Reeba Korah

FPGA Implementation of Integer Transform and Quantizer for H.264 Encoder

This paper deals with the process of Transformation and Quantization that is carried out on each inter-predicted residual block in a video encoding process and their reduced complexity hardware implementation. H.264/AVC utilizes 4 × 4 integer transform, which is derived from the 4 × 4 DCT. We propose, a reduced complexity algorithm and a pipelined structure for the Core forward integer transform module. A multiplier-less architecture is realized with less number of shifts and adds compared to existing works. The corresponding inverse transform is exactly reversible. Each of the transformed coefficients is quantized by a scalar quantizer. The quantization step size can be varied from macroblock to macroblock. The proposed unified pipelined architecture outperforms many recent implementations in terms of gate count and is capable of processing a 4 × 4 residual block in 4 clock cycles.

Motion estimation with candidate block and pixel subsampling algorithm

A novel technique of candidate block selection and spatial sub sampling of pixels is presented. Although FSBMA gives an optimal solution and low control overhead, its hardware implementation will not be feasible in terms of power dissipation, speed of processing, compression ratio and real estate. In FSBMA, for a 16/spl times/16 block, with a search range of -p to p, totally (2p+1)/sup 2/ number of candidate blocks are to be searched. In this paper, we have constructed an N-queen lattice of candidate blocks, to characterize spatial information in all directions. This arrangement reduces the number of candidate blocks to be searched to 5(2p+1). The total computational complexity is found to be reduced to 7.35% compared to FSBMA. The loss in SNR is very negligible and at the worst case comes to an average of less than 0.23 dB in both low motion and medium motion video sequences.

FPGA implementation for speed monitoring and speed control of a DC motor using fuzzy logic

This paper presents the speed monitoring of a DC motor drive using field programmable gate array which provides a faster operation than any other conventional analog methods or digital methods like DSP. The paper consists of two parts. The first part deals with the MATLAB simulation for speed control of a DC motor using fuzzy logic. The most effective method of control is the control of PWM, generated for triggering the converter circuit. The PWM is given through the driver circuit to the half bridge converter, which controls the speed of the motor. The hardware part deals with the speed monitoring of the DC motor using FPGA. In this implementation, the PWM is generated using XBLINX software, which is given to the converter through the FPGA The use of FPGA reduces the processing speed and hence increases the efficiency of the motor. The result of the open loop control is compared with the fuzzy logic control.

MEDICAL IMAGE SEGMENTATION FOR ANATOMICAL KNOWLEDGE EXTRACTION

Computed Tomography-Angiography (CTA) images of the abdomen, followed by precise segmentation and subsequent computation of shape based features of liver play an important role in hepatic surgery, patient/donor diagnosis during liver transplantation and at various treatment stages. Nevertheless, the issues like intensity similarity and Partial Volume Effect (PVE) between the neighboring organs; left the task of liver segmentation critical. The accurate segmentation of liver helps the surgeons to perfectly classify the patients based on their liver anatomy which in turn helps them in the treatment decision phase. In this study, we propose an effective Advanced Region Growing (ARG) algorithm for segmentation of liver from CTA images. The performance of the proposed technique was tested with several CTA images acquired across a wide range of patients. The proposed ARG algorithm identifies the liver regions on the images based on the statistical features (intensity distribution) and orientation value. The proposed technique addressed the aforementioned issues and been evaluated both quantitatively and qualitatively. For quantitative analysis proposed method was compared with manual segmentation (gold standard). The method was also compared with standard region growing.

A Framework of Secured Embedding Scheme Using Vector Discrete Wavelet Transformation and Lagrange Interpolation

Information hiding techniques have a significant role in recent application areas. Steganography is the embedding of information within an innocent cover work in a way which cannot be detected by any person without accessing the steganographic key. The proposed work uses a steganographic scheme for useful information with the help of human skin tone regions as cover image. The proposed algorithm has undergone Lagrange interpolation encryption for enhancement of the security of the hidden information. First, the skin tone regions are identified by using YCbCr color space which can be used as a cover image. Image pixels which belong to the skin regions are used to carry more secret bits, and the secret information is hidden in both horizontal and vertical sequences of the skin areas of the cover image. The secret information will hide behind the human skin regions rather than other objects in the same image because the skin pixels have high intensity value. The performance of embedding is done and is quite invisible by the vector discrete wavelet transformation (VDWT) technique. A new Lagrange interpolation-based encryption method is introduced to achieve high security of the hidden information with higher payload and better visual quality.

Detection of Optic Disc by Line Filter Operator Approach in Retinal Images

The location of Optic Disc (OD) is of critical importance in retinal image analysis. This research paper carries out a new automated methodology to detect the optic disc (OD) in retinal images. OD detection helps the ophthalmologists to find whether the patient is affected by diabetic retinopathy or not. The proposed technique is to use line operator which gives higher percentage of detection than the already existing methods. The purpose of this project is to automatically detect the position of the OD in digital retinal fundus images. The method starts with converting the RGB image input into its LAB component. This image is smoothed using bilateral smoothing filter. Further, filtering is carried out using line operator. After which gray orientation and binary map orientation is carried out and then with the use of the resulting maximum image variation the area of the presence of the OD is found. The portions other than OD are blurred using 2D circular convolution. On applying mathematical steps like peak classification, concentric circles design and image difference calculation, OD is detected. The proposed method was evaluated using a subset of the STARE project’s dataset and the success percentage was found to be 96%.

Computer-aided detection and identification of mine-like objects in infrared imagery using digital image processing

Numerous image processing algorithms are available for detection and identification of buried landmine objects from infrared IR images. There is no universal solution available for this challenging worldwide problem. While developing an algorithm for a specific database, the external factors are not taken into consideration which implies that an algorithm which gives best results for a particular database may not be effective for another database acquired from a dissimilar scenario. Nevertheless, in order to increase the robustness of the detection system, a hybrid of intelligent algorithms and newly proposed methodologies are being used in many situations yielding better results. In that view, an effort is taken to identify the feasibility of some of the available image processing algorithms for their effective application in landmine detection using IR images. The results of outdoor measurements in two different soil types are presented and the implementations of these algorithms are qualitatively and quantitatively compared.

Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes.

The segmentation of organs in CT volumes is a prerequisite for diagnosis and treatment planning. In this paper, we focus on liver segmentation from contrast-enhanced abdominal CT volumes, a challenging task due to intensity overlapping, blurred edges, large variability in liver shape, and complex background with cluttered features. The algorithm integrates multidiscriminative cues (i.e., prior domain information, intensity model, and regional characteristics of liver in a graph-cut image segmentation framework). The paper proposes a swarm intelligence inspired edge-adaptive weight function for regulating the energy minimization of the traditional graph-cut model. The model is validated both qualitatively (by clinicians and radiologists) and quantitatively on publically available computed tomography (CT) datasets (MICCAI 2007 liver segmentation challenge, 3D-IRCAD). Quantitative evaluation of segmentation results is performed using liver volume calculations and a mean score of 80.8% and 82.5% on MICCAI and IRCAD dataset, respectively, is obtained. The experimental result illustrates the efficiency and effectiveness of the proposed method.

Circuit level, 32nm, 1-bit MOSSI-ULP adder: power, PDP and area efficient base cell for unsigned multiplier

Scaling the feature size under 0.1 micron leads to the domination of leakage power along with the consumption by interconnects also. Among alternatives for power optimization, the circuit level approach is the best option for low power consumption which is used in this work to incorporate a novel MOS Switch Integrated Ultra-Low Power (MOSSI-ULP) 1-bit full adder using 32 nm BPTM file. It is then used as a base cell in an array multiplier. Its performance is compared with similar kind of adders like SERF, ULPFA, TGA, TFA and BBL-PT. The analysis shows that the proposed design consumes a maximum of 11 time lower average power than ULPFA. Though MOSSI-ULP switches with a moderate frequency, it requires 17 times less PDP than ULPFA and maximum of 3 times less area.

Segmentation algorithms for automatic detection of retinal images using CVIP tools

Medical image segmentation plays a crucial role in many medical imaging applications by automating or facilating the delineation of anatomical structure and other regions of interest. This paper makes a review on the current segmentation algorithm used for retinal images. These algorithms are divided into three categories according to their main concepts like Edge-based, Region based and Special - theory based Segmentations. The reviewed algorithms are tested using Computer Vision and Image Processing (CVIP) tools.