scholarly journals Effects of Color Space Transformations on Classification Performance of Sperm Morphology

2021 ◽  
Author(s):  
Mecit YÜZKAT ◽  
Hamza O.İLHAN ◽  
Nizamettin AYDIN
Keyword(s):  
Sperm Morphology ◽  
Color Space ◽  
Classification Performance

Classification of Remotely Sensed Images Using Independent Component Analysis and Spatial Consistency

2004 ◽  
Vol 8 (2) ◽  
pp. 216-222 ◽  
Author(s):  
Xiang-Yan Zeng ◽  
◽  
Yen-Wei Chen ◽  
Zensho Nakao ◽  
Keyword(s):  
Independent Component Analysis ◽  
Color Space ◽  
Component Analysis ◽  
Remotely Sensed ◽  
Classification Performance ◽  
Independent Component ◽  
Color Representation ◽  
Spatial Consistency ◽  
Remotely Sensed Images

We apply independent component analysis (ICA) to learn efficient color representation of remotely sensed images. Among the three basis functions obtained from RGB color space, two are in an opposing-color model by which the responses of R, G and B cones are combined in opposing fashions. This is coincident with the idea of contrasting reflected in many color systems. The interesting point is that there is no summation component that corresponds to illumination in other transforms. Spectral independent components are then used to cluster pixels. After pixel-based classification, we segment an image on the basis of regions by spatial consistency. Experimental results show that this method considerably improves the classification performance of multispectral remotely sensed images.

scholarly journals Robust Image Hashing Based on Cool and Warm Hue and Space Angle

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yan Zhao ◽  
Shuai Liu
Keyword(s):  
Dimensional Space ◽  
Color Space ◽  
Three Dimensional ◽  
Computation Time ◽  
Classification Performance ◽  
Image Hashing ◽  
Color Component ◽  
Space Angle ◽  
Opposite Color ◽  
Three Dimensional Space

Image hashing has attracted more and more attention in the field of information security. In this paper, a novel hashing algorithm using cool and warm hue information and three-dimensional space angle is proposed. Firstly, the original image is preprocessed to get the opposite color component and the hue component H in HSV color space. Then, the distribution of cool and warm hue pixels is extracted from hue component H. Blocks the hue component H, according to the proportion of warm hue and cool hue pixels in each small block, combined with the quaternion and opposite color component, constructed the cool and warm hue opposite color quaternion (CWOCQ) feature. Then, three-dimensional space, opposite color, and cool and warm hue are combined to obtain the three-dimensional space angle (TDSA) feature. The CWOCQ feature and the TDSA feature are connected and disturbed to obtain the final hash sequence. Experimental results show that the proposed algorithm has good security and has better image classification performance and shorter computation time compared with some advanced algorithms.

scholarly journals Leaf recognition using BP-RBF hybrid neural network

2021 ◽  
Author(s):  
Xin Yang ◽  
Haiming Ni ◽  
Jingkui Li ◽  
Jialuo Lv ◽  
Hongbo Mu ◽  
...  
Keyword(s):  
Neural Network ◽  
Species Recognition ◽  
Color Space ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Classification Performance ◽  
Hybrid Neural Network ◽  
Plant Recognition ◽  
Image Pretreatment ◽  
Leaf Image

AbstractPlant recognition has great potential in forestry research and management. A new method combined back propagation neural network and radial basis function neural network to identify tree species using a few features and samples. The process was carried out in three steps: image pretreatment, feature extraction, and leaf recognition. In the image pretreatment processing, an image segmentation method based on hue, saturation and value color space and connected component labeling was presented, which can obtain the complete leaf image without veins and background. The BP-RBF hybrid neural network was used to test the influence of shape and texture on species recognition. The recognition accuracy of different classifiers was used to compare classification performance. The accuracy of the BP-RBF hybrid neural network using nine dimensional features was 96.2%, highest among all the classifiers.

scholarly journals Klasifikasi Untuk Menentukan Tingkat Kematangan Buah Pisang Sunpride

2016 ◽  
Vol 2 ◽  
pp. 109
Author(s):  
Gracelia Adelaida Bere ◽  
Elizabeth Nurmiyati Tamtjita ◽  
Anggraini Kusumaningrum
Keyword(s):  
Euclidean Distance ◽  
Color Space ◽  
Extraction Process ◽  
Classification Performance ◽  
Test Image ◽  
K Nearest Neighbors ◽  
Color Features ◽  
Training Samples ◽  
Rgb Color Space ◽  
Test Objects

         YIQ (Iuma, In-phase, Quadrature) is a color space used to transmit analog TV signal. This research is conducting a possibility test on using YIQ as color features for fruit ripeness classification, which tested on Sunpride bananas. Classification is done using k-NN algorithm against YIQ values of several ripeness stage. The classification process itself consists of two steps: training and testing. In the training step, values from RGB color space of the images as training samples are converted into YIQ and extracted as features to form the classes, while in testing step, the test image went through the same conversion and feature extraction process, then classified using k-NN against the classes’ features, using k=3 and k=1. There are 120 Sunpride banana images used as test objects, and the results obtained shown that the classification performance using k=3 for Sangat Matang class is 100%, Busuk class is 66,67%, Mengkal class is 60% and Matang class is 60%. Results using k=1 for Sangat Matang class is 100%, Busuk class is 66,67%, Mengkal class is 66,7% and Matang class is 56,67%.Keywords  : Classification, YIQ Color Space, Banana Sunpride, Euclidean Distance, k-Nearest Neighbors

scholarly journals Effect of boiling on classification performance of potatoes determined by computer vision

2021 ◽  
Author(s):  
Ewa Ropelewska
Keyword(s):  
Outer Surface ◽  
Color Space ◽  
Classification Performance ◽  
Color Spaces ◽  
Color Channel ◽  
Discriminative Models ◽  
Model Combining ◽  
Processed Potatoes ◽  
Cultivar Discrimination ◽  
Color Channels

AbstractThe aim of this study was to evaluate the effect of potato boiling on the correctness of cultivar discrimination. The research was performed in an objective, inexpensive and fast manner using the image analysis technique. The textures of the outer surface of slice images of raw and boiled potatoes were calculated. The discriminative models based on a set of textures selected from all color channels (R, G, B, L, a, b, X, Y, Z, U, V, S), textures selected for color spaces and textures selected for individual color channels were developed. In the case of discriminant analysis of raw potatoes of cultivars ‘Colomba’, ‘Irga’ and ‘Riviera’, the accuracies reached 94.33% for the model built based on a set of textures selected from all color channels, 94% for Lab and XYZ color spaces, 92% for color channel b and 92.33% for a set of combined textures selected from channels B, b, and Z. The processed potatoes were characterized by the accuracy of up to 98.67% for the model including the textures selected from all color channels, 98% for RGB color space, 95.33% for color channel b, 96.67% for the model combining the textures selected from channels B, b, and Z. In the case of raw and processed potatoes, the cultivar ‘Irga’ differed in 100% from other potato cultivars. The results revealed an increase in cultivar discrimination accuracy after the processing of potatoes. The textural features of the outer surface of slice images have proved useful for cultivar discrimination of raw and processed potatoes.

119: Effect of Epididymitis on Semen Biochemical and Sperm Morphology Parameters

2005 ◽  
Vol 173 (4S) ◽  
pp. 32-32
Author(s):  
Petra Huwe ◽  
Roelof Menkveld ◽  
Martin Ludwig ◽  
Wolfgang Weidner
Keyword(s):  
Sperm Morphology ◽  
Morphology Parameters

The Sound of Enemies and Friends in the Neighborhood

2011 ◽  
Vol 58 (6) ◽  
pp. 454-463 ◽  
Author(s):  
Diane Pecher ◽  
Inge Boot ◽  
Saskia van Dantzig ◽  
Carol J. Madden ◽  
David E. Huber ◽  
...  
Keyword(s):  
Word Recognition ◽  
Visual Word Recognition ◽  
Neighborhood Effects ◽  
Classification Performance ◽  
Visual Word ◽  
Orthographic Neighborhood ◽  
Semantic Classification ◽  
Semantic Class ◽  
Orthographic Neighbors ◽  
Target Words

Previous studies (e.g., Pecher, Zeelenberg, & Wagenmakers, 2005) found that semantic classification performance is better for target words with orthographic neighbors that are mostly from the same semantic class (e.g., living) compared to target words with orthographic neighbors that are mostly from the opposite semantic class (e.g., nonliving). In the present study we investigated the contribution of phonology to orthographic neighborhood effects by comparing effects of phonologically congruent orthographic neighbors (book-hook) to phonologically incongruent orthographic neighbors (sand-wand). The prior presentation of a semantically congruent word produced larger effects on subsequent animacy decisions when the previously presented word was a phonologically congruent neighbor than when it was a phonologically incongruent neighbor. In a second experiment, performance differences between target words with versus without semantically congruent orthographic neighbors were larger if the orthographic neighbors were also phonologically congruent. These results support models of visual word recognition that assume an important role for phonology in cascaded access to meaning.

IDEAL COLOR SPACE COMPONENT FOR RECONSTRUCTION OF CONTAMINATION PLUMES

2013 ◽  
Vol 20 (3) ◽  
pp. 125-159 ◽  
Author(s):  
Sina Kashuk ◽  
Sophia R. Mercurio ◽  
Magued Iskander
Keyword(s):  
Color Space

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

A Correspondence-Free Color Chart Design for Color Calibration

2020 ◽  
Vol 2020 (1) ◽  
pp. 100-104
Author(s):  
Hakki Can Karaimer ◽  
Rang Nguyen
Keyword(s):  
Color Space ◽  
Fixed Number ◽  
New Approach ◽  
Color Chart ◽  
Color Calibration ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Chart Design ◽  
Calibration Approach

Colorimetric calibration computes the necessary color space transformation to map a camera's device-specific color space to a device-independent perceptual color space. Color calibration is most commonly performed by imaging a color rendition chart with a fixed number of color patches with known colorimetric values (e. g., CIE XYZ values). The color space transformation is estimated based on the correspondences between the camera's image and the chart's colors. We present a new approach to colorimetric calibration that does not require explicit color correspondences. Our approach computes a color space transformation by aligning the color distributions of the captured image to the known distribution of a calibration chart containing thousands of colors. We show that a histogram-based colorimetric calibration approach provides results that are onpar with the traditional patch-based method without the need to establish correspondences.

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