs) in the field. Typical applications of WVSN include environmental monitoring, health care, industrial process monitoring, stadium/airports monitoring for security reasons and many more. The energy budget in the outdoor applications of WVSN is limited to the batteries and the frequent replacement of batteries is usually not desirable. So the processing as well as the communication energy consumption of the VSN needs to be optimized in such a way that the network remains functional for longer duration. The images captured by VSN contain huge amount of data and require efficient computational resources for processing the images and wide communication bandwidth for the transmission of the results. Image processing algorithms must be designed and developed in such a way that they are computationally less complex and must provide high compression rate. For some applications of WVSN, the captured images can be segmented into bi-level images and hence bi-level image coding methods will efficiently reduce the information amount in these segmented images. But the compression rate of the bi-level image coding methods is limited by the underlined compression algorithm. Hence there is a need for designing other intelligent and efficient algorithms which are computationally less complex and provide better compression rate than that of bi-level image coding methods. Change coding is one such algorithm which is computationally less complex (require only exclusive OR operations) and provide better compression efficiency compared to image coding but it is effective for applications having slight changes between adjacent frames of the video. The detection and coding of the Region of Interest (ROIs) in the change frame efficiently reduce the information amount in the change frame. But, if the number of objects in the change frames is higher than a certain level then the compression efficiency of both the change coding and ROI coding becomes worse than that of image coding. This paper explores the compression efficiency of the Binary Video Codec (BVC) for the data reduction in WVSN. We proposed to implement all the three compression techniques i.e. image coding, change coding and ROI coding at the VSN and then select the smallest bit stream among the results of the three compression techniques. In this way the compression performance of the BVC will never become worse than that of image coding. We concluded that the compression efficiency of BVC is always better than that of change coding and is always better than or equal that of ROI coding and image coding.

Proceedings Article | 19 February 2013 Paper

Analysis and characterization of embedded vision systems for taxonomy formulation

Muhammad Imran, Khaled Benkrid, Khursheed Khursheed, Naeem Ahmad, Mattias O’Nils, Najeem Lawal

Proceedings Volume 8656, 86560J (2013) https://doi.org/10.1117/12.2000584

KEYWORDS: Taxonomy, Embedded systems, Feature extraction, Cameras, Systems modeling, Transform theory, Image segmentation, Visual process modeling, Imaging systems, Performance modeling

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Proceedings Article | 2 May 2012 Paper

Selection of bi-level image compression method for reduction of communication energy in wireless visual sensor networks

Khursheed Khursheed, Muhammad Imran, Naeem Ahmad, Mattias O'Nils

Proceedings Volume 8437, 84370M (2012) https://doi.org/10.1117/12.923716

KEYWORDS: Image compression, Visualization, Wireless communications, Sensor networks, Image processing, Image sensors, Data communications, Visual compression, Image quality standards, Medical imaging

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Proceedings Article | 2 May 2012 Paper

Cost optimization of a sky surveillance visual sensor network

Naeem Ahmad, Khursheed Khursheed, Muhammad Imran, Najeem Lawal, Mattias O'Nils

Proceedings Volume 8437, 84370U (2012) https://doi.org/10.1117/12.924344

KEYWORDS: Cameras, Sensors, Image sensors, Video surveillance, Sensor networks, Visualization, Surveillance, Image processing, Video, Image resolution

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Proceedings Article | 2 May 2012 Paper

Complexity analysis of vision functions for implementation of wireless smart cameras using system taxonomy

Muhammad Imran, Khursheed Khursheed, Naeem Ahmad, A. Waheed Malik, Mattias O'Nils, Najeem Lawal

Proceedings Volume 8437, 84370C (2012) https://doi.org/10.1117/12.923797

KEYWORDS: Cameras, Imaging systems, Systems modeling, Taxonomy, Visual process modeling, Image processing, Image segmentation, Transform theory, Image compression, Image filtering

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Proceedings Article | 2 May 2012 Paper

Real-time machine vision system using FPGA and soft-core processor

Abdul Waheed Malik, Benny Thörnberg, Xiaozhou Meng, Muhammad Imran

Proceedings Volume 8437, 84370Z (2012) https://doi.org/10.1117/12.927854

KEYWORDS: Field programmable gate arrays, Machine vision, Image processing, Cameras, Feature extraction, Imaging systems, Clocks, Image segmentation, Visual process modeling, Computer programming

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Showing 5 of 8 publications

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