Effect of Segmentation Parameters on Classification Accuracy of High-Density EMG recordings

2019 ◽  
Author(s):  
J. Lara ◽  
N. Paskaranandavadivel ◽  
L. K. Cheng
Keyword(s):  
Classification Accuracy ◽  
High Density ◽  
Segmentation Parameters

scholarly journals Scale Matters: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Large and Heterogeneous Satellite Images

2018 ◽  
Vol 10 (9) ◽  
pp. 1440 ◽  
Author(s):  
Stefanos Georganos ◽  
Tais Grippa ◽  
Moritz Lennert ◽  
Sabine Vanhuysse ◽  
Brian Johnson ◽  
...  
Keyword(s):  
Parameter Optimization ◽  
Classification Accuracy ◽  
Urban Areas ◽  
Landscape Patterns ◽  
Unsupervised Segmentation ◽  
African City ◽  
Local Approach ◽  
Sub Saharan ◽  
Land Cover Maps ◽  
Segmentation Parameters

To classify Very-High-Resolution (VHR) imagery, Geographic Object Based Image Analysis (GEOBIA) is the most popular method used to produce high quality Land-Use/Land-Cover maps. A crucial step in GEOBIA is the appropriate parametrization of the segmentation algorithm prior to the classification. However, little effort has been made to automatically optimize GEOBIA algorithms in an unsupervised and spatially meaningful manner. So far, most Unsupervised Segmentation Parameter Optimization (USPO) techniques, assume spatial stationarity for the whole study area extent. This can be questionable, particularly for applications in geographically large and heterogeneous urban areas. In this study, we employed a novel framework named Spatially Partitioned Unsupervised Segmentation Parameter Optimization (SPUSPO), which optimizes segmentation parameters locally rather than globally, for the Sub-Saharan African city of Ouagadougou, Burkina Faso, using WorldView-3 imagery (607 km2). The results showed that there exists significant spatial variation in the optimal segmentation parameters suggested by USPO across the whole scene, which follows landscape patterns—mainly of the various built-up and vegetation types. The most appropriate automatic spatial partitioning method from the investigated techniques, was an edge-detection cutline algorithm, which achieved higher classification accuracy than a global optimization, better predicted built-up regions, and did not suffer from edge effects. The overall classification accuracy using SPUSPO was 90.5%, whilst the accuracy from undertaking a traditional USPO approach was 89.5%. The differences between them were statistically significant (p < 0.05) based on a McNemar’s test of similarity. Our methods were validated further by employing a segmentation goodness metric, Area Fit Index (AFI)on building objects across Ouagadougou, which suggested that a global USPO was more over-segmented than our local approach. The mean AFI values for SPUSPO and USPO were 0.28 and 0.36, respectively. Finally, the processing was carried out using the open-source software GRASS GIS, due to its efficiency in raster-based applications.

scholarly journals Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery

2018 ◽  
Vol 7 (11) ◽  
pp. 424 ◽  
Author(s):  
Ozgun Akcay ◽  
Emin Avsar ◽  
Melis Inalpulat ◽  
Levent Genc ◽  
Ahmet Cam
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Classification Accuracy ◽  
Land Cover Classification ◽  
Aerial Images ◽  
High Resolution Data ◽  
Area Of Interest ◽  
Object Based ◽  
Segmentation Parameters

Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained.

scholarly journals SPiQE: an automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis

2019 ◽  
Author(s):  
J Bashford ◽  
A Wickham ◽  
R Iniesta ◽  
E Drakakis ◽  
M Boutelle ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Classification Accuracy ◽  
Surface Emg ◽  
Analytical Tool ◽  
High Density ◽  
List Type ◽  
Motor Neuropathy ◽  
Density Surface ◽  
Als Patients ◽  
Lateral Sclerosis

AbstractOBJECTIVESFasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). Compared to concentric needle EMG, high-density surface EMG (HDSEMG) is non-invasive and records fasciculation potentials (FPs) from greater muscle volumes over longer durations. To detect and characterise FPs from vast data sets generated by serial HDSEMG, we developed an automated analytical tool.METHODSSix ALS patients and two control patients (one with benign fasciculation syndrome and one with multifocal motor neuropathy) underwent 30-minute HDSEMG from biceps and gastrocnemius monthly. In MATLAB we developed a novel, innovative method to identify FPs amidst fluctuating noise levels. One hundred repeats of 5-fold cross validation estimated the model’s predictive ability.RESULTSBy applying this method, we identified 5,318 FPs from 80 minutes of recordings with a sensitivity of 83.6% (+/-0.2 SEM), specificity of 91.6% (+/-0.1 SEM) and classification accuracy of 87.9% (+/-0.1 SEM). An amplitude exclusion threshold (100μV) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p=0.394).CONCLUSIONWe have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE).SIGNIFICANCELongitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.HighlightsSPiQE combines serial high-density surface EMG with an innovative signal-processing methodologySPiQE identifies fasciculations in ALS patients with high sensitivity and specificityThe optimal noise-responsive model achieves an average classification accuracy of 88%

scholarly journals IMPACT OF SEGMENTATION PARAMETERS ON THE CLASSIFICATION OF VHR IMAGES ACQUIRED BY RPAS

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 43-48
Author(s):  
M. G. Lacerda ◽  
E. H. Shiguemori ◽  
A. J. Damião ◽  
C. S. Anjos ◽  
M. Habermann
Keyword(s):  
Classification Accuracy ◽  
Processing Time ◽  
Object Oriented ◽  
Sensing Applications ◽  
Final Output ◽  
Remote Sensing Applications ◽  
Imaging Sensors ◽  
Quality Imaging ◽  
Segmentation Parameters

Abstract. RPAs (Remotely Piloted Aircrafts) have been used in many Remote Sensing applications, featuring high-quality imaging sensors. In some situations, the images are interpreted in an automated fashion using object-oriented classification. In this case, the first step is segmentation. However, the setting of segmentation parameters such as scale, shape, and compactness may yield too many different segmentations, thus it is necessary to understand the influence of those parameters on the final output. This paper compares 24 segmentation parameter sets by taking into account classification scores. The results indicate that the segmentation parameters exert influence on both classification accuracy and processing time.

Planar defects in ordered (Ga,In)P

1988 ◽  
Vol 46 ◽  
pp. 902-903
Author(s):  
S. McKernan ◽  
C. B. Carter ◽  
D. Bour ◽  
J. R. Shealy
Keyword(s):  
Electron Diffraction ◽  
Vapor Phase ◽  
Growth Direction ◽  
High Density ◽  
Ordered Structure ◽  
Planar Defects ◽  
Diffraction Patterns ◽  
Ternary Semiconductors ◽  
Anion Species ◽  
Metallic Vapor

The growth of ternary III-V semiconductors by organo-metallic vapor phase epitaxy (OMVPE) is widely practiced. It has been generally assumed that the resulting structure is the same as that of the corresponding binary semiconductors, but with the two different cation or anion species randomly distributed on their appropriate sublattice sites. Recently several different ternary semiconductors including AlxGa1-xAs, Gaxln-1-xAs and Gaxln1-xP1-6 have been observed in ordered states. A common feature of these ordered compounds is that they contain a relatively high density of defects. This is evident in electron diffraction patterns from these materials where streaks, which are typically parallel to the growth direction, are associated with the extra reflections arising from the ordering. However, where the (Ga,ln)P epilayer is reasonably well ordered the streaking is extremely faint, and the intensity of the ordered spot at 1/2(111) is much greater than that at 1/2(111). In these cases it is possible to image relatively clearly many of the defects found in the ordered structure.

Quantitative defect studies in semiconductor TEM samples prepared by low angle ion milling

1995 ◽  
Vol 53 ◽  
pp. 512-513
Author(s):  
L. Mulestagno ◽  
J.C. Holzer ◽  
P. Fraundorf
Keyword(s):  
Sample Preparation ◽  
Milling Time ◽  
High Density ◽  
Low Density ◽  
Ion Milling ◽  
High Quality ◽  
Variable Angle ◽  
Major Disadvantage ◽  
Induced Defects

Due to the wealth of information, both analytical and structural that can be obtained from it TEM always has been a favorite tool for the analysis of process-induced defects in semiconductor wafers. The only major disadvantage has always been, that the volume under study in the TEM is relatively small, making it difficult to locate low density defects, and sample preparation is a somewhat lengthy procedure. This problem has been somewhat alleviated by the availability of efficient low angle milling.Using a PIPS® variable angle ion -mill, manufactured by Gatan, we have been consistently obtaining planar specimens with a high quality thin area in excess of 5 × 104 μm2 in about half an hour (milling time), which has made it possible to locate defects at lower densities, or, for defects of relatively high density, obtain information which is statistically more significant (table 1).

Using the scanning electron microscope for failure analysis of high density magnetic media

1987 ◽  
Vol 45 ◽  
pp. 392-393
Author(s):  
Evelyn R. Ackerman ◽  
Gary D. Burnett
Keyword(s):  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Failure Analysis ◽  
High Density ◽  
Magnetic Media ◽  
Specific Data ◽  
Retrieval Systems ◽  
Operating Environments ◽  
The Media ◽  
Scanning Electron

Advancements in state of the art high density Head/Disk retrieval systems has increased the demand for sophisticated failure analysis methods. From 1968 to 1974 the emphasis was on the number of tracks per inch. (TPI) ranging from 100 to 400 as summarized in Table 1. This emphasis shifted with the increase in densities to include the number of bits per inch (BPI). A bit is formed by magnetizing the Fe203 particles of the media in one direction and allowing magnetic heads to recognize specific data patterns. From 1977 to 1986 the tracks per inch increased from 470 to 1400 corresponding to an increase from 6300 to 10,800 bits per inch respectively. Due to the reduction in the bit and track sizes, build and operating environments of systems have become critical factors in media reliability.Using the Ferrofluid pattern developing technique, the scanning electron microscope can be a valuable diagnostic tool in the examination of failure sites on disks.

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