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干豆数据集


原文:

Dry Bean Dataset

Data Set Information:

Seven different types of dry beans were used in this research, taking into account the features such as form, shape, type, and structure by the market situation. A computer vision system was developed to distinguish seven different registered varieties of dry beans with similar features in order to obtain uniform seed classification. For the classification model, images of 13,611 grains of 7 different registered dry beans were taken with a high-resolution camera. Bean images obtained by computer vision system were subjected to segmentation and feature extraction stages, and a total of 16 features; 12 dimensions and 4 shape forms, were obtained from the grains.

Attribute Information:

1.) Area (A): The area of a bean zone and the number of pixels within its boundaries.

2.) Perimeter (P): Bean circumference is defined as the length of its border.

3.) Major axis length (L): The distance between the ends of the longest line that can be drawn from a bean.

4.) Minor axis length (l): The longest line that can be drawn from the bean while standing perpendicular to the main axis.

5.) Aspect ratio (K): Defines the relationship between L and l.

6.) Eccentricity (Ec): Eccentricity of the ellipse having the same moments as the region.

7.) Convex area (C): Number of pixels in the smallest convex polygon that can contain the area of a bean seed.

8.) Equivalent diameter (Ed): The diameter of a circle having the same area as a bean seed area.

9.) Extent (Ex): The ratio of the pixels in the bounding box to the bean area.

10.)Solidity (S): Also known as convexity. The ratio of the pixels in the convex shell to those found in beans.

11.)Roundness (R): Calculated with the following formula: (4piA)/(P^2)

12.)Compactness (CO): Measures the roundness of an object: Ed/L

13.)ShapeFactor1 (SF1)

14.)ShapeFactor2 (SF2)

15.)ShapeFactor3 (SF3)

16.)ShapeFactor4 (SF4)

17.)Class (Seker, Barbunya, Bombay, Cali, Dermosan, Horoz and Sira)

译:

数据集信息:

本研究使用了七种不同类型的干豆,根据市场情况考虑了形状、形状、类型和结构等特征。为了获得统一的种子分类,开发了一个计算机视觉系统来区分七个具有相似特征的不同登记的干豆品种。对于分类模型,使用高分辨率相机拍摄了7种不同登记的干豆的13611粒图像。通过计算机视觉系统获得的大豆图像经过分割和特征提取阶段,共有16个特征;从晶粒中获得12个尺寸和4个形状。

属性信息:

1.)Area (A):bean区域的面积及其边界内的像素数。

2)Perimeter (P):豆周长定义为其边界的长度。

3.)Major axis length(L):可以从豆子上绘制的最长直线的两端之间的距离。

4.)Minor axis length (l):当垂直于主轴站立时,可以从豆子上画出的最长线。

5.)Aspect ratio(K):定义L和L之间的关系。

6.)Eccentricity(Ec):与区域具有相同力矩的椭圆的偏心率。

7.)Convex area(C):可以包含豆种子区域的最小凸多边形中的像素数。

8.)Equivalent diameter(Ed):具有与豆子面积相同面积的圆的直径。

9.)Extent(Ex):边界框中的像素与bean区域的比率。

10.)Solidity :也称为凸度。凸包中的像素与豆子中的像素之比。

11.)Roundness (R):用以下公式计算:(4piA)/(P^2)

12.)Compactness (CO):测量物体的圆度:Ed/L

13.)ShapeFactor1(SF1)

14.)ShapeFactor2(SF2)

15.)ShapeFactor3(SF3)

16.)ShapeFactor4(SF4)

17.)Class(塞克、巴本亚、孟买、卡利、德莫桑、霍罗斯和西拉)

示例数据:



Area

Perimeter

MajorAxisLength

MinorAxisLength

AspectRation

Eccentricity

ConvexArea

28395

610.291

208.1781167

173.888747

1.197191424

0.549812187

28715

28734

638.018

200.5247957

182.7344194

1.097356461

0.411785251

29172

29380

624.11

212.8261299

175.9311426

1.209712656

0.562727317

29690

30008

645.884

210.557999

182.5165157

1.153638059

0.498615976

30724

30140

620.134

201.8478822

190.2792788

1.06079802

0.333679658

30417

30279

634.927

212.5605564

181.5101816

1.171066849

0.52040066

30600

30477

670.033

211.0501553

184.0390501

1.146768336

0.489477894

30970

30519

629.727

212.9967551

182.7372038

1.165590535

0.513759558

30847

30685

635.681

213.5341452

183.1571463

1.165852108

0.51408086

31044

30834

631.934

217.2278128

180.8974686

1.2008339

0.553642225

31120

30917

640.765

213.5600894

184.4398709

1.157884618

0.504102365

31280

31091

638.558

210.4862549

188.3268476

1.117664622

0.446621924

31458

31107

640.594

214.6485485

184.9692526

1.160455295

0.507365875

31423

31158

642.626

216.4848362

183.6443122

1.178826797

0.529514251

31492

31158

641.105

212.0669751

187.1929601

1.132879009

0.469924157

31474

31178

636.888

212.9759252

186.5620882

1.141582018

0.482352224

31520

31202

644.454

215.6406947

184.4716842

1.168963657

0.517871223

31573

31203

639.782

215.067737

184.8748759

1.163315112

0.510946829

31558

31272

638.666

212.4503189

187.535939

1.132851229

0.469883494

31593

31335

635.011

216.7900923

184.1634403

1.177161395

0.52758671

31599

31374

636.401

219.865394

182.0088637

1.207992784

0.56099452

31604

31530

638.857

213.7856543

188.0664823

1.136755746

0.475535642

31791

31573

674.103

217.3070261

185.4482507

1.171793345

0.52126839

32197

31637

656.711

229.7192546

175.510446

1.308863717

0.645190802

32045

31675

657.431

236.7526321

171.2105592

1.3828156

0.690678219

32009

31682

646.721

210.0456816

192.2484159

1.092574316

0.402841974

32026

31703

656.305

215.7089067

187.2724497

1.151845384

0.496263281

32093

31748

641.826

219.7765183

184.1151053

1.193690859

0.546072628

32020

31768

650.954

220.9594949

183.2920681

1.205504947

0.558465259

32173


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