A New Supervised Clustering Framework Using Multi Discriminative Parts and Expectation–Maximization Approach for a Fine-Grained Animal Breed Classification (SC-MPEM)

The goal of few-shot image recognition is to distinguish different categories with only one or a few training samples. Previous works of few-shot learning mainly work on general object images. And current solutions usually learn a global image representation from training tasks to adapt novel tasks. However, fine-gained categories are distinguished by subtle and local parts, which could not be captured by global representations effectively. This may hinder existing few-shot learning approaches from dealing with fine-gained categories well. In this work, we propose a multi-attention meta-learning (MattML) method for few-shot fine-grained image recognition (FSFGIR). Instead of using only base learner for general feature learning, the proposed meta-learning method uses attention mechanisms of the base learner and task learner to capture discriminative parts of images. The base learner is equipped with two convolutional block attention modules (CBAM) and a classifier. The two CBAM can learn diverse and informative parts. And the initial weights of classifier are attended by the task learner, which gives the classifier a task-related sensitive initialization. For adaptation, the gradient-based meta-learning approach is employed by updating the parameters of two CBAM and the attended classifier, which facilitates the updated base learner to adaptively focus on discriminative parts. We experimentally analyze the different components of our method, and experimental results on four benchmark datasets demonstrate the effectiveness and superiority of our method.

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Non-locally Enhanced Feature Fusion Network for Aircraft Recognition in Remote Sensing Images

Remote Sensing ◽

10.3390/rs12040681 ◽

2020 ◽

Vol 12 (4) ◽

pp. 681

Author(s):

Yunsheng Xiong ◽

Xin Niu ◽

Yong Dou ◽

Hang Qie ◽

Kang Wang

Keyword(s):

Remote Sensing ◽

Loss Function ◽

Feature Fusion ◽

Remote Sensing Images ◽

Feature Maps ◽

Long Distance ◽

Test Dataset ◽

Fine Grained ◽

Recognition Ability ◽

Discriminative Parts

Aircraft recognition has great application value, but aircraft in remote sensing images have some problems such as low resolution, poor contrasts, poor sharpness, and lack of details caused by the vertical view, which make the aircraft recognition very difficult. Especially when there are many kinds of aircraft and the differences between aircraft are subtle, the fine-grained recognition of aircraft is more challenging. In this paper, we propose a non-locally enhanced feature fusion network(NLFFNet) and attempt to make full use of the features from discriminative parts of aircraft. First, according to the long-distance self-correlation in aircraft images, we adopt non-locally enhanced operation and guide the network to pay more attention to the discriminating areas and enhance the features beneficial to classification. Second, we propose a part-level feature fusion mechanism(PFF), which crops 5 parts of the aircraft on the shared feature maps, then extracts the subtle features inside the parts through the part full connection layer(PFC) and fuses the features of these parts together through the combined full connection layer(CFC). In addition, by adopting the improved loss function, we can enhance the weight of hard examples in the loss function meanwhile reducing the weight of excessively hard examples, which improves the overall recognition ability of the network. The dataset includes 47 categories of aircraft, including many aircraft of the same family with slight differences in appearance, and our method can achieve 89.12% accuracy on the test dataset, which proves the effectiveness of our method.

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MultiCAM: Multiple Class Activation Mapping for Aircraft Recognition in Remote Sensing Images

Remote Sensing ◽

10.3390/rs11050544 ◽

2019 ◽

Vol 11 (5) ◽

pp. 544 ◽

Cited By ~ 13

Author(s):

Kun Fu ◽

Wei Dai ◽

Yue Zhang ◽

Zhirui Wang ◽

Menglong Yan ◽

...

Keyword(s):

Remote Sensing ◽

Feature Fusion ◽

State Of The Art ◽

Remote Sensing Images ◽

Visual Classification ◽

Fine Grained ◽

Object Parts ◽

Multiple Class ◽

Activation Mapping ◽

Discriminative Parts

Aircraft recognition in remote sensing images has long been a meaningful topic. Most related methods treat entire images as a whole and do not concentrate on the features of parts. In fact, a variety of aircraft types have small interclass variance, and the main evidence for classifying subcategories is related to some discriminative object parts. In this paper, we introduce the idea of fine-grained visual classification (FGVC) and attempt to make full use of the features from discriminative object parts. First, multiple class activation mapping (MultiCAM) is proposed to extract the discriminative parts of aircrafts of different categories. Second, we present a mask filter (MF) strategy to enhance the discriminative object parts and filter the interference of the background from original images. Third, a selective connected feature fusion method is proposed to fuse the features extracted from both networks, focusing on the original images and the results of MF, respectively. Compared with the single prediction category in class activation mapping (CAM), MultiCAM makes full use of the predictions of all categories to overcome the wrong discriminative parts produced by a wrong single prediction category. Additionally, the designed MF preserves the object scale information and helps the network to concentrate on the object itself rather than the interfering background. Experiments on a challenging dataset prove that our method can achieve state-of-the-art performance.

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Application of an image management system in microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100129899 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1052-1053

Author(s):

Richard S. Chemock

Keyword(s):

Data Storage ◽

Cost Savings ◽

Image Data ◽

Analytical Techniques ◽

Operational Mode ◽

Fine Grained ◽

Image Recording ◽

Primary Output ◽

Capacity Data ◽

Image Management System

One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.

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Use of fractals to describe microstructures of porous thick-film platinum electrodes

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100121971 ◽

1992 ◽

Vol 50 (1) ◽

pp. 314-315

Author(s):

Steven D. Toteda

Keyword(s):

Fractal Dimension ◽

Power Plants ◽

Electrical Response ◽

Cubic Phase ◽

Platinum Electrodes ◽

Fine Grained ◽

Impedance Response ◽

Platinum Particles ◽

Energy Surfaces ◽

Highly Porous

Zirconia oxygen sensors, in such applications as power plants and automobiles, generally utilize platinum electrodes for the catalytic reaction of dissociating O2 at the surface. The microstructure of the platinum electrode defines the resulting electrical response. The electrode must be porous enough to allow the oxygen to reach the zirconia surface while still remaining electrically continuous. At low sintering temperatures, the platinum is highly porous and fine grained. The platinum particles sinter together as the firing temperatures are increased. As the sintering temperatures are raised even further, the surface of the platinum begins to facet with lower energy surfaces. These microstructural changes can be seen in Figures 1 and 2, but the goal of the work is to characterize the microstructure by its fractal dimension and then relate the fractal dimension to the electrical response. The sensors were fabricated from zirconia powder stabilized in the cubic phase with 8 mol% percent yttria. Each substrate was sintered for 14 hours at 1200°C. The resulting zirconia pellets, 13mm in diameter and 2mm in thickness, were roughly 97 to 98 percent of theoretical density. The Engelhard #6082 platinum paste was applied to the zirconia disks after they were mechanically polished ( diamond). The electrodes were then sintered at temperatures ranging from 600°C to 1000°C. Each sensor was tested to determine the impedance response from 1Hz to 5,000Hz. These frequencies correspond to the electrode at the test temperature of 600°C.

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Amorphous silica coating on α-alumina particles

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100137422 ◽

1995 ◽

Vol 53 ◽

pp. 210-211

Author(s):

J. W. Mellowes ◽

C. M. Chun ◽

I. A. Aksay

Keyword(s):

Amorphous Silica ◽

Heterogeneous Nucleation ◽

Homogeneous Nucleation ◽

Silica Coating ◽

Full Density ◽

Optimal Conditions ◽

Fine Grained ◽

Alumina Particles ◽

Complete Mullitization ◽

Powder Compacts

Mullite (3Al2O32SiO2) can be fabricated by transient viscous sintering using composite particles which consist of inner cores of a-alumina and outer coatings of amorphous silica. Powder compacts prepared with these particles are sintered to almost full density at relatively low temperatures (~1300°C) and converted to dense, fine-grained mullite at higher temperatures (>1500°C) by reaction between the alumina core and the silica coating. In order to achieve complete mullitization, optimal conditions for coating alumina particles with amorphous silica must be achieved. Formation of amorphous silica can occur in solution (homogeneous nucleation) or on the surface of alumina (heterogeneous nucleation) depending on the degree of supersaturation of the solvent in which the particles are immersed. Successful coating of silica on alumina occurs when heterogeneous nucleation is promoted and homogeneous nucleation is suppressed. Therefore, one key to successful coating is an understanding of the factors such as pH and concentration that control silica nucleation in aqueous solutions. In the current work, we use TEM to determine the optimal conditions of this processing.

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Interfacial structures of dissimilar materials joined by capacitor-discharge welding

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100170402 ◽

1994 ◽

Vol 52 ◽

pp. 534-535

Author(s):

C. P. Doğan ◽

R. D. Wilson ◽

J. A. Hawk

Keyword(s):

Rapid Solidification ◽

Fusion Zone ◽

Dissimilar Materials ◽

Solidification Process ◽

Weld Interface ◽

Capacitor Discharge ◽

Fine Grained ◽

Iron Aluminum ◽

Conductive Ceramics ◽

Capacitor Discharge Welding

Capacitor Discharge Welding is a rapid solidification technique for joining conductive materials that results in a narrow fusion zone and almost no heat affected zone. As a result, the microstructures and properties of the bulk materials are essentially continuous across the weld interface. During the joining process, one of the materials to be joined acts as the anode and the other acts as the cathode. The anode and cathode are brought together with a concomitant discharge of a capacitor bank, creating an arc which melts the materials at the joining surfaces and welds them together (Fig. 1). As the electrodes impact, the arc is extinguished, and the molten interface cools at rates that can exceed 106 K/s. This process results in reduced porosity in the fusion zone, a fine-grained weldment, and a reduced tendency for hot cracking.At the U.S. Bureau of Mines, we are currently examining the possibilities of using capacitor discharge welding to join dissimilar metals, metals to intermetallics, and metals to conductive ceramics. In this particular study, we will examine the microstructural characteristics of iron-aluminum welds in detail, focussing our attention primarily on interfaces produced during the rapid solidification process.

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TEM/AEM characterization of fine-grained clay minerals in very-low-grade rocks: Evaluation of contamination by empa involving celadonite family minerals

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100165938 ◽

1996 ◽

Vol 54 ◽

pp. 694-695

Author(s):

Gejing Li ◽

D. R. Peacor ◽

D. S. Coombs ◽

Y. Kawachi

Keyword(s):

Electron Microscopy ◽

Volcanic Rocks ◽

Analytical Electron Microscopy ◽

Metamorphic Rocks ◽

New Mineral ◽

Chemical Compositions ◽

Low Grade ◽

Fine Grained ◽

Depth Analysis ◽

Mineral Aggregates

Recent advances in transmission electron microscopy (TEM) and analytical electron microscopy (AEM) have led to many new insights into the structural and chemical characteristics of very finegrained, optically homogeneous mineral aggregates in sedimentary and very low-grade metamorphic rocks. Chemical compositions obtained by electron microprobe analysis (EMPA) on such materials have been shown by TEM/AEM to result from beam overlap on contaminant phases on a scale below resolution of EMPA, which in turn can lead to errors in interpretation and determination of formation conditions. Here we present an in-depth analysis of the relation between AEM and EMPA data, which leads also to the definition of new mineral phases, and demonstrate the resolution power of AEM relative to EMPA in investigations of very fine-grained mineral aggregates in sedimentary and very low-grade metamorphic rocks.Celadonite, having end-member composition KMgFe3+Si4O10(OH)2, and with minor substitution of Fe2+ for Mg and Al for Fe3+ on octahedral sites, is a fine-grained mica widespread in volcanic rocks and volcaniclastic sediments which have undergone low-temperature alteration in the oceanic crust and in burial metamorphic sequences.

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Correlation between dislocation, grain boundary and interface of duplex SS in stress corrosion cracking(SCC)

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100177775 ◽

1990 ◽

Vol 48 (4) ◽

pp. 928-929

Author(s):

Wang Zheng-fang ◽

Z.F. Wang

Keyword(s):

Stainless Steel ◽

Thermal Cycle ◽

Secondary Phase ◽

Corrosion Cracking ◽

Coarse Grained ◽

Test Environment ◽

Fine Grained ◽

Evaluation Test ◽

Welding Thermal Cycle ◽

Micro Analysis

The main purpose of this study highlights on the evaluation of chloride SCC resistance of the material,duplex stainless steel,OOCr18Ni5Mo3Si2 (18-5Mo) and its welded coarse grained zone(CGZ).18-5Mo is a dual phases (A+F) stainless steel with yield strength:512N/mm2 .The proportion of secondary Phase(A phase) accounts for 30-35% of the total with fine grained and homogeneously distributed A and F phases(Fig.1).After being welded by a specific welding thermal cycle to the material,i.e. Tmax=1350°C and t8/5=20s,microstructure may change from fine grained morphology to coarse grained morphology and from homogeneously distributed of A phase to a concentration of A phase(Fig.2).Meanwhile,the proportion of A phase reduced from 35% to 5-10°o.For this reason it is known as welded coarse grained zone(CGZ).In association with difference of microstructure between base metal and welded CGZ,so chloride SCC resistance also differ from each other.Test procedures:Constant load tensile test(CLTT) were performed for recording Esce-t curve by which corrosion cracking growth can be described, tf,fractured time,can also be recorded by the test which is taken as a electrochemical behavior and mechanical property for SCC resistance evaluation. Test environment:143°C boiling 42%MgCl2 solution is used.Besides, micro analysis were conducted with light microscopy(LM),SEM,TEM,and Auger energy spectrum(AES) so as to reveal the correlation between the data generated by the CLTT results and micro analysis.

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