Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D and 3D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, 3D networks have some drawbacks due to their high cost on computational resources. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, named RA-UNet, to precisely extract the liver volume of interests (VOI) and segment tumors from the liver VOI. The proposed network has a basic architecture as a 3D U-Net which extracts contextual information combining low-level feature maps with high-level ones. Attention modules are stacked so that the attention-aware features change adaptively as the network goes "very deep" and this is made possible by residual learning. This is the first work that an attention residual mechanism is used to process medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and the 3DIRCADb dataset. The results show that our architecture outperforms other state-of-the-art methods. We also extend our RA-UNet to brain tumor segmentation on the BraTS2018 and BraTS2017 datasets, and the results indicate that RA-UNet achieves good performance on a brain tumor segmentation task as well.

/pdf/ra-unet-a-hybrid-deep-attention-aware-network-to-extract-q770ubv6ol.pdf

RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

String matching has been an extensively studied research domain in the past two decades due to its various applications in the fields of text, image, signal, and speech processing. As a result, choosing an appropriate string matching algorithm for current applications and addressing challenges is difficult. Understanding different string matching approaches (such as exact string matching and approximate string matching algorithms), integrating several algorithms, and modifying algorithms to address related issues are also difficult. This paper presents a survey on single-pattern exact string matching algorithms. The main purpose of this survey is to propose new classification, identify new directions and highlight the possible challenges, current trends, and future works in the area of string matching algorithms with a core focus on exact string matching algorithms.

/pdf/exact-string-matching-algorithms-survey-issues-and-future-349jc1is6b.pdf

Exact String Matching Algorithms: Survey, Issues, and Future Research Directions

The main purpose of human body part detection is to estimate the size, orientation or position of the human body parts within the digital scene information. Estimation of various body parts of the human from an image is a critical step for several model-based systems and body-parts tracking. In this paper, body parts detection for pose estimation is implemented. During foreground silhouettes detection, the proposed method have used segmentation techniques to obtained salient region areas and skin tone detection. After successful silhouettes extraction, body parts estimation is applied by using body parts model. Five basic body key points was determined and in addition seven more body sub key points was estimated with the help of five basic body key points. The estimated key points of the body are then represented using circular marks on the original image. The experimental results over two challenging video datasets such as KTH-multiview football and UCF sports action datasets showed significant accuracies of 90.01% and 86.67. The proposed method performs better than the state-of the-art methods in terms of body-parts detection accuracy.

Human Body Parts Estimation and Detection for Physical Sports Movements

Field Programmable Gate Array (FPGA) is a general purpose programmable logic device that can be configured by a customer after manufacturing to perform from a simple logic gate operations to complex systems on chip or even artificial intelligence systems. Scientific publications related to FPGA started in 1992 and, up to now, we found more than 70,000 documents in the two leading scientific databases (Scopus and Clarivative Web of Science). These publications show the vast range of applications based on FPGAs, from the new mechanism that enables the magnetic suspension system for the kilogram redefinition, to the Mars rovers’ navigation systems. This paper reviews the top FPGAs’ applications by a scientometric analysis in ScientoPy, covering publications related to FPGAs from 1992 to 2018. Here we found the top 150 applications that we divided into the following categories: digital control, communication interfaces, networking, computer security, cryptography techniques, machine learning, digital signal processing, image and video processing, big data, computer algorithms and other applications. Also, we present an evolution and trend analysis of the related applications.

Field Programmable Gate Array Applications—A Scientometric Review

Human body action recognition has drawn a good deal of interest in the community of computer vision, owing to its wide range of applications. Recently, the video / image sequence base action recognition techniques are believed to be ideal for its efficiency and lower cost compared to other techniques such as the ambient sensor and the wearable sensor. However, given to a large amount of variation in human pose and image quality, reliable detection of human action is still a very challenging job for scientists. In this document, we used linear discriminant analysis for the generation of features from the body parts detected. The primary goal of this study is to combine linear discriminant analysis with an artificial neural network for precise human action detection and recognition. Our proposed mechanism detects complicated human actions in two state-of-the-art datasets, i.e. KTH-dataset and Weizmann Human Action. We obtained multidimensional features from twelve body parts, which are estimated from body models. These multidimensional characteristics are used as inputs for the artificial neural network. To access the efficiency of our suggested method, we compared the outcomes with other state-of-the-art classifiers. Experimental results show that our proposed technique is reliable and applicable in health exercise systems, smart surveillance, e-learning, abnormal behavioral detection, protection for child abuse, care of the elderly people, virtual reality, intelligent image retrieval and human computer interaction.

Human Actions Tracking and Recognition Based on Body Parts Detection via Artificial Neural Network

An automatic optic disc localization in retinal images used to screen eye related diseases like diabetic retinopathy. Many techniques are available to detect Optic Disc (OD) in high-resolution retinal images. Unfortunately, there are no efficient methods available to detect OD in low-resolution retinal images. The objective of this research paper is to develop an automated method for localization of Optic Disc in low resolution retinal images. This paper proposes a modified directional matched filter parameters of the retinal blood vessels to localize the center of optic disc. The proposed method was implemented in MATLAB and evaluated both normal and abnormal low resolution retinal images using the subset of Optic Nerve Head Segmentation Dataset (ONHSD) and the success percentage was found to be an average of 96.96% with 23seconds.

/pdf/an-automatic-localization-of-optic-disc-in-low-resolution-rl70ujkezu.pdf

An Automatic Localization of Optic Disc in Low Resolution Retinal Images by Modified Directional Matched Filter

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.

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

Standby and dynamic power minimization using enhanced hybrid power gating structure for deep-submicron CMOS VLSI

In the field of Image processing, to be more specific new technologies are emerging in the domain of segmentation. This is because the new change that has taken place in computer processing application using color images hence color image segmentation is a kind of interest for the recent researchers. In image segmentation, color space plays a vital role. Different types of color space are RGB, HSV, and CIE XYZ etc. The RGB color space is not trust worthy or very accurate for computer vision applications. It is unsuitable for an application like human hand tracking and gesturing. This paper gives a review of the use of different focus of color space like RGB and HSV, as well as for hybrid combination of RGB-HSV. A hybrid technique is proposed that can produce complete information even in a poor background and can give improved color preserve information which is highly useful for hand gesturing.

Review on image segmentation based on color space and its hybrid

Static power dissipation is a dominant field in deep sub-micron technologies. Technology scaling down into submicron technology to achieve higher operating speed of CMOS circuits, the leakage power becomes more and more. As process geometrics move to finer technologies, device consistency and threshold voltage becomes much smaller. When decreasing the supply voltage intends to its decreasing the threshold voltage and oxide thickness. Tremendous increasing in device density and reducing threshold voltage result it vigorously increases the leakage power. The main motivation of this article is to reduce leakage power and to maintain the logic state. We propose a new technique by combine of older technique in accordance with leakage power, critical path delay and feasibility issues. The proposed technique simulated in Tanner-SPICE using 180nm, 90nm and 45nm process technology and convincing power reduction is achieved with minimum critical path delay.

http://ieeexplore.ieee.org/iel7/6571718/6577001/06577157.pdf

Reeba Korah

Papers

An Automatic Localization of Optic Disc in Low Resolution Retinal Images by Modified Directional Matched Filter

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

Standby and dynamic power minimization using enhanced hybrid power gating structure for deep-submicron CMOS VLSI

Review on image segmentation based on color space and its hybrid

A novel sleepy stack 6-T SRAM cell design for reducing leakage power in submicron technologies