Texture Feature Extraction of RGB, HSV, YIQ and Dithered Images using GLCM, Wavelet Decomposition Techniques

Lumb, Manisha; Sethi, Poonam

doi:10.5120/11623-7087

Cited by 10 publications

(5 citation statements)

References 6 publications

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“…Hue takes a wide range of values, from 0 to 360 degrees. It corresponds to the whole colour variation spectrum, beginning with red, and ending with red, a whole circle achieved [35]. The suggested and tested image masking and enhancement system based on fuzzy-logic architecture is given below, as in Fig.…”

Section: Theory and Methodsmentioning

confidence: 99%

Image Masking and Enhancement System for Melanoma Early Stage Detection

Yalcinkaya¹,

Erbas²

2022

Intelligent Automation &Amp; Soft Computing

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Early stage melanoma detection (ESMD) is crucial as late detection kills. Computer aided diagnosis systems (CADS) integrated with high level algorithms are major tools capable of ESMD with high degree of accuracy, specificity, and sensitivity. CADS use the image and the information within the pixels of the image. Pixels' characteristics and orientations determine the colour and shapes of the images as the pixels and associated environment are closely interrelated with the lesion. CADS integrated with Convolutional Neural Networks (CNN) specifically play a major role for ESMD with high degree of accuracy. The proposed system has two steps to produce high degree of accuracy; the first is the Fuzzy-Logic (FL) based maskProd and pixel Enhancement vectors and the second is integration with deep CNN algorithms. The vectors, maskProd and pixel Enhancement, are based on lesion masking and image enhancement via fuzzy logic as hSvMask production has three inputs; sChannel, entropyFilter and distan-ceVector. The originality of this paper is based on the regional enhancement of each pixel of the image by fuzzy-logic. To change the brightness of an image by the conventional methods (CM), it is required to multiply the value of all and each pixels of the image by the same constant or weight value in order to generate new values of the pixels. But in the proposed method, FL determines a special and specific constant or weight value for every single pixel of the image; called a variable weight value. Secondly via conventional or classical methods of image processing, mask production is realized based on the threshold value predetermined, and this threshold value decides whether the image lesion is melanoma or not. CM use constant weight values but in the method suggested, FL determines a specific variable weight value for every single pixel of the image. Secondly CMs realize the mask production using predetermined threshold values, but proposed method suggests a new way capable of detecting pixels in the lesion-free regions, to do that, a system using FL was developed for each and every single pixel via the data obtained from three sources: the s-channel of the HSV (Hue, Saturation and Value) colour space, distance vector, and image entropy vector. The proposed method and its integration with algorithms; such as Alex-

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Section: Theory and Methodsmentioning

confidence: 99%

Image Masking and Enhancement System for Melanoma Early Stage Detection

Yalcinkaya¹,

Erbas²

2022

Intelligent Automation &Amp; Soft Computing

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“…To date, most proposed vision-based recognition methods, as mentioned in [ 39 ], have used additive Red Green Blue (RGB) colour model-based images for object detection. Nonetheless, as presented by [ 40 ], contrast, entropy, correlation, energy, the mean, and the standard deviation can be calculated from examined images. As stated in [ 41 ], the use of the RGB colour model images has its advantages compared to grayscale ones by providing three times more data, but it still has significant drawbacks.…”

Section: Preliminariesmentioning

confidence: 99%

Vision and RTLS Safety Implementation in an Experimental Human—Robot Collaboration Scenario

Slovák

Melicher

Šimovec

et al. 2021

Sensors

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Human–robot collaboration is becoming ever more widespread in industry because of its adaptability. Conventional safety elements are used when converting a workplace into a collaborative one, although new technologies are becoming more widespread. This work proposes a safe robotic workplace that can adapt its operation and speed depending on the surrounding stimuli. The benefit lies in its use of promising technologies that combine safety and collaboration. Using a depth camera operating on the passive stereo principle, safety zones are created around the robotic workplace, while objects moving around the workplace are identified, including their distance from the robotic system. Passive stereo employs two colour streams that enable distance computation based on pixel shift. The colour stream is also used in the human identification process. Human identification is achieved using the Histogram of Oriented Gradients, pre-learned precisely for this purpose. The workplace also features autonomous trolleys for material supply. Unequivocal trolley identification is achieved using a real-time location system through tags placed on each trolley. The robotic workplace’s speed and the halting of its work depend on the positions of objects within safety zones. The entry of a trolley with an exception to a safety zone does not affect the workplace speed. This work simulates individual scenarios that may occur at a robotic workplace with an emphasis on compliance with safety measures. The novelty lies in the integration of a real-time location system into a vision-based safety system, which are not new technologies by themselves, but their interconnection to achieve exception handling in order to reduce downtimes in the collaborative robotic system is innovative.

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“…It is generally obtained from the application of a local operator, statistical analysis, or measurement in a transformed domain. It can be extracted and estimated by many ways like Co-occurrence Matrix, Gabor Filters … etc [28] [29].…”

Section: A Feature Extractionmentioning

confidence: 99%

“… Mathematical formulation of the main descriptors derived from GLCMs Although GLCMs provide a rich description of spatial dependence, it is impractical to manipulate them in their raw form. A set of 14 statistical descriptors or attributes are well known in the literature to summarize the textual information contained in the GLCMs, but the five descriptors appearing most often are entropy, energy, contrast, homogeneity, and correlation [38] [39].…”

Section: Techniques Of Texture Features  Gray Level Co-occurrencementioning

confidence: 99%

Withdrawn

2020

IJEAT

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Today, skin cancer is a serious health problem and the quest for a correct clinical diagnosis has been an ongoing concern for dermatologists and for many researchers an important field study. Field Programmable Gate Array (FPGA) technology has become a key part of the current movement towards innovation. It provides a viable target for applying image processing algorithms. It presents a viable target for the implementation of algorithms suited to image processing even under real-time requirements, because of their flexibility and great potential. FPGAs have shown very high performance in spite of their low operational frequency and high parallelism in applications. In this paper, an embedded Content-Based Image Retrieval (CBIR) implementation based on FPGA is proposed to manage the image data efficiently and fastly. We describe the implementation of CBIR in image processing applications using FPGA. The main aim behind this implementation is to reach minimum timing by the maximum utilization of resources. We are conducting a study on the advantages of this embedded approach and discussed its effectiveness for a set of benchmark images publically available dataset PH2. The FPGA-based implementation shows the feasibility of the proposed methodology. The functional implementation of all processes using the Verilog code of FPGA has been compiled on the ALTERA QUARTUS-II software tool.

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Texture Feature Extraction of RGB, HSV, YIQ and Dithered Images using GLCM, Wavelet Decomposition Techniques

Cited by 10 publications

References 6 publications

Image Masking and Enhancement System for Melanoma Early Stage Detection

Image Masking and Enhancement System for Melanoma Early Stage Detection

Vision and RTLS Safety Implementation in an Experimental Human—Robot Collaboration Scenario

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