Digital Image Pattern Recognition Research Articles

Automated color detection in orchids using color labels and deep learning.

The color of particular parts of a flower is often employed as one of the features to differentiate between flower types. Thus, color is also used in flower-image classification. Color labels, such as ‘green’, ‘red’, and ‘yellow’, are used by taxonomists and lay people alike to describe the color of plants. Flower image datasets usually only consist of images and do not contain flower descriptions. In this research, we have built a flower-image dataset, especially regarding orchid species, which consists of human-friendly textual descriptions of features of specific flowers, on the one hand, and digital photographs indicating how a flower looks like, on the other hand. Using this dataset, a new automated color detection model was developed. It is the first research of its kind using color labels and deep learning for color detection in flower recognition. As deep learning often excels in pattern recognition in digital images, we applied transfer learning with various amounts of unfreezing of layers with five different neural network architectures (VGG16, Inception, Resnet50, Xception, Nasnet) to determine which architecture and which scheme of transfer learning performs best. In addition, various color scheme scenarios were tested, including the use of primary and secondary color together, and, in addition, the effectiveness of dealing with multi-class classification using multi-class, combined binary, and, finally, ensemble classifiers were studied. The best overall performance was achieved by the ensemble classifier. The results show that the proposed method can detect the color of flower and labellum very well without having to perform image segmentation. The result of this study can act as a foundation for the development of an image-based plant recognition system that is able to offer an explanation of a provided classification.

Dermoscopy Image Segmentation in Melanoma Skin Cancer using Otsu Thresholding Method

Melanoma is a skin cancer that originates from melanocytes, melanin-producing cells in the skin. It requires quite a long time to detect melanoma through a biopsy. By utilizing technology, the time required to obtain biopsy results in detection of melanoma can be shortened using image pattern recognition. Segmentation is a stage that affects the results in image analysis for pattern recognition in digital images because of the accuracy of a confident segment in an image analysis. Otsu thresholding is a segmentation method aims to find the threshold point that divides the grayscale image of histogram into two different areas automatically. In this study, segmentation was carried out on 15 dermoscopy of melanoma images that were subjected to grayscaling, histogram, segmentation with Otsu Thresholding, binarization, image negation, and testing. The test carried out using the Receiver Operating Character (ROC) method exhibited a mean sensitivity level of 70.3%, a mean specificity level of 95.53%, and a mean accuracy of 94.82%.

Fractional Fourier-Radial Transform for Digital Image Recognition

This paper presents a new system for pattern recognition in digital images, called Fractional Fourier-Radial Transform, invariant to translation, scale and rotation (TSR invariant) taking advantage of the well-known properties of some integral transform as Fourier Transform, Mellin Transform and the Radial Hilbert Transform. The main contribution of this work is the use of the Fractional Fourier Transform to avoid, or reduce the overlap between results due to the optimal order selection for each reference image, assuming α = β for computing optimization, which helps to get a higher difference between the reference images spectrum. This system was tested using different species of phytoplankton obtaining a level of confidence of at least 92.68% invariant to position, size, and rotation, supporting scale variations of ±20%. The mean of the highest confidence values for the scale variation correlations is 98.47%, for rotation variation correlations is 100%, and for the rotation and scale variation correlations is 98.15%. The testing dataset images are selected due to their morphology complexity; they have a real pattern to be recognized instead of using a test-book data set.

Pattern Recognition in Digital Images using Fractals

Pattern recognition in digital images is a conjoint problem with application in remote sensing, electron microscopy, medical imaging and astrophysics, still no general solution which can be rivalled with the human cognitive system in which a pattern can be conceded subject to random positioning and scale. This research has stemmed in the design and implementation of a new algorithm for general pattern recognition based on the use of fractal image compression. This approach has for the first time allowed the pattern recognition problem to be solved in a way that is invariant of rotation and scale. It allows both ANNs and correlation to be used subject to appropriate pre-and post-processing techniques for digital image processing.

Semantic segmentation of synchrotron tomography of multiphase Al-Si alloys using a convolutional neural network with a pixel-wise weighted loss function

Human-based segmentation of tomographic images can be a tedious time-consuming task. Deep learning algorithms and, particularly, convolutional neural networks have become state of the art techniques for pattern recognition in digital images that can replace human-based image segmentation. However, their use in materials science is beginning to be explored and their application needs to be adapted to the specific needs of this field. In the present work, a convolutional neural network is trained to segment the microstructural components of an Al-Si cast alloy imaged using synchrotron X-ray tomography. A pixel-wise weighted error function is implemented to account for microstructural features which are hard to identify in the tomographs and that play a relevant role for the correct description of the 3D architecture of the alloy investigated. The results show that the total operation time for the segmentation using the trained convolutional neural network was reduced to <1% of the time needed with human-based segmentation.

Detection of Carolina Geranium (Geranium carolinianum) Growing in Competition with Strawberry Using Convolutional Neural Networks

AbstractWeed interference during crop establishment is a serious concern for Florida strawberry [Fragaria×ananassa(Weston) Duchesne ex Rozier (pro sp.) [chiloensis×virginiana]] producers. In situ remote detection for precision herbicide application reduces both the risk of crop injury and herbicide inputs. Carolina geranium (Geranium carolinianumL.) is a widespread broadleaf weed within Florida strawberry production with sensitivity to clopyralid, the only available POST broadleaf herbicide.Geranium carolinianumleaf structure is distinct from that of the strawberry plant, which makes it an ideal candidate for pattern recognition in digital images via convolutional neural networks (CNNs). The study objective was to assess the precision of three CNNs in detectingG. carolinianum. Images ofG. carolinianumgrowing in competition with strawberry were gathered at four sites in Hillsborough County, FL. Three CNNs were compared, including object detection–based DetectNet, image classification–based VGGNet, and GoogLeNet. Two DetectNet networks were trained to detect either leaves or canopies ofG. carolinianum. Image classification using GoogLeNet and VGGNet was largely unsuccessful during validation with whole images (Fscore&lt;0.02). CNN training using cropped images increasedG. carolinianumdetection during validation for VGGNet (Fscore=0.77) and GoogLeNet (Fscore=0.62). TheG. carolinianumleaf–trained DetectNet achieved the highestFscore(0.94) for plant detection during validation. Leaf-based detection led to more consistent detection ofG. carolinianumwithin the strawberry canopy and reduced recall-related errors encountered in canopy-based training. The smaller target of leaf-based DetectNet did increase false positives, but such errors can be overcome with additional training images for network desensitization training. DetectNet was the most viable CNN tested for image-based remote sensing ofG. carolinianumin competition with strawberry. Future research will identify the optimal approach for in situ detection and integrate the detection technology with a precision sprayer.

Распознавание образов на цифровых изображениях на основе теории инвариантов

Распознавание образов на цифровых изображениях на основе теории инвариантов