A Convolutional Neural Networks-Based Approach for Texture Directionality Detection

The perceived texture directionality is an important, not fully explored image characteristic. In many applications texture directionality detection is of fundamental importance. Several approaches have been proposed, such as the fast Fourier-based method. We recently proposed a method based on the interpolated grey-level co-occurrence matrix (iGLCM), robust to image blur and noise but slower than the Fourier-based method. Here we test the applicability of convolutional neural networks (CNNs) to texture directionality detection. To obtain the large amount of training data required, we built a training dataset consisting of synthetic textures with known directionality and varying perturbation levels. Subsequently, we defined and tested shallow and deep CNN architectures. We present the test results focusing on the CNN architectures and their robustness with respect to image perturbations. We identify the best performing CNN architecture, and compare it with the iGLCM, the Fourier and the local gradient orientation methods. We find that the accuracy of CNN is lower, yet comparable to the iGLCM, and it outperforms the other two methods. As expected, the CNN method shows the highest computing speed. Finally, we demonstrate the best performing CNN on real-life images. Visual analysis suggests that the learned patterns generalize to real-life image data. Hence, CNNs represent a promising approach for texture directionality detection, warranting further investigation.

Comparative Study on Effects of Thermal Gradient Direction on Heat Exchange between a Pure Fluid and a Nanofluid: Employing Finite Volume Method

This work aims to determine how the temperature gradient orientation affects the heat exchange between two superposed fluid layers separated by zero wall thickness. The finite volume method (FVM) has been developed to solve the governing equations of both fluid layers. To achieve the coupling between the two layers, the heat flow continuity with the no-slip condition at the interface was adopted. The lower part of the space is filled with a nanofluid while the upper part is filled with a pure fluid layer. We have explored two cases of temperature gradient orientation: parallel gradient to gravity forces of our system and perpendicular gradient to gravity forces. We took a set of parameters, Ri and ϕ, to see their influence on the thermal and hydrodynamic fields as well as the heat exchange rate between the two layers. The main applications of this study related to biological systems such as the cytoplasm and the nucleoplasm are phase-separated solutions, which can be useful as models for membranelles organelles and can serve as a cooling system application using heat exchange. The Richardson number and the volume of nanosolid particles have a big impact on the rate of change of heat transmission. When a thermal gradient is perpendicular to gravity forces, total heat transmission improves with increasing solid volume percentage, but when the thermal gradient is parallel to gravity forces, overall heat transfer decreases significantly.

An efficient machine learning approach to nephrology through iris recognition

Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system.

FGFF Descriptor and Modified Hu Moment-Based Hand Gesture Recognition

Gesture recognition has been studied for decades and still remains an open problem. One important reason is that the features representing those gestures are not sufficient, which may lead to poor performance and weak robustness. Therefore, this work aims at a comprehensive and discriminative feature for hand gesture recognition. Here, a distinctive Fingertip Gradient orientation with Finger Fourier (FGFF) descriptor and modified Hu moments are suggested on the platform of a Kinect sensor. Firstly, two algorithms are designed to extract the fingertip-emphasized features, including palm center, fingertips, and their gradient orientations, followed by the finger-emphasized Fourier descriptor to construct the FGFF descriptors. Then, the modified Hu moment invariants with much lower exponents are discussed to encode contour-emphasized structure in the hand region. Finally, a weighted AdaBoost classifier is built based on finger-earth mover’s distance and SVM models to realize the hand gesture recognition. Extensive experiments on a ten-gesture dataset were carried out and compared the proposed algorithm with three benchmark methods to validate its performance. Encouraging results were obtained considering recognition accuracy and efficiency.

An Efficient Machine Learning Approach to Nephrology Through Iris Recognition

Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system.

Noise Resilient Local Gradient Orientation for Content-Based Image Retrieval

In a world of multimedia information, where users seek accurate results against search query and demand relevant multimedia content retrieval, developing an accurate content-based image retrieval (CBIR) system is difficult due to the presence of noise in the image. The performance of the CBIR system is impaired by this noise. To estimate the distance between the query and database images, CBIR systems use image feature representation. The noise or artifacts present within the visual data might confuse the CBIR when retrieving relevant results. Therefore, we propose Noise Resilient Local Gradient Orientation (NRLGO) feature representation that overcomes the noise factor within the visual information and strengthens the CBIR to retrieve accurate and relevant results. The proposed NRLGO consists of three steps: estimation and removal of noise to protect the local visual structure; extraction of color, texture, and local contrast features; and, at the end, generation of microstructure for visual representation. The Manhattan distance between the query image and the database image is used to measure their similarity. The proposed technique was tested using the Corel dataset, which contains 10000 images from 100 different categories. The outcomes of the experiment signify that the proposed NRLGO has higher retrieval performance in comparison with state-of-the-art techniques.

Occlusion-aware Visual Tracker using Spatial Structural Information and Dominant Features

To overcome the problem of occlusion in visual tracking, this paper proposes an occlusion-aware tracking algorithm. The proposed algorithm divides the object into discrete image patches according to the pixel distribution of the object by means of clustering. To avoid the drifting of the tracker to false targets, the proposed algorithm extracts the dominant features, such as color histogram or histogram of oriented gradient orientation, from these image patches, and uses them as cues for tracking. To enhance the robustness of the tracker, the proposed algorithm employs an implicit spatial structure between these patches as another cue for tracking; Afterwards, the proposed algorithm incorporates these components into the particle filter framework, which results in a robust and precise tracker. Experimental results on color image sequences with different resolutions show that the proposed tracker outperforms the comparison algorithms on handling occlusion in visual tracking

Navigating the Tower of Babel: The Epistemological Shift of Bioinspired Innovation

The disparity between disciplinary approaches to bioinspired innovation has created a cultural divide that is stifling to the overall advancement of the approach for sustainable societies. This paper aims to advance the effectiveness of bioinspired innovation processes for positive benefits through interdisciplinary communication by exploring the epistemological assumptions in various fields that contribute to the discipline. We propose that there is a shift in epistemological assumptions within bioinspired innovation processes at the points where biological models derived from reductionist approaches are interpreted as socially-constructed design principles, which are then realized in practical settings wrought with complexity and multiplicity. This epistemological shift from one position to another frequently leaves practitioners with erroneous assumptions due to a naturalistic fallacy. Drawing on examples in biology, we provide three recommendations to improve the clarity of the dialogue amongst interdisciplinary teams. (1) The deliberate articulation of epistemological perspectives amongst team members. (2) The application of a gradient orientation towards sustainability instead of a dichotomous orientation. (3) Ongoing dialogue and further research to develop novel epistemological approaches towards the topic. Adopting these recommendations could further advance the effectiveness of bioinspired innovation processes to positively impact social and ecological systems.