Effect of Initial Grain Size on Friction Stir Weldability for Rolled and Cast CoCrFeMnNi High-Entropy Alloys

Metals and Materials International - This study investigated the influence of the initial grain size on the plastic deformation and tunnel defects that occurred from friction stir welding of...     

FDCNet: filtering deep convolutional network for marine organism classification

Convolutional networks are currently the most popular computer vision methods for a wide variety of applications in multimedia research fields. Most recent methods have focused on solving problems with natural images and usually use a training database, such as Imagenet or Openimage, to detect the characteristics of the objects. However, in practical applications, training samples are difficult to acquire. In this study, we develop a powerful approach that can accurately learn marine organisms. The proposed filtering deep convolutional network (FDCNet) classifies deep-sea objects better than state-of-the-art classification methods, such as AlexNet, GoogLeNet, ResNet50, and ResNet101. The classification accuracy of the proposed FDCNet method is 1.8%, 2.9%, 2.0%, and 1.0% better than AlexNet, GooLeNet, ResNet50, and ResNet101, respectively. In addition, we have built the first marine organism database, Kyutech10K, with seven categories (i.e., shrimp, squid, crab, shark, sea urchin, manganese, and sand).     

Detecting moving objects from a video taken by a moving camera using sequential inference of background images

This paper proposes a method of detecting moving objects using sequential inference of the background in a video taken with a moving camera. In the video taken using a moving camera, all positions of pixels change every frame. The positions of the background pixels in the image frame T are not the same as the positions of the background pixels in the image frame T + 1. 2D projective transform can be used to find changes in the pixel position every frame. Bilinear interpolation with four nearest pixels around the pixel in image frame T which corresponds to a pixel in the image frame T+1 can be used for creating a background model at T + 1. Having obtained the background model, a pixel in image frame T + 1 can be determined if it is a background pixel or a foreground pixel. The detection results of the proposed method are compared with the ground truth to determine the effectiveness of the proposed method.     

Active contour model-based segmentation algorithm for medical robots recognition

In this paper, an identifying and classifying algorithm is proposed to solve the problem of recognizing objects accurately and effectively. First, via image preprocessing, initial images are obtained via denoising, smoothness, and image erosion. Then, we use granularity analysis and morphology methods to recognize the objects. For small objects identification and to analyze the objects, we calculate four characteristics of each cell: area, roundness, rectangle factor, and elongation. Finally, we segment the cells using the modified active contour method. In addition, we apply chromatic features to recognize the blood cancer cells. The algorithm is tested on multiple collected clinical cases of blood cell images. The results prove that the algorithm is valid and efficient when recognizing blood cancer cells and has relatively high accuracy rates for identification and classification. The experimental results also certificate the effectiveness of the proposed method for extracting precise, continuous edges with limited human intervention, especially for images with neighboring or overlapping blood cells. In addition, the results of the experiments show that this algorithm can accelerate the detection velocity.     

Deep-Sea Organisms Tracking Using Dehazing and Deep Learning

Mobile Networks and Applications - Deep-sea organism automatic tracking has rarely been studied because of a lack of training data. However, it is extremely important for underwater robots to...     

Arterial hemodynamic analysis on non-enhanced magnetic resonance angiogram using optical flow

Peripheral arterial disease (PAD) is one of the reasons caused to the lower extremity atherosclerotic disease. Its diagnosis is needed to obtain much kind of the information of vascular morphology as well as the blood flow information based on hemodynamics. The diagnosis of the PAD using magnetic resonance imaging (MRI) equipment without contrast medium is available as a useful visual screening in clinical practice. In this paper, we propose a novel method for visualizing hemodynamics to arterial images obtained by a non-contrast enhanced magnetic resonance angiography (MRA) based on the Lucas?CKanade optical flow with the image pyramid processing, and satisfied experimental results are obtained.     

Automatic detection of blood vessels from CTA images employing morphological operation

Recently, the disease arteriosclerosis has rapidly increased, and in particular the type that originates in the peripheral arteries of the hands and feet and is called arteriosclerosis obliterans (ASO). ASO is one of the typical diseases that cause chronic ischemia of the limbs and lead to obstruction of the blood flow. In diagnosing ASO, lower contrast enhanced computed tomography (CT) is useful for observing the arteries. However, it is a huge task for radiologists to separate accurately only the arteries which are involved in the disease, using the manual or semiautomatic proprietary software which is supplied for lower computed tomographic angiography (CTA). We have developed a new technique for the detection of arteries from CTA images by the use of a morphological operation. In this technique an N-Quoit filter, which is a useful filter for the detection of lung nodules, is applied to identify the arteries in CTA images. Some experimental results have shown good performance in the segmentation of arteries. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Detection of Circulating Tumor Cells in Fluorescence Microscopy Images Based on ANN Classifier

Mobile Networks and Applications - Circulating tumor cells (CTCs) is a clinical biomarker for cancer metastasis. CTCs are cells circulating in the body of patients by being separated from primary...     

Effect of post weld heat treatment on weldability of high entropy alloy welds

The effect of post weld heat treatment (PWHT) temperature on laser beam welds in high-entropy alloys (HEAs) using a cold-rolled cantor system (CoCrFeMnNi) was investigated. Laser welding of low heat input was applied to reduce thermal distortion. The cold-rolled HEA welds indicated larger grain size and inferior tensile/hardness properties as compared to the base metal (BM). By applying PWHT, the welds showed superior hardness to the BM with no variation in the face-centred cubic phase and a decrease in the size and fraction of CrMn oxide inclusions. As the PWHT temperature increased (800–1000°C), the variation in the grain size decreased between the weld metal and heat-affected zone, thus resulting in approximately the same tensile strength and elongation of the transverse welds as compared to the BM.