iosr-JVSP   Volume-6 ~ Issue-5 ~ Sep-Oct 2016

ABSTRACT: An efficient architecture is proposed in this paper for high speed Discrete Wavelet Transform computing. The proposed architecture includes Line Buffers, PIPO and Lifting Block. This architecture works in non-separable fashion using a lifting scheme computes 1D, 2D and 3D-DWT at different resolution levels. The lifting scheme represents the fastest implementation of the DWT. A Verilog model is described and synthesized using Xilinx 14.4.The architecture has regular systolic structure, simple control flow for data extraction and small embedded buffers.

Keywords: DWT, HDL, FSM, lifting scheme, Verilog, PIPO.

[1] Kishore Andra, Chaitali Chakrabarti and Tinku Acharya, "A VLSI Architecture for Lifting-Based Forward and Inverse Wavelet Transform" IEEE Transaction on signal processing , VOL. 50, NO. 4, pp.966-977,
[2] Tze-Yun Sung Hsi-Chin Hsin Yaw-Shih Shieh and Chun-Wang Yu "Low-Power Multiplier less 2-D DWT and IDWT Architectures Using 4-tap Daubechies Filters", Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies,10-10 December 2006.
[3] Abdullah AL Muhit, Md. Shabiul Islam and Masuri Othman "VLSI Implementation of Discrete Wavelet Transform (DWT) for Image Compression", 2nd International Conference on Autonomous Robots and Agents, 13-15 December 2004 .
[4] Motra.A.S. Bora.P.K. and Chakrabarti.I. "An efficient hardware implementation of DWT and IDWT",Conference on Convergent Technologies for Asia-Pacific Region, 15-17 October 2003.

ABSTRACT: Object detection and tracking are two fundamental tasks in video camera surveillance. In the case of moving object detection and tracking, an integrated Kalman Filter based system can be used. Automatic object detection is usually the first task in a camera-based surveillance system and background modelling (BM) is commonly used to extract predefined information such as object's shape, geometry and etc., for further processing. But occlusion handling is required to perform advanced recognition and segmentation processes................

Keywords: Object detection, tracking, recognition, background modelling, HoG, Kalman filter, SVM

[1] Shuai Zhang, Chong Wang, Shing-Chow Chan, Xiguang Wei, and Check-Hei Ho, "New Object Detection, Tracking, and Recognition Approaches for Video Surveillance Over Camera Network Sensors Journal, IEEE, Volume: 15, Issue: 5, May 2015.
[2] Akhil A. Vijay, Anoop K. Johnson, "An Integrated System for Tracking and Recognition using Kalman Filter", IEEE International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), July 2014
[3] Oytun Akman, A. Aydin Alatan and Tolga C iloglu. "Multi-Camera Visual Surveillance for Motion Detection, Occlusion Handling, Tracking and Event Recognition", Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications - M2SFA2 2008, Oct 2008, Marseille, France
[4] A. Yilmaz, O. Javed, and M. Shah, "Object tracking: A survey," ACM Comput. Surv. vol. 38, no. 4, pp. pp. 1–13, 2006.
[5] Chun Yuan, Wei Xu, "Multi-Object Events Recognition from Video Sequences using Extended Finite State Machine", 2011 4th International Congress on Image and Signal Processing

ABSTRACT: Skin diseases rate has been increasing for past few decades. Psoriasis is chronic inflammatory skin disease which affects more thgan 3% of population. One of the risk factor in skin cancer is unprotected exposer to UV radiation, which causes various skin diseases. For early diagnosis of skin cancer, psoriasis and dermatophytosis and increases chance for cure significantly. Therefore proposed system used for early prevention and detection named a Image analysis system to detects skin diseases. The image analysis technique where user will able to take skin images of different mole type or rashes type. System will process and analyse the images, which provide notification to user you need medical help. This system provides automatic skin diseases prevention and detection.

Keywords: Enhancement, Segmentation, statistical analysis, Adaboost classifier.

[1]. B.V.Dhandra, Shridevi Soma, Shweta Reddy, "Color Histogram Approach For Analysis Of Psoriasis Skin Disease", IEEE system Journal, vol.99,pp. 25-29.
[2]. A. Karargyris, O. Karargyris, A. Pantelopoulos, "DERMA/ care: An Advanced Image- Processing Mobile Application for Monitoring Skin Cancer,"in IEEE 24th International Conference on Tools with Artificial intelligence(ICTAI), 2012, PP.1-7
[3]. R. Siegel, D. Naishadhama, A. Jemal, "Cancer Statistics, 2012,"CA: a cancer journal for clinicians, vol.62,2012, pp.10-29.
[4]. T. Wadhawan, N. Situ, K. Lancaster, X. Yuan, G. Zouridakis, "SkinScan: A Portable Library for Melanoma Detection On Handheld devicees," in IEEE International Symposium On Biomedical Images: from nano to micro,2011,2011,pp.133-136.
[5]. Omar Abuzaghleh, Buket D. Barkana, Miad Faezipour, "SKIN cure: A Real Time Image Analysis System to Aid in the malignant melanoma Prevention and Early Detection."Member IEEE SSIAI 2014, pp. 85-88.

ABSTRACT: Super-resolution (SR) become the most important task in image processing because of requirement of high quality of images. It becomes popular in technology like computer vision and pattern recognition. In Single Image Super-resolution (SR) uses the sequences of single low resolution (LR) images from high resolution (HR) to improve the quality of image. The SR is an image processing problem which aims to generate a high resolution (HR) image from low resolution input image. The SR is difficult because of the missing information in the given LR image................

Keywords: Accuracy, bi-linear SR, Directional group sparsity, PSNR, Super-Resolution.

[1]. Xiaoyan Li, Hongjie He, Ruxin Wang, and Dacheng Tao, "Single Image Super resolution via Directional Group Sparsity and Directional Features", IEEE transaction on image processing, Vol. 24, No. 9 Sept 2015.
[2]. X. Li and M. T. Orchard, "New edge-directed interpolation," IEEE Trans. Image Process., vol. 10, no. 10, pp. 1521–1527, Oct. 2001.
[3]. X. Zhang and X. Wu, "Image interpolation by adaptive 2D autoregressive modeling and soft-decision estimation," IEEE Trans. Image Ptocess, vol. 17, no. 6, pp. 887–896, Jun. 2008.
[4]. D. Tao, L. Jin, Y. Wang, and X. Li, "Person reidentification by minimum classification error-based KISS metric learning," IEEE Trans. Cybern., vol. 45, no. 2, pp. 242–252, Feb. 2015.
[5]. C. Xu, D. Tao, and C. Xu, "Multi-view intact space learning," IEEE Trans. Pattern Anal. Mach. Intell., Mar. 2015, doi: 10.1109/TPAMI.2015.2417578