Edge Detection: A Collection of Pixel based Approach for Colored Images

The existing traditional edge detection algorithms process a single pixel on an image at a time, thereby calculating a value which shows the edge magnitude of the pixel and the edge orientation. Most of these existing algorithms convert the coloured images into gray scale before detection of edges. However, this process leads to inaccurate precision of recognized edges, thus producing false and broken edges in the image. This paper presents a profile modelling scheme for collection of pixels based on the step and ramp edges, with a view to reducing the false and broken edges present in the image. The collection of pixel scheme generated is used with the Vector Order Statistics to reduce the imprecision of recognized edges when converting from coloured to gray scale images. The Pratt Figure of Merit (PFOM) is used as a quantitative comparison between the existing traditional edge detection algorithm and the developed algorithm as a means of validation. The PFOM value obtained for the developed algorithm is 0.8480, which showed an improvement over the existing traditional edge detection algorithms.Comment: 5 Page

IMAGE PROCESSING BASED SMART SERICULTURE SYSTEM USING IOT

Rearing of silkworm is highly dependent on environmental variations. To have a healthy cocoon production, it is necessary to have a proper temperature and humidity controlled house for silkworm rearing. Temperature, humidity and fresh air should be managed to get a wonderful silk product. An ideal temperature of 23°C to 28°C and humidity in between 65% to 85% is to be maintained. IoT based silkworm rearing house consists of sensors and actuators, which are interfaced with a low power controllers. The Sericulture unit can be equipped with a wireless sensor node to sense the real time Temperature and Humidity [1], also necessary actuators to control these environmental parameters. The color change in the body of the worms indicates the different stages  and the light yellowish indicates that they have reached to the cocoon stage and the morphological changes in silkworm structure can be used to detect abnormal worms[2].The proposed framework introduces an Internet of Things (IoT) empowered Wireless Personal Area Network (WPAN) system. The received image is first segregated into two classes as diseased or healthy by analyzing the histogram of the background removed image based on thresholding. Again the diseased class will be sub classified into 2 diseases as either Flacherie or Pebrine by applying suitable mask for extracting worm and obtaining the histogram of the worm and analyzing it. The result will be sent to the farmer via E-mail. The proposed system could be a probable solution for productivity in silkworms. View Article DOI: 10.47856/ijaast.2021.v08i9.00

استفاده از روش تشخيص لبه برای محاسبه پارامتر سطح در استاندارد PASI

مقدمه : اين مطالعه جهت تعيين پارمتر سطح در استاندارد PASI در بيماری پسوريازيس با تشخيص لبه در پردازش تصوير طراحی شده است. روش بررسی: در اين مقاله با استفاده از روش تشخيص لبه در يک روش نيمه اتوماتيک کامپيوتری ارايه می شود که برای محاسبه پارامتر سطح در استاندارد PASI در فضای رنگی CIE_LAB و انتخاب آستانه خودکار جهت تفکيک پلاکهای پوستی از تصوير استفاده می کند. در اين روش از 15 بيمار با بررسی بيش از 20 تصوير برای هرکدام صورت گرفته است. يافته ها: پس از بررسی تصاوير بيماران با اين روش در 65 درصد موارد دقت تشخيص تصوير بالای 90 درصد در 30 درصد ببين 80 تا 90 درصد و در 5 درصد بين 70 تا 80 درصد می باشد. نتيجه گيری: يکی از مزايای مهم اين روش حذف خودکار موی سر چشم و نويزهای موجود است. همچنين در اين روش می توان کمترين دخالت دستی در اجرا به دست آورد. با توجه به مشخص بودن لبه بيماری از پوست و وابستگی رنگ نوع پلاکها به رنگ پوست مي­توان با مطالعه جامع­تر در زمينه پزشکی و پوست به اطلاعات کاملتری از آن دست يافت. و اين از برتری های اين روش ارايه شده می باشد

Expert knowledge based approach for automatic sorting and packing

The automatic sorting system is presented in this work which is based on expert knowledge and high resolution visual sensor. Proposed system was tested for natural amber sorting task. Five types of amber have been explored in this research. Experimental investigation involves amber classification in three different color spaces (RGB, HSV and Grayscale). The results have shown that the highest classification accuracy is reached using the combination of the most essential features sets acquired from different color spaces

Object Tracking Through the Use of Color Hue Image Processing

Many industrial and commercial applications today are beginning to use autonomous systems to increase productivity and cut costs in production and manpower. Most of these applications are only semi autonomous; they still need assistance from a human to either start up or receive continual instructions. With the improvement of image processing techniques, camera processing capabilities and more efficient vehicles, a new wave of fully autonomous vehicles can be implemented. A simple system that uses a dedicated image processor connected to an RC vehicle can be developed to provide an example for the new techniques currently available. The Pixycam, a dedicated color-hue image processing camera, can be used to allow an RC vehicle to track colored objects autonomously. The results of this system show that the combination of the Pixycam and an RC vehicle can be used to successfully track and follow colored objects in well-lit environments. In order to demonstrate this system and its different capabilities, MATLAB and Pixymon were used to simulate and compare the image processing power of the camera. The final system and colored objects were then used to move the vehicle around on a flat surface testing the responsiveness of the system

Classification of novel selected region of interest for color image encryption

Securing digital image in exchanging huge multimedia data over internet with limited bandwidth is a significant and sensitive issue. Selective image encryption being an effective method for reducing the amount of encrypted data can achieve adequate security enhancement. Determining and selecting the region of interest in digital color images is challenging for selective image encryption due to their complex structure and distinct regions of varying importance. We propose a new feature in acquiring and selecting Region of Interest (ROI) for the color images to develop a selective encryption scheme. The hybrid domain is used to encrypt regions based on chaotic map approach which automatically generates secret key. This new attribute is a vitality facet representing the noteworthy part of the color image. The security performance of selective image encryption is found to enhance considerably based on the rates of encrypted area selection. Computation is performed using MATLAB R2008a codes on eight images (Lena, Pepper, Splash, Airplane, House, Tiffany, Baboon and Sailboat) each of size 512*512 pixels obtained from standard USC-SIPI Image Database. A block size of 128*128 pixels with threshold levels 0.0017 and 0.48 are employed. Results are analyzed and compared with edge detection method using the same algorithm. Encrypted area, entropy and correlation coefficients performances reveal that the proposed scheme achieves good alternative in the confined region of interest, fulfills the desired confidentiality and protects image privacy

New Aggregation Approaches with HSV to Color Edge Detection

The majority of edge detection algorithms only deal with grayscale images, while their use with color images remains an open problem. This paper explores different approaches to aggregate color information of RGB and HSV images for edge extraction purposes through the usage of the Sobel operator and Canny algorithm. This paper makes use of Berkeley’s image data set, and to evaluate the performance of the different aggregations, the F-measure is computed. Higher potential of aggregations with HSV channels than with RGB channels is found. This article also shows that depending on the type of image used, RGB or HSV, some methods are more appropriate than others

Cloud-Edge suppression for visual outdoor navigation

Hoffmann A, Möller R. Cloud-Edge suppression for visual outdoor navigation. Robotics. 2017;6(4): 38.Outdoor environments pose multiple challenges for the visual navigation of robots, like changing illumination conditions, seasonal changes, dynamic environments and non-planar terrain. Illumination changes are mostly caused by the movement of the Sun and by changing cloud cover. Moving clouds themselves also are a dynamic aspect of a visual scene. For visual homing algorithms, which compute the direction to a previously visited place by comparing the current view with a snapshot taken at that place, in particular, the changing cloud cover poses a problem, since cloud movements do not correspond to movements of the camera and thus constitute misleading information. We propose an edge-filtering method operating on linearly-transformed RGB channels, which reliably detects edges in the ground region of the image while suppressing edges in the sky region. To fulfill this criterion, the factors for the linear transformation of the RGB channels are optimized systematically concerning this special requirement. Furthermore, we test the proposed linear transformation on an existing visual homing algorithm (MinWarping) and show that the performance of the visual homing method is significantly improved compared to the use of edge-filtering methods on alternative color information

Modeling Of Mouse Eye And Errors In Ocular Parameters Affecting Refractive State

ABSTRACT MODELING OF MOUSE EYE AND ERRORS IN OCULAR PARAMETERS AFFECTING REFRACTIVE STATE by GURINDER BAWA September 2013 Advisor: Dr. Ivan Avrutsky Major: Electrical Engineering Degree: Doctor of Philosophy Rodents eye are particularly used to study refractive error state of an eye and development of refractive eye. Genetic organization of rodents is similar to that of humans, which makes them interesting candidates to be researched upon. From rodents family mice models are encouraged over rats because of availability of genetically engineered models. Despite of extensive work that has been performed on mice and rat models, still no one is able to quantify an optical model, due to variability in the reported ocular parameters. In this Dissertation, we have extracted ocular parameters and generated schematics of eye from the raw data from School of Medicine, Detroit. In order to see how the rays would travel through an eye and the defects associated with an eye; ray tracing has been performed using ocular parameters. Finally we have systematically evaluated the contribution of various ocular parameters, such as radii of curvature of ocular surfaces, thicknesses of ocular components, and refractive indices of ocular refractive media, using variational analysis and a computational model of the rodent eye. Variational analysis revealed that variation in all the ocular parameters does affect the refractive status of the eye, but depending upon the magnitude of the impact those parameters are listed as critical or non critical. Variation in the depth of the vitreous chamber, thickness of the lens, radius of the anterior surface of the cornea, radius of the anterior surface of the lens, as well as refractive indices for the lens and vitreous, appears to have the largest impact on the refractive error and thus are categorized as critical ocular parameters. The radii of the posterior surfaces of the cornea and lens have much smaller contributions to the refractive state, while the radii of the anterior and posterior surfaces of the retina have no effect on the refractive error. These data provide the framework for further refinement of the optical models of the rat and mouse eye and suggest that extra efforts should be directed towards increasing the linear resolution of the rodent eye biometry and obtaining more accurate data for the refractive indices of the lens and vitreous

BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator