Problem Of Object Recognition Research Articles

Addressing Once More the (Im)possibility of Color Reconstruction in Underwater Images.

Color is an important cue in object recognition and classification problems. In underwater imagery, colors undergo strong distortion due to light propagation through an absorbing and scattering medium. Distortions depend on a number of complex phenomena, the most important being wavelength-dependent absorption and the sensitivity of sensors in trichromatic cameras. It has been shown previously that unique reconstruction in this case is not possible-at least for a simplified image formation model. In this paper, the authors use numerical simulations to demonstrate that this statement also holds for the underwater image formation model that is currently the most sophisticated.

Recognition of Organic Waste Objects Based on Vision Systems Using Attention Convolutional Neural Networks Models

Purpose: High population growth and increasing consumption patterns have resulted in significant organic waste production. The public often does not understand the correct way to deal with the problem of organic waste, including public awareness regarding the need for its management. Therefore, a system is needed to recognize waste objects based on various types. Currently, much research in this field has been studying object recognition, for example, the implementation of the Convolutional Neural Networks (CNN) model. However, there are still various challenges that must be addressed, including objects with diverse visual characteristics such as form, size, color, and physical condition. This research focuses on developing a system that enhances object recognition of waste, specifically organic waste, using an Attention Convolutional Neural Network (ACNN). By integrating attention mechanisms into the CNN model, this study addresses the challenges of recognizing waste objects with diverse visual characteristics. The proposed system seeks to improve the accuracy and efficiency of organic waste identification, which is crucial for advancing waste management practices and reducing environmental impact. Methods: This research combines a CNN architecture with an attention mechanism to create a better object detection environment called Attention-CNN (ACNN). The ACNN architecture employed consists of one layer input, three convoluted layers, three max-pooling layers, one attention layer, one flattened layer, four dropout layers, and two dense layers arranged in a certain way. Result: The research result shows that the model CNN with attention mechanism (ACNN) was slightly better at 86.93% than the standard model of CNN, which accounted for 86.70% in accuracy. Novelty: In general, the current use of CNN architecture to address waste object recognition problems typically employs standard architectures, resulting in lower accuracy for complex waste objects. In contrast, our research integrates attention mechanisms into the CNN architecture (ACNN), enhancing the model's ability to focus on relevant features of waste objects. This leads to improved recognition accuracy and robustness against visual variability. This distinction is important as it overcomes the limitations of standard CNN models in handling visually diverse and complex waste objects, thereby highlighting the novelty and contribution of our research.

A modified algebraic method of mathematical signal processing in radar problems

A new method for approximate solution of ill-posed inverse problems of reconstructing images of objects with angular resolution exceeding the Rayleigh criterion is proposed and justified, i.e. with super resolution. Angular super-resolution allows you to obtain images of objects with increased clarity and distinguish previously invisible details of images of complex objects. In addition, on this basis, the probability of correct solutions to problems of object recognition and identification increases. Mathematically, the problem is reduced to solving the linear Fredholm integral equation of the first kind of convolution type. Solutions are sought with additional conditions in the form of restrictions on the location and size of the desired radiation source, which makes it possible to regularize the problem. The method is a development of one of the parameterization methods the algebraic method. The solution is sought in the form of a representation of the desired function in the area where the source is located in the form of a series expansion over the input sequence of orthogonal functions with unknown coefficients. Thus, the inverse problem is parameterized and reduced to searching for expansion coefficients. The presented method is based on the use of a priori information about the localization area of the radiation source, or on an estimate of the location and size of this area obtained by scanning the viewing sector with a goniometer system. Using zero values of the function describing the source outside this region, for systems based on antenna arrays it is possible to find tens and even hundreds of expansion coefficients of the desired function in a Fourier series. The solution is constructed in the form of an iterative process with a consistent increase in the number of functions used in the expansion until the solution remains stable. The adequacy and stability of the solutions was verified during numerical experiments using a mathematical model. The results of numerical studies show that the presented methods of digital processing of received signals make it possible to achieve an effective angular resolution 3–10 times higher than the Rayleigh criterion. The proposed method makes it possible to miniaturize the antenna system without degrading its characteristics. Compared to known ones, it is relatively simple, which allows it to be used by systems in real time.

An Image Recognition Method Based on Correct Parking of Intelligent Medical Vehicles

This paper introduces a circular object recognition method based on image processing technology. With the continuous development of robot technology, there is an increasing demand for efficient and accurate image processing technology in the fields of robot vision navigation and automated production. Therefore, this paper aims to propose an image processing method that can effectively recognize circular targets, and provide technical support for applications in the fields of robot vision navigation and automated production. The method first reads the image and converts it to a binary image, then constructs and closes the graph structure elements and fills the holes. By calculating the area and perimeter of the circular target, the method of threshold matching is used to determine the circular target. In this paper, the red and green circular targets are taken as examples to identify them respectively and judge whether the recognition is successful through logical operations. The experimental results show that the method can effectively identify circular targets under different clearing conditions, and has high accuracy and robustness. This technology can be applied in the fields of robot visual navigation, automatic production, etc., and provides technical support for realizing intelligent and efficient production and service. At the same time, the research method of this paper also provides reference and references for the solution of other object recognition problems.

Review on COVID-19 Detection Method Based on Deep Learning

This article discusses the use of deep learning in combating Covid-19. It has been four years since Covid-19 ravaged the world. We hope to help researchers and medical staff through our investigation. Deep learning is a very effective method, which has a wide range of functions. Deep learning neural network is very suitable for fighting against covid. We will also introduce the information of this article in detail. Because neural network is mainly used to fight against CT in tumor and other diseases. First, I introduce the main image types. This is mainly CT type. I introduced the main features of this figure.Then we introduce the problem of image classification. Here we use convolutional neural network and its optimization scheme. At last, the problem of object recognition and image segmentation is introduced. It is hoped that it will aid in the rapid adoption of deep learning in COVID-19 research. I also try my best to contribute to this research. In recent years, in-depth learning has played a vital role in all fields. Especially in medicine, we hope that through this report, we can give you a deeper understanding of deep learning.

Object Identification Using Deep Learning

Human visual system is very fast that he can easily detect and identify the objects using this visual system it can easily identify multiple objects and detect the obstacle. This time there is a very large amount of data, a faster CPU and good algorithm that can easily train computers to detect and classify multiple objects with high accuracy. It uses an algorithm to detect and identify the objects that is “You Only Look Once” (YOLO) algorithm. This project is based on a deep learning approach to solve the problem of object detection and recognition. This network is trained on the most challenging dataset known as (PASCAL VOC), in which object detection is conducted annually manner. The resulting system is very fast and accurate. Object recognition is a collection of related tasks for identifying objects in digital photograph. R-CNN is a family of techniques for addressing object localization and recognition tasks. Propose a new object detection method which improves the existing model at every stage of object detection and identification, use COCO dataset model in this lot of categories of images are stored.

СИСТЕМИ РОЗПІЗНАВАННЯ «СВІЙ-ЧУЖИЙ», ІНТЕГРОВАНІ В БОЙОВЕ ЕКІПІРУВАННЯ. ОГЛЯД ТЕХНОЛОГІЙ

This general article examines the means and methods of identification and directions for building a recognition system, for which scientific publications are analyzed in detail, which highlight the current state of existing developments in Ukraine and the leading countries of the world. Solving issues related to the identification of one's units, unerringly separating them from opposing forces of the enemy, thereby maximally eliminating the probability of falling under «Friendly fire» of one's fire equipment is relevant and timely and is possible due to the development of new methodological approaches that are based on mathematical support in accordance with the principles tasks of search and simulation modeling in the environment of modern software packages. Attention is paid to the examination of known technological measures and it is shown that they are not able to provide an effective solution to problems related to the recognition of one's own and enemy forces. Taking into account the trends in the development of the weapons of the probable enemy, conditions are determined that should be considered the most typical for solving the problem of reliable recognition of manpower on the battlefield. It is substantiated that in order to achieve it, it is necessary to carry out the formulation and solve a number of interrelated scientific and technical problems, from a proposal for improving the characteristics of targets – «own», «foreign» and «neutral» to the development of a rational structure of a system for complex solution of tasks of registration and signal processing and development of approaches to solving the problem of object recognition in combat conditions based on the information-theoretical approach to the search problem, to the synthesis of home-foreign recognition systems integrated into combat equipment. Keywords: systems of recognition, battle equipment, thermal emitters, laser location, optics of the nightly seeing, sensitive elements of information system warning on dangerous.

Complex-Valued Iris Recognition Network.

In this work, we design a fully complex-valued neural network for the task of iris recognition. Unlike the problem of general object recognition, where real-valued neural networks can be used to extract pertinent features, iris recognition depends on the extraction of both phase and magnitude information from the input iris texture in order to better represent its biometric content. This necessitates the extraction and processing of phase information that cannot be effectively handled by a real-valued neural network. In this regard, we design a fully complex-valued neural network that can better capture the multi-scale, multi-resolution, and multi-orientation phase and amplitude features of the iris texture. We show a strong correspondence of the proposed complex-valued iris recognition network with Gabor wavelets that are used to generate the classical IrisCode; however, the proposed method enables a new capability of automatic complex-valued feature learning that is tailored for iris recognition. We conduct experiments on three benchmark datasets - ND-CrossSensor-2013, CASIA-Iris-Thousand and UBIRIS.v2 - and show the benefit of the proposed network for the task of iris recognition. We exploit visualization schemes to convey how the complex-valued network, when compared to standard real-valued networks, extracts fundamentally different features from the iris texture.

Hybrid Optimization-Enabled Deep Learning for Indoor Object Detection and Distance Estimation to Assist Visually Impaired Persons

Indoor object recognition mainly deals with recognizing indoor objects and in recent years, it becomes a crucial research topic among investigators. Visually Impaired Persons (VIPs) is a group of people who have a disability to visualize objects and indoor assistance to such people for safe navigation seems to be a challenging task. Several innovative devices and methods have been developed to assist VIP's towards their destination but, most of them failed to address the problem of multi-class object recognition. Moreover, such methods are incapable to provide an accurate distance between an object and a person. To bridge this gap, an efficacious model is proposed to assist VIP's in their daily lives for indoor object recognition using a newly designed Honey Adam African Vultures Optimization (HAAVO) algorithm. Here, object detection and object recognition is carried out using Generative Adversarial Network (GAN) and Deep Convolutional Neural Network (DCNN), respectively. The DCNN classifier and Deep Residual Network (DRN) utilized to estimate the distance is optimally trained using the proposed HAAVO. In addition, the designed HAAVO is obtained by integration of the Honey Badger Algorithm (HBA) with Adam Optimizer and African Vultures Optimization (AVO). The devised model provided high accuracy with respect to object detection and delivered superior performance values in accordance with testing accuracy, precision, and recall with the measures of 0.940, 0.946, and 0.953.

AutoML-Based Neural Architecture Search for Object Recognition in Satellite Imagery

Advancements in optical satellite hardware and lowered costs for satellite launches raised the high demand for geospatial intelligence. The object recognition problem in multi-spectral satellite imagery carries dataset properties unique to this problem. Perspective distortion, resolution variability, data spectrality, and other features make it difficult for a specific human-invented neural network to perform well on a dispersed type of scenery, ranging data quality, and different objects. UNET, MACU, and other manually designed network architectures deliver high-performance results for accuracy and prediction speed in large objects. However, once trained on different datasets, the performance drops and requires manual recalibration or further configuration testing to adjust the neural network architecture. To solve these issues, AutoML-based techniques can be employed. In this paper, we focus on Neural Architecture Search that is capable of obtaining a well-performing network configuration without human manual intervention. Firstly, we conducted detailed testing on the top four performing neural networks for object recognition in satellite imagery to compare their performance: FastFCN, DeepLabv3, UNET, and MACU. Then we applied and further developed a Neural Architecture Search technique for the best-performing manually designed MACU by optimizing a search space at the artificial neuron cellular level of the network. Several NAS-MACU versions were explored and evaluated. Our developed AutoML process generated a NAS-MACU neural network that produced better performance compared with MACU, especially in a low-information intensity environment. The experimental investigation was performed on our annotated and updated publicly available satellite imagery dataset. We can state that the application of the Neural Architecture Search procedure has the capability to be applied across various datasets and object recognition problems within the remote sensing research field.

3D object detection through fog and occlusion: passive integral imaging vs active (LiDAR) sensing.

In this paper, we address the problem of object recognition in degraded environments including fog and partial occlusion. Both long wave infrared (LWIR) imaging systems and LiDAR (time of flight) imaging systems using Azure Kinect, which combine conventional visible and lidar sensing information, have been previously demonstrated for object recognition in ideal conditions. However, the object detection performance of Azure Kinect depth imaging systems may decrease significantly in adverse weather conditions such as fog, rain, and snow. The concentration of fog degrades the depth images of Azure Kinect camera, and the overall visibility of RGBD images (fused RGB and depth image), which can make object recognition tasks challenging. LWIR imaging may avoid these issues of lidar-based imaging systems. However, due to poor spatial resolution of LWIR cameras, thermal imaging provides limited textural information within a scene and hence may fail to provide adequate discriminatory information to identify between objects of similar texture, shape and size. To improve the object detection task in fog and occlusion, we use three-dimensional (3D) integral imaging (InIm) system with a visible range camera. 3D InIm provides depth information, mitigates the occlusion and fog in front of the object, and improves the object recognition capabilities. For object recognition, the YOLOv3 neural network is used for each of the tested imaging systems. Since the concentration of fog affects the images from different sensors (visible, LWIR, and Azure Kinect depth cameras) in different ways, we compared the performance of the network on these images in terms of average precision and average miss rate. For the experiments we conducted, the results indicate that in degraded environment 3D InIm using visible range cameras can provide better image reconstruction as compared to the LWIR camera and Azure Kinect RGBD camera, and therefore it may improve the detection accuracy of the network. To the best of our knowledge, this is the first report comparing the performance of object detection between passive integral imaging system vs active (LiDAR) sensing in degraded environments such as fog and partial occlusion.

Visual Object Recognition System

This article introduces the problem of object detection and recognition. The potential mobility of this solution, ease of installation and ease of initial setup, as well as the absence of expensive, resource-intensive and complex image collection and processing systems are presented. Solutions to the problem are demonstrated, along with the advantages and disadvantages of each. The selection of contours by a filter based on the Prewitt operator and a detector of characteristic points is an algorithm of the system, developed within the framework of object recognition techniques. The reader can follow the interim and final demonstrations of the system algorithm in this article to learn about its advantages over traditional video surveillance systems, as well as some of its disadvantages. A webcam with a video frame rate of 25 frames per second, a mobile phone and a PC with the Matlab2020 programming environment installed (due to its convenience and built-in image processing functions) are required to illustrate how the system works.

Grassmannian learning mutual subspace method for image set recognition

This paper addresses the problem of object recognition given a set of images as input (e.g., multiple camera sources and video frames). Convolutional neural network (CNN)-based frameworks do not exploit these sets effectively, processing a pattern as observed, not capturing the underlying feature distribution as it does not consider the variance of images in the set. To address this issue, we propose the Grassmannian learning mutual subspace method (G-LMSM), a NN layer embedded on top of CNNs that can process image sets more effectively and can be trained in an end-to-end manner. The image set is first represented by a low-dimensional input subspace and then this input subspace is matched with dictionary subspaces by a similarity of their canonical angles, an interpretable and easy to compute metric. The key idea of G-LMSM is that the dictionary subspaces are learned as points on the Grassmann manifold, optimized with Riemannian stochastic gradient descent. This learning is stable, efficient and theoretically well-grounded. We demonstrate the effectiveness of our proposed method on hand shape recognition, face identification, and facial emotion recognition.

Application of methods of fractal images analysis in solving environmental assessment and object recognition problems

Application of methods of fractal images analysis in solving environmental assessment and object recognition problems

Mispronunciation Detection and Diagnosis with Articulatory-Level Feedback Generation for Non-Native Arabic Speech

A high-performance versatile computer-assisted pronunciation training (CAPT) system that provides the learner immediate feedback as to whether their pronunciation is correct is very helpful in learning correct pronunciation and allows learners to practice this at any time and with unlimited repetitions, without the presence of an instructor. In this paper, we propose deep learning-based techniques to build a high-performance versatile CAPT system for mispronunciation detection and diagnosis (MDD) and articulatory feedback generation for non-native Arabic learners. The proposed system can locate the error in pronunciation, recognize the mispronounced phonemes, and detect the corresponding articulatory features (AFs), not only in words but even in sentences. We formulate the recognition of phonemes and corresponding AFs as a multi-label object recognition problem, where the objects are the phonemes and their AFs in a spectral image. Moreover, we investigate the use of cutting-edge neural text-to-speech (TTS) technology to generate a new corpus of high-quality speech from predefined text that has the most common substitution errors among Arabic learners. The proposed model and its various enhanced versions achieved excellent results. We compared the performance of the different proposed models with the state-of-the-art end-to-end technique of MDD, and our system had a better performance. In addition, we proposed using fusion between the proposed model and the end-to-end model and obtained a better performance. Our best model achieved a 3.83% phoneme error rate (PER) in the phoneme recognition task, a 70.53% F1-score in the MDD task, and a detection error rate (DER) of 2.6% for the AF detection task.

Object Recognition and Dorsal Stream Vulnerabilities in Children With Early Brain Damage.

AimVisual functions of the dorsal stream are considered vulnerable in children with early brain damage. Considering the recognition of objects in suboptimal representations a dorsal stream dysfunction, we examined whether children with early brain damage and impaired object recognition had either general or selective dorsal stream dysfunctions.MethodIn a group of children with early brain damage (n = 48) we evaluated the dorsal stream functioning. To determine whether these patients had an increased risk of a dorsal stream dysfunction we compared the percentage of patients with impaired object recognition, assessed with the L94, with the estimated base rate. Then we evaluated the performance levels on motion perception, visual attention and visuomotor tasks in patients with (n = 18) and without (n = 11) object recognition abnormalities. A general dorsal stream dysfunction was considered present if a patient showed at least one abnormally low score in two out of three additional dorsal stream functions.ResultsSix of the eighteen (33.3%) patients with object recognition problems scored abnormally low on at least two additional dorsal stream functions. This was significantly higher than the base rate (p = 0.01). The difference of 24.1% between the patients with and without object recognition problems was not significant. Of the patients with object recognition problems 72.2% had at least 1 dorsal weakness, whereas this was only the case in 27.3% of patients without object recognition problems. Compared to patients with normal object recognition, patients with object recognition problems scored significantly more abnormally low on motion perception and visual attention (ps = 0.03) but did not differ on visuomotor skills.ConclusionChildren with object recognition problems seem at risk for other dorsal stream dysfunctions, but dysfunctions might be rather specific than general. Multiple functions/aspects should be evaluated in neuropsychological assessment of children at risk.

Recognition of Geometric Images by Linguistic Method

Image recognition is currently one of the fastest-growing areas in applied mathematics. Of the many methods for solving problems in this area, the grammatical (linguistic) method of pattern recognition is the least studied. The essence of the grammar method is to construct appropriate grammar for object classes. In this case, the object recognition problem is related to the language generated by the given grammar. Using the linguistic method, an algorithm and software for recognizing geometric images have been developed. While the development the following tasks were solved. Methods have been developed for describing geometric images (triangles, squares, polygons) and corresponding grammars have been constructed for them so that the chains generated by this grammar represent objects of this class. The problems of constructing given classes of geometric images, as well as constructing a grammar for each class, are solved.
 At the training stage, classes are considered, each of which is described by a finite set of chains. To classify a new image, that is, to determine which class it belongs to, a parsing of the corresponding chain of this image was performed using grammars. Thus, the belonging of the chain to the language born by this grammar was clarified.

A CNN-ELM Classification Model for Automated Tomato Maturity Grading

Tomatoes are popular around the world due to their high nutritional value. Tomatoes are also one of the world’s most widely cultivated and profitable crops. The distribution and marketing of tomatoes depend highly on their quality. Estimating tomato ripeness is an essential step in determining shelf life and quality. With the abundant supply of tomatoes on the market, it is exceedingly difficult to estimate tomato ripeness using human graders. To address this issue and improve tomato quality inspection and sorting, automated tomato maturity classification models based on different features have been developed. However, current methods heavily rely on human-engineered or handcrafted features. Convolutional neural networks have emerged as the preferred technique for general object recognition problems because they can automatically detect and extract valuable features by directly working on input images. This paper proposes a CNN-ELM classification model for automated tomato maturity grading that combines CNNs’ automated feature learning capabilities with the efficiency of extreme learning machines to perform fast and accurate classification even with limited training data. The results showed that the proposed CNN-ELM model had a classification accuracy of 96.67% and an F1-score of 96.67% in identifying six maturity stages from the test data.

Visual feature synthesis with semantic reconstructor for traditional and generalized zero‐shot object classification

Zero-shot learning (ZSL) addresses the novel object recognition problem by leveraging semantic embedding to transfer knowledge from seen categories to unseen categories. Generative ZSL models synthesize the visual features of unseen classes and convert ZSL task into a classical supervised learning problem. These generative ZSL models are trained by using the seen classes. Although promising progress has been achieved in the ZSL and generalized zero-shot learning (GZSL) tasks. The existing approaches still suffer from a strong bias problem between unseen and seen classes, where unseen objects in the target domain tend to be recognized as seen classes in the source domain. To deal with the problem, we propose a novel named semantic consistent Wasserstein generative adversarial network (scWGAN), which uses a semantic reconstructor to reconstruct semantic embeddings from generated visual features by incorporating a novel Semantic Consistent Loss noted L rec . The Semantic Consistent Loss guides our proposed scWGAN to generate visual features that mirror the semantic relationships between seen and unseen classes. We also introduce a visual classifier to constrain visual feature generator. Extensive experiments show that the proposed approach is superior to previous state-of-the-art works under both traditional ZSL and challenging GZSL settings on six popular data sets AWA1, AWA2, CUB, APY, and SUN.

Intelligent System for Estimation of the Spatial Position of Apples Based on YOLOv3 and Real Sense Depth Camera D415

Despite the great possibilities of modern neural network architectures concerning the problems of object detection and recognition, the output of such models is the local (pixel) coordinates of objects bounding boxes in the image and their predicted classes. However, in several practical tasks, it is necessary to obtain more complete information about the object from the image. In particular, for robotic apple picking, it is necessary to clearly understand where and how much to move the grabber. To determine the real position of the apple relative to the source of image registration, it is proposed to use the Intel Real Sense depth camera and aggregate information from its depth and brightness channels. The apples detection is carried out using the YOLOv3 architecture; then, based on the distance to the object and its localization in the image, the relative distances are calculated for all coordinates. In this case, to determine the coordinates of apples, a transition to a symmetric coordinate system takes place by means of simple linear transformations. Estimating the position in a symmetric coordinate system allows estimating not only the magnitude of the shift but also the location of the object relative to the camera. The proposed approach makes it possible to obtain position estimates with high accuracy. The approximate root mean square error is 7–12 mm, depending on the range and axis. As for precision and recall metrics, the first is 100% and the second is 90%.