Safety in swimming pools is a crucial issue. In this paper, a real time drowning detection method based on HSV color space analysis is presented which uses prior knowledge of the video sequences to set the best values for the color channels. Our method uses a HSV thresholding mechanism along with Contour detection to detect the region of interest in each frame of video sequences. The presented software can detect drowning person in indoor swimming pools and sends an alarm to the lifeguard rescues if the previously detected person is missing for a specific amount of time. The presented algorithm for this system is tested on several video sequences recorded in swimming pools in real conditions and the results are of high accuracy with a high capability of tracking individuals in real time. According to the evaluation results, the number of false alarms generated by the system is minimal and the maximum alarm delay reported by the system is 2.6 sec which can relatively be reliable compared to the acceptable time for rescue and resuscitation.

Image Enhancement is the method of improving the visibility of given image. Image Properties are used for the analysis of Quality of the given image. Various image Properties considered to improve the quality of the image. The Classification or grouping of images can be made by applying unsupervised image Classification algorithm. In our proposed method, various image properties are studied and an Adaptive K-means Clustering method is applied for Fractal image with Entropy Properties. The images are to enhanced on the basis of its grouping automatically. The resulted Classification can be acceptable by the user since the grouping is made on the type of the image i.e., Good Visible, Moderate and Blur images.

Feature extraction and classification processes while developing Optical Character Recognition (OCR) systems involve massive computations and quite expensive especially for South Indian scripts. Multiple combinations of vowels and consonants along with its modifiers led to generation of huge number of classes with respect to character recognition systems. The feature extraction and classification of characters from such huge number of classes in south Indian language OCRs remains as a non-trivial problem. This paper proposes a technique for feature extraction and classification of Telugu handwritten script based on customized template matching approach with the support of caching technique for better performance. The technique of caching is implemented using main database with a cache database maintaining the frequently used character templates for set of all character templates. The XML database is used for defining the classes for various character templates and the class representations are provided using a novel class structure designed based on XML tags. The proposed system exhibits the recognition efficiency on our own test dataset with an overall accuracy of 83.55% for handwritten characters.

Due to the wide usage of digital filters in communication systems, reliability and area has to be considered and deficiency tolerant channel usage are required. Throughout the decades, there are number of techniques that have been proposed to achieve fault tolerance. As the number of parallel filters are increasing in any digital device, the redundancy module should also be small in size. In this paper, a simple technique of constant multiplication reduction method is introduced in the Error Correction Codes (ECC) based parallel filters in order to reduce the size of the redundant module. Main agenda is to reduce the size of the redundant module by not affecting the functionalityof the system. The proposed scheme is coded in HDL and simulation results are obtained by using Xilinx 12.1i. The presented result shows that the slices can be reduced and hence the size. As a result of reduction in size, the optimization of area can also be concluded.

X-rays and other medical images are distorted because of the limitations of the Imaging system. The other source from where the distortions get in are the transmission channels. The distortions are generally noise and blur. Unless and until the medical images are free of noise and blur they cannot be used by medical professionals to the full extent for diagnosis purpose. Therefore these images must be restored properly before they are used for diagnosis purpose. There are different restoration techniques out of which one is Blind Image Deconvolution. X-ray images restored with this technique have ringing effect in them. Using edgetaper (matlab function) prior to Blind Image Deconvolution reduces the ringing effect to an extent. This paper presents Blind Deconvolution algorithm with weights which gives lesser ringing effect in X-ray images when they are restored.

Because of increasing terrorist activities, the resolution of video cameras and the number of cameras deployed for surveillance are increasing exponentially – producing huge amount of video data. Manual analysis of this large volume of video data by human operators for crime scene and forensic analysis is neither reliable nor scalable. This has generated enormous interest in research activities related to automation of video surveillance systems which allows real-time automatic extraction and analysis of information from live incoming video streams and enables automatic detection and tracking of targets without human intervention. To meet the real-time requirements of automated video surveillance systems, very different technologies and design methodologies have been used in literature. These range from use of General Purpose Processors (GPPs) or special purpose Digital Signal Processors (DSPs) or Graphics Processing Units (GPUs) to Application Specific Integrated Circuits (ASICs) or Applications Specific Instruction Set Processors (ASIPs) or even programmable logic devices like Field Programmable Gate Arrays (FPGAs). FPGAs provide real-time performance that is hard to achieve with GPPs/DSPs, limit the extensive design work, time, and cost required for ASICs, and allow algorithmic changes in later stages of system development. Due to these features FPGAs are being increasingly used for prototyping automated video surveillance system quickly. In this paper we present the top level description of a complete automated video surveillance system along with the elaboration of different challenges/issues involved in its design and implementation, a comparative analysis of design methodologies and existing FPGA platforms, complete design flow for prototyping the FPGA-based automated video surveillance system, and details of various primary input/output interfaces required for designing smart automated video surveillance systems for future.

An efficient approach for scene classification is necessary for automatically labeling an image as well as for retrieval of desired images from large scale repositories. In this paper machine learning and computer vision techniques have been applied for scene classification. The system is based on feature fusion method with holistic visual color, texture and edge descriptors. Color moments, Color Coherence Vector, Color Auto Correlogram, GLCM, Daubechies Wavelets, Gabor filters and MPEG-7 Edge Direction Histogram have been used in the proposed system to find the best combination of features for this problem. Two state-of-the-art soft computing machine learning techniques: Support vector machine (SVM) and Artificial Neural Networks have been used to classify scene images into meaningful categories. The benchmarked Oliva-Torralba dataset has been used in this research. We report satisfactory categorization performances on a large data set of eight categories of 2688 complex, natural and urban scenes. Using a set of exhaustive experiments our proposed system has achieved classification accuracy as high as 92.5% for natural scenes (OT4) and as high as 86.4% for mixed scene categories (OT8). We also evaluate the system performance by predictive accuracy measures namely sensitivity, specificity, F-score and kappa statistic.

In recent years chaos has received a great deal of attention from the researches specialized in communications, signal and image processing. The complexity property of the chaotic signal raised the idea of using such signals in secure communications. Digital image watermarking is a technique mainly developed for copyright protection and image authentication and it can be considered as one application area of the secure communication. In this study, a chaos based digital image watermarking algorithm based on redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) is proposed. To the best of our knowledge, there do not exist any digital watermarking scheme combining RDWT, SVD and chaos. Robustness and invisibility of the proposed method are improved by using the logistic mapping function to generate a chaotic image matrix serving as the watermark that is used to modify the singular values of the low frequency sub-band of the cover image obtained by applying RDWT. The method is shown to be robust against both the geometrical and image processing attacks and to provide better watermark concealment via computer simulations. Using a chaotic signal as the watermark allows the proposed scheme to meet the security requirements as well.