Выделение малоразмерных изображений объектов нерегулярного вида

З метою підвищення стійкості, адекватності і точності виділення малорозмірних зображень об’єктів нерегулярного вигляду в автоматичному режимі в роботі запропоновано підхід, який засновано на аналізі контрастності розглядаємих зображень відносно фону з використанням спеціального виду регіональних масок, адаптованих до параметрів форми аналізуємих об’єктів нерегулярного вигляду.With the aim to increase stability, adequacy and accuracy of segmentation of small-sized images of irregular objects in automatic mode, an approach is suggested that is based on the image vs. background contrast analysis that uses the proposed regional masks being adjusted to the form parameters of the considered irregular objects

Application of Fuzzy Logic on Image Edge Detection

In this paper a novel method for an application of digital image processing, Edge Detection is developed. The contemporary Fuzzy logic, a key concept of artificial intelligence helps to implement the fuzzy relative pixel value algorithms and helps to find and highlight all the edges associated with an image by checking the relative pixel values and thus provides an algorithm to abridge the concepts of digital image processing and artificial intelligence. Exhaustive scanning of an image using the windowing technique takes place which is subjected to a set of fuzzy conditions for the comparison of pixel values with adjacent pixels to check the pixel magnitude gradient in the window. After the testing of fuzzy conditions the appropriate values are allocated to the pixels in the window under testing to provide an image highlighted with all the associated edges

Detecting text in clutter scene

University of Technology Sydney. Faculty of Engineering and Information Technology.We often encounter cluttered visual scenes and need to identify objects correctly to navigate and interact with the world. As text takes the typical form of a human-designed informative visual object, retrieving texts in both indoor and outdoor environments is an important step towards providing contextual clues for a wide variety of vision tasks. Furthermore, it plays an invaluable role for multimedia retrieval and location based services. Text detection from clutter background is nevertheless a challenging task because the text, being figures in image, can be presented in various ways with lots of room for uncertainty such as size, scale, font type, font texture and colour, unpredicted decorative elements put on the text, etc. The situation will be even more complicated if the text is presented in a clutter background where non-text objects possess similar low-level features to text. Further, all these objects are composed of distinct geometric shapes and they are similar with the essential composition elements of text objects. Pursuing a robust text feature descriptor is therefore always difficult because special feature descriptor is only a fragment of text existence. It needs the completely understanding of text. Regarding the design, understanding, representation and calculating of text as one unitary process of text perceiving, we deal with the completely understanding and representation of text in image with many kinds of aspects in different levels. Without following the legend feature based solution, this research is motivated by perceptual image processing and the observation of painting masters. It will explore a brand new solution by investigating the spatial structure of text and the compositional complexity of the visual object (i.e. text) in image. The research will present the composition granularity indicator and expose novel discriminable attributes embedded inside text objects, which can successfully differentiate text regions and non-text regions on clutter backgrounds. As figures in image with the clutter scene, it is merely the physical appearance of text which provides the perceptual content and plays a central role for text detection, i.e. location and coarse identification. During the view-construction of text, properties of individual character and textual organization of characters build up the physical appearance. When observers see text appearance in clutter scene, they describe their feelings in terms of crowding effect and clutter. However, the appearance of text still has enough saliency to reveal an informative message. Accordingly, text not only has the characteristics of crowding effect and clutter but also follows the principles of saliency. Significantly, the crowding effect of text is derived from the space regularity of inbuilt neighbouring letters which have commonalities beside their distinctiveness. In addition, low-level features of individual letters contribute to the commonalities and distinctiveness from the moment that the font is designed. Therefore, the computational model of text appearance is built up to integrate the three-level properties, including features of individual characters (low-level features), properties for spatial regularity (i.e. neighbourhood, appearance similarity), and the crowding statistics property of space averaged over pooling regions. In terms of image processing, if we consider the view construction of text, the features of individual characters in image processing are obtained on the basis of the properties of construction, including mean intensity, local RMS contrast, shape, pixel density, edge density, stroke width, straight line ratio, height to width ratio, stroke width to height ratio, etc. For the purpose of calculating the properties of space regularity and the crowding space averaging property, the spatial elements and relations are quantified and these involve space granularity and composition rules. If we examine the works of painters, especially impressionists, they use directional brushstroke or colour patches as space granularity to represent “formless” visual objects in space regularity instead of clear contour shape sketches. The space regularity of patches, i.e. repetitive patterns, can offer a compositional format to express an artist’s feelings about an object rather than to simply describe it. Secondly, it is the harmonious proportions among component parts that bridle component space patches into objects. If we consider the painter’s harmonious proportions, the component parts of an object can be said to react simultaneously so that they can be seen at one and the same time both together and separately. Similarly, image is described by a set of grey space patches in multi-grey levels. In addition, each space patch groups pixels in position proximity and similarity, in just the same way as the colour patch is used by impressionists. The space organisation of them is also quantified as the measurement of space relations, especially in terms of the neighbourhood and proportions among component parts. Moreover, the harmonious proportions among space patches are captured by the mathematical tool of geometric mean. Geometric mean (i.e., GM) is calculated over those space patches which possess the same grey level, and considered as the space granularity to form objects. Grey patches with the same GM are composed of GM regions, which are enlarged, extended kinds of pooling regions. Regions given by clusters which have resulted from similarity and neighbourhood are direct, compact pooling regions. Therefore, the statistical properties of space averaging are calculated over GM regions and image is represented as a set of GM regions over which text and other visual objects are analysed by GM indication. Finally, the representation of an image and the three-level computational text model are put into practice to develop a new-brand algorithm on the public benchmark dataset and to design and implement an automatic processing system on the real big data of the bank cheque. The resulting performance of these tools/processes shows that they are highly competitive and effective

Robust Camera Calibration and Evaluation Procedure Based on Images Rectification and 3D Reconstruction

This paper presents a robust camera calibration algorithm based on contour matching of a known pattern object. The method does not require a fastidious selection of particular pattern points. We introduce two versions of our algorithm, depending on whether we dispose of a single or several calibration images. We propose an evaluation procedure which can be applied for all calibration methods for stereo systems with unlimited number of cameras. We apply this evaluation framework to 3 camera calibration techniques, our proposed robust algorithm, the modified Zhang algorithm implemented by J. Bouguet and Faugeras-Toscani method. Experiments show that our proposed robust approach presents very good results in comparison with the two other methods. The proposed evaluation procedure gives a simple and interactive tool to evaluate any camera calibration method

Automated Vision-Based High Intraocular Pressure Detection Using Frontal Eye Images

Glaucoma, the silent thief of vision, is mostly caused by the gradual increase of pressure in the eye which is known as intraocular pressure (IOP). An effective way to prevent the rise in eye pressure is by early detection. Prior computer vision-based work regarding IOP relies on fundus images of the optic nerves. This paper provides a novel vision-based framework to help in the initial IOP screening using only frontal eye images. The framework first introduces the utilization of a fully convolutional neural (FCN) network on frontal eye images for sclera and iris segmentation. Using these extracted areas, six features that include mean redness level of the sclera, red area percentage, Pupil/Iris diameter ratio, and three sclera contour features (distance, area, and angle) are computed. A database of images from the Princess Basma Hospital is used in this work, containing 400 facial images; 200 cases with normal IOP; and 200 cases with high IOP. Once the features are extracted, two classifiers (support vector machine and decision tree) are applied to obtain the status of the patients in terms of IOP (normal or high). The overall accuracy of the proposed framework is over 97.75% using the decision tree. The novelties and contributions of this work include introducing a fully convolutional network architecture for eye sclera segmentation, in addition to scientifically correlating the frontal eye view (image) with IOP by introducing new sclera contour features that have not been previously introduced in the literature from frontal eye images for IOP status determination.https://doi.org/10.1109/JTEHM.2019.291553

Determination of Structure from Motion Using Aerial Imagery

The structure from motion process creates three-dimensional models from a sequence of images. Until recently, most research in this field has been restricted to land-based imagery. This research examines the current methods of land-based structure from motion and evaluates their performance for aerial imagery. Current structure from motion algorithms search the initial image for features to track though the subsequent images. These features are used to create point correspondences between the two images. The correspondences are used to estimate the motion of the camera and then the three-dimensional structure of the scene. This research tests current algorithms using synthetic data for correctness and to characterize the motions necessary to produce accurate models. Two approaches are investigated: full Euclidean reconstructions, where the camera motion is estimated using the correspondences, and navigation-aided Euclidean reconstructions, where the camera motion is calculated using the Global Positioning System and inertial navigation system data from the aircraft. Both sets algorithms are applied to images collected from an airborne blimp. It is found that full Euclidean reconstructions have two orders of magnitude more error than navigation-aided Euclidean reconstructions when using typical images from airborne cameras