Bio-Inspired Firefly Algorithm for Polygonal Approximation on Various Shapes

Image analysis and computer vision: 1991

Functionality in Object Recognition: Guest Editor's Introduction

With the rapid development of computer, computer vision technology is also making rapid progress. In this paper, deep neural network algorithm is used to improve the technology of computer vision, improve the visual effect, and at the same time, innovative algorithm structure, improve the identification of pattern recognition. Computer vision and pattern recognition is a very cutting edge technology that has given people very advanced tools for vision and recognition. At present, the technology can control more accurate image resolution and improve the ability of pattern recognition. This article mainly explains the process of using deep neural network algorithm to improve computer vision and pattern recognition from the internal mechanism, and reveals the working principle and internal mechanism of computer vision and pattern recognition technology application. Data analysis proves that the pattern recognition application established by deep neural network algorithm performs very well in the field of vision and pattern recognition.KeywordsDeep neural network algorithmComputer visionPattern recognitionTechnical application

Multioutput regression has recently shown great ability to solve challenging problems in both computer vision and medical image analysis. However, due to the huge image variability and ambiguity, it is fundamentally challenging to handle the highly complex input-target relationship of multioutput regression, especially with indiscriminate high-dimensional representations. In this paper, we propose a novel supervised descriptor learning (SDL) algorithm for multioutput regression, which can establish discriminative and compact feature representations to improve the multivariate estimation performance. The SDL is formulated as generalized low-rank approximations of matrices with a supervised manifold regularization. The SDL is able to simultaneously extract discriminative features closely related to multivariate targets and remove irrelevant and redundant information by transforming raw features into a new low-dimensional space aligned to targets. The achieved discriminative while compact descriptor largely reduces the variability and ambiguity for multioutput regression, which enables more accurate and efficient multivariate estimation. We conduct extensive evaluation of the proposed SDL on both synthetic data and real-world multioutput regression tasks for both computer vision and medical image analysis. Experimental results have shown that the proposed SDL can achieve high multivariate estimation accuracy on all tasks and largely outperforms the algorithms in the state of the arts. Our method establishes a novel SDL framework for multioutput regression, which can be widely used to boost the performance in different applications.

Part 1 Pattern recognition: synthesis and classification of 2-D shapes from their landmark points, N. Perez de la Blanca. Part 2 Image analysis and computer vision: projective invariants to identify polyhedric 3D objects, A. Sanfeliu and A. Llorens non-supervised characterization of galaxies using different approaches, J.A. Garcia, et al analysis and optimization of the k-Means algorithm for remote sensing applications, P. Montolio, et al. Part 3 Speech recognition: isolated word recognition based on multilayer perceptions, F. Casacuberta, et al. Part 4 Applications in image analysis and computer vision: analysis of gammagraphic images by mathematical morphology, A. Dupuy, et al a new hybrid coding method for videoconferencing applications and ISDN, J. Zamora, et al artificial vision applied to guidance of an autonomous robot, M. Mazo and D. Maravall, et al.

Exploration and exploitation are two strategies used to search the problem space in Evolutionary Algorithms (EAs). To significantly increase the performance of these optimization techniques in terms of the solution optimality is to strike the right balance between exploration and exploitation. Firefly is one of the most favored EAs. In this study, we introduce an entire fuzzy system to tune dynamically the firefly parameters in order to keep the exploration and exploitation in balance in each of the searching steps. A serious concern of EAs is to be stuck in local optimum solutions. The proposed fuzzy controller helps the firefly algorithm to converge to the optimal solution and escape from local optimums. To evaluate the efficiency of the fuzzy-based firefly algorithm, we conduct experiments on a set of high dimensional benchmark functions. The goal here is to compare the new firefly method with the standard firefly and well-known nature-inspired optimization algorithms. The results of the experiments show the superiority of the proposed Fuzzy firefly algorithm over the standard one.

We propose a novel computationally efficient correspondence algorithm to identify the correspondence of similar features of the Reference frame to Search frame in the dynamic image sequence analysis for face tracking. The correspondence of the features between the set of images is the central problem in Computer Vision(CV), Image Analysis(IA) and Pattern Recognition(PR). In these areas Correspondence of features is the combinatorial explosion problem or NP-hard, because of their exhaustive searching for features in the sequence of image frames. This correspondence has three major steps such as segmentation, feature extraction and matching. In this paper we propose block-based segmentation in which the frame which contains the face to be track can be segmented as window of size 20*20 which covers the face. Here we consider the window trace mean is the feature since this is rotation invariant and it covers all the rows and columns of the window and it reduces the dimensionality and finally for matching we used the minimum absolute difference method which does not involves more computations since no multiplication operation is involved. We restrict the searching space to [-3,+3] pixels horizontally and vertically in the search image. Experimental results show that this novel algorithm could achieve much higher computational reduction as compared with full search (FS), Diamond search (DS)[6], Cross Diamond Search (CDS)[7,8] and Area Based Correspondence[14] algorithm for face tracking image sequence while similar prediction accuracy is maintained and it is especially suitable for video conferencing and slow moving dynamic image sequence.

This paper presents a comprehensive review of image representation and color spaces in the domain ofcomputer vision. Image representation serves as the foundation of computer vision systems, encompassingtechniques such as pixel-based, vector-based, and feature-based representations. Color spaces provide astandardized framework for encoding color information in digital images, with popular models includingRGB, HSV, Lab, and CMYK. The paper explores fundamental concepts, comparative analysis, practicalapplications, and future directions in image representation and color spaces. Insights gained from the reviewhighlight the significance of these concepts in various computer vision applications, including objectrecognition, image segmentation, and image enhancement. Future research directions include addressingchallenges such as achieving color constancy and developing adaptive color space selection techniques. Byleveraging the findings from this review, researchers and practitioners can advance the state-of-the-art incomputer vision and develop more robust and effective systems for real-world applications.

Historically, geoscience has been a prominent domain for applications of computer vision and pattern recognition. The numerous challenges associated with geoscience-related imaging data, which include poor imaging quality, noise, missing values, lack of precise boundaries defining various geoscience objects and processes, as well as non-stationarity in space and/or time, provide an ideal test bed for advanced computer vision techniques. On the other hand, the developments in pattern recognition, especially with the rapid evolution of powerful graphical processing units (GPUs) and the subsequent deep learning breakthrough, enable valuable computational tools, which can aid geoscientists in important problems, such as land cover mapping, target detection, pattern mining in imaging data, boundary extraction and change detection. In this landscape, classical computer vision approaches, such as active contours, superpixels, or descriptor-guided classification, provide alternatives that remain relevant when domain expert labelling of large sample collections is often not feasible. This issue persists, despite efforts for the standardization of geoscience datasets, such as Microsoft’s effort for AI on Earth, or Google Earth. This work covers developments in applications of computer vision and pattern recognition on geoscience-related imaging data, following both pre-deep learning and post-deep learning paradigms. Various imaging modalities are addressed, including: multispectral images, hyperspectral images (HSIs), synthetic aperture radar (SAR) images, point clouds obtained from light detection and ranging (LiDAR) sensors or digital elevation models (DEMs).

Can computer systems help us understand paintings and the methods of great masters? In this three-part series, David C. Stork describes how he uses techniques adapted from computer vision and image analysis to shed light on art and problems in art history. In part 1, the subject is perspective, and among the questions posed: did the artists of the Renaissance use optical projections as has been recently hypothesized?

In the past decade, people's interest in computer vision has grown. Stable multiplication rate boost with powerful computing power every 13 months, face detection and recognition has changed from esoteric concept to popular One of the better and successful applications of computer vision research and image analysis and image analysis Algorithm-based understanding. In our work, we will be detecting contours (shapes) of various geometrical figures in the sample given binary mask extracted from HSV (Hue Saturation Value) range using Python 3.6, Open-Source Computer Vision Library (OpenCV 3x) and Numpy. The very fundamental functions are used for processing the frames, that involves getting live video feed, loading them frame-by-frame, and detecting various shape of red colors inside the live feed frames and segment them with previously saved image with the exact same location. In this paper we discuss three primary problems of computer vision: Finding contours, detection of specified color and segment with another image. We tried state-of-the-art algorithms for these three tasks. HSV color extraction is used to get the mask of desire colored object. In order to get high quality image segmentations results, we applied Morphological operations to refine the mask such as, Opening which removes unnecessary details which in our case are the white region on the boundary of the cloth that is not required, Erosion, Dilatation operation and Gaussian Blurring to smoothen the result. We link this contour detector with Convex Hull, this strategy performs better than our existing image segmentation algorithms on measures of both boundary and segmentation quality. Finally, we replaced pixel value of red color region detected by HSV mask with the pixel value of our background image with the help of bitwise operation to get the augmented result of invisible cloak.

The method of Probabilistic Nodes Combination (PNC) enables interpolation and modeling of two-dimensional curves using nodes combinations and different coefficients γ: polynomial, sinusoidal, cosinusoidal, tangent, cotangent, logarithmic, exponential, arc sin, arc cos, arc tan, arc cot or power function, also inverse functions. This probabilistic view is novel approach a problem of modeling and interpolation. Computer vision and pattern recognition are interested in appropriate methods of shape representation and curve modeling. PNC method represents the possibilities of shape reconstruction and curve interpolation via the choice of nodes combination and probability distribution function for interpolated points. It seems to be quite new look at the problem of contour representation and curve modeling in artificial intelligence and computer vision. Function for γ calculations is chosen individually at each curve modeling and it is treated as probability distribution function: γ depends on initial requirements and curve specifications.

Concepts from Information Theory have been used quite widely in Image Processing, Computer Vision and Medical Image Analysis for several decades now. Most widely used concepts are that of KL-divergence, minimum description length (MDL), etc. These concepts have been popularly employed for image registration, segmentation, classification etc. In this chapter we review several methods, mostly developed by our group at the Center for Vision, Graphics & Medical Imaging in the University of Florida, that glean concepts from Information Theory and apply them to achieve analysis of Diffusion-Weighted Magnetic Resonance (DW-MRI) data.This relatively new MRI modality allows one to non-invasively infer axonal connectivity patterns in the central nervous system. The focus of this chapter is to review automated image analysis techniques that allow us to automatically segment the region of interest in the DWMRI image wherein one might want to track the axonal pathways and also methods to reconstruct complex local tissue geometries containing axonal fiber crossings. Implementation results illustrating the algorithm application to real DW-MRI data sets are depicted to demonstrate the effectiveness of the methods reviewed.

The process parameter in the non-traditional machining process is an important aspect of enhancing the performance parameter. Precision machining and machining of complex part is a challenging task which is time consuming and expensive. Therefore, the optimal values of process parameters should be selected so that performance parameter, like, material removal rate (MRR), radial overcut (ROC) and heat affected zone (HAZ), etc., can be optimised. In this paper, a recently developed optimisation technique firefly algorithm (FA) is considered for finding out the optimal solution. The FA technique is attempted to two non-traditional machining processes, electrochemical micromachining (EMM) and electrochemical discharge machining (ECDM). The variations of process parameters w.r.t performance parameter are represented graphically for better and effective study of the machining processes. The results of EMM and ECDM obtained by using FA are compared and it is found that the results obtained by the application of FA are better compared to the previous researcher's result.

The analysis of natural images has been the topic of research in uncountable articles in computer vision and pattern recognition (e.g., natural images has been used as benchmarks for object recognition and image retrieval). However, despite the research progress in such field, there is a gap in the analysis of certain type of natural images, for instance, those in the context of animal behavior. In fact, biologists perform the analysis of natural images manually without the aid of techniques that were supposedly developed for this purpose. In this context, this paper presents a study on automated methods for the analysis of natural images of hummingbirds with the goal to assist biologists in the study of animal behavior. The automated analysis of hummingbird behavior is challenging mainly because of (1) the speed at which these birds move and interact; (2) the unpredictability of their trajectories; and (3) its camouflage skills. We report a comparative study of two deep learning approaches for the detection of hummingbirds in their nest. Two variants of transfer learning from convolutional neural networks (CNNs) are evaluated in real imagery for hummingbird behavior analysis. Transfer learning is adopted because not enough images are available for training a CNN from scratch, besides, transfer learning is less time consuming. Experimental results are encouraging, as acceptable classification performance is achieved with CNN-based features. Interestingly, a pretrained CNN without fine tunning and a standard classifier performed better in the considered data set.