V. R. Satpute

Video encoding techniques based on 3D-DWT

This paper deals with the application of Spatial and Temporal DWT (Discrete Wavelet transform) on the videos. Here we will discuss about three mechanisms and their performance on videos at increased DWT level. In any video processing algorithm, memory is the major criteria. In these three mechanisms dynamic (automatic) DWT level selection and manual level selection is implemented. Here we will also discuss about implementation of different DWT level in spatial and temporal domain. In this paper Haar wavelet is taken as the reference as it has its inherent properties and ease of implementation.

Design of Eight Bit Novel Reversible Arithmetic and Logic Unit

Digital circuits made up of classical gates dissipate significant amount of energy as bits are erased during logic operations. Use of reversible logic gates to implement such circuits can significantly reduce the power consumed. This paper covers various aspects about reversible computing and reversible logic gates. Furthermore in this paper we have tried to design a reversible implementation of eight bit arithmetic and logic unit, optimal in terms of number of gates used and number of garbage outputs produced.

A novel approach based on variance for local feature analysis of facial images

A low dimensional representation of sensory signals is a key for solving many of the computational problems encountered in high level vision. In this paper, a comparison of face recognition techniques using principal component analysis (PCA) is done with local feature analysis (LFA) and an alternate method based on variance for quickly finding the local feature points on face images is also proposed. The LFA method is an extension of the eigenfaces method and gives a low-dimensional output for face representation. Principal component analysis (PCA) that is used for dimensionality reduction in the eigenfaces technique leads to global outputs, which are non-topographic and are not biologically plausible. On the other hand, the local feature analysis (LFA) technique yields local, topographic outputs which are sparsely distributed. They are effectively low dimensional but retain all the characteristics of the global modes. Local representations are desirable since they offer robustness against variability due to changes in the localised regions of the objects. A strategy for recognising faces using LFA is also proposed and several results on reconstruction and recognition are given to compare the performance of the variance method with that of LFA and PCA.

A simple and efficient approach for medical image security using chaos on EZW

In this digital era medical image security is important as they carry highly personal data, which should be kept away from the hackers and unauthorized persons, as these images are transmitted/stored among hospitals and also by health insurance companies. Chaos based cryptography/security has been increased in recent years. In this paper we proposed an algorithm, in which security can be given along with effective compression of image. Our process of providing image security starts with compressing the image using EZW. The output sequence of EZW is converted to 2D and on the 2D data we are going to apply row wise and column wise scrambling algorithm based on chaos. In this paper we are also going to discuss about part of EZW in providing additional security to image along with its main function of compression.

Sign language recognition through kinect based depth images and neural network

Sign language is the language of the people with hearing and speaking disabilities. In it mostly hands are moved in a particular way which along with some facial expression produces a meaningful thought which the speaker would like to convey to others. Using the sign language people with speaking and hearing disabilities can communicate with others who know the language very easily but it becomes difficult when it comes to interacting with a normal person. As a result there is a requirement of an intermediate system which will help in improving the interaction between people with the hearing disabilities as well as with the normal people. In this paper we present a sign language recognition technique using kinect depth camera and neural network. Using the kinect camera we obtain the image of the person standing in front of the camera and then we crop the hand region from the depth image and pre-process that image using the morphological operations to remove unwanted region from the hand image and find the contour of the hand sign and from the particular contour position of the hand we generate a signal on which Discrete Cosine Transform (DCT) is applied and first 200 DCT coefficient of the signal are feed to the neural network for training and classification and finally the network classify and recognize the sign. A data set of sign from 0 to 9 are formed using kinect camera and we tested on 1236 images in the database on which training is applied and we achieved 98% training and an average accuracy for all the sign recognition as 83.5%.

An efficient low dynamic range image compression using improved block based EZW

This papers deals with an efficient image compression technique for images having low dynamic range. The images with low dynamic range generally have low intensity variations. By considering this fundamental characteristic into account we can go for image compression at higher ratio with small modifications to the existing block based EZW algorithm. To achieve the improvement in compression ratio, block-wise Embedded Zero Wavelet (EZW) is applied on the images by forcing all the blocks in the image to take the same number of dominant and sub-ordinate passes. The number of passes applied on each block of the image will be equal to the lowest number of passes taken by one of the blocks in image. This downside the number of passes applied on the image which reduces the number of bits used for encoding the image which successively increase the compression ratio. The proposed algorithm is analyzed with respect to the normal block-wise EZW by mathematical parameters as well as with visual quality. The mathematical parameters chosen for comparison are Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index. The algorithm is tested from low resolution to UHD resolution images.

Image security using chaos and EZW compression

This paper deals with the image security along with compression. Here we have proposed an algorithm using chaos on EZW compression technique to provide security along with image compression. We had chosen chaos for image security due to its robustness to initial condition and mixing property. Our process of providing image security starts with compressing the image using EZW. The output sequence of EZW is converted to 2-D data and on this 2-D data we apply row and column scrambling algorithm based on chaos. In this paper we also discuss about part of EZW in providing additional security to image along with its main function of compression.

Comparative analysis of video compression mechanisms using 3D-DWT with EZW

In this paper, two compression mechanisms based on 3D-Discrete Wavelet Transform (DWT) and 2D Embedded Zero Wavelet (EZW) are compared depending on the mathematical parameters Peak Signal-to-Noise Ratio (PSNR) and compression ratio(CR). In this paper, we are using Haar wavelet decomposition for compression, as it has shown improved compression in recent years when used with the techniques like EZW, SPIHT etc. Haar wavelet is chosen because of its ease of implementation and inherent properties and EZW is chosen for compression. We apply EZW frame-by-frame on the encoded video as it is meant for 2D-data only. Here we are adding the extra blocks for video encoding and decoding before and after the existing compression technique i.e., EZW. So, these mechanisms are very easy to implement by just adding the extra blocks of encoding and decoding.

A blind video watermarking technique for indoor video content protection using Discrete Wavelet Transform

In this paper, an highly effective algorithm for copyright protection is proposed using a simple and efficient embedding technique for DWT-based video watermarking for indoor video watermarking applications. Discrete Wavelet Transform (DWT) is applied on the video frame to achieve frequency domain representation of video sequences. The frequency domain representations of DWT are low pass and high pass components of which the low pass component is used to generate the key for each frame. This way the generated key is used at the receiver for extracting the watermark which results in copyright protection. Blind watermarking technique is followed in this paper, this needs only the key for extraction of hidden watermark. The advantage of this method is that it does not require the original video sequences for extraction. To scrutinize the robustness of the proposed algorithm, the original watermark image is equated with those of extracted watermark images by applying several attacks. The results are computed and the performance is evaluated based on the parameters Peak Signal to Noise Ratio (PSNR), Normalized Correlation Coefficient (NC) as well as Structural Similarity index (SSIM). The results illustrates that the process is a blind watermark technique and is highly robust against the different attacks and also for different noisy environments.

A novel approach based on 2D - DWT and variance method for human detection and tracking in video surveillance applications

Moving object detection is very important in modern world for fast video surveillance. There are various methods used for detecting moving objects out of which frame differencing method is widely used and is most efficient method. In this paper we focus on the surveillance at the most secured areas such as airports, defense establishments, power stations etc. Similarly, the area where no human is allowed without authority to enter such as bank locker rooms, restricted military area etc. automotive surveillance and traffic monitoring plays a vital role. In real time surveillance system, storing the captured video and detecting object are two most important issues. Storing such videos needs more memory and the detection of the object is also need to be fast. To solve these problems compression and fast object detection is required. To detect the moving object, detection of its edges and location in the frame are important steps. In this paper we propose a mechanism to use discrete wavelet transform (DWT) for two purposes for compression and edge detection, whereas to locate the object we propose variance method on to the 2-D DWT outputs of video frames. For this analysis HAAR wavelet is used as reference.