Nouf Abdullah Almujally¹, Bisma Riaz Chughtai², Naif Al Mudawi³, Abdulwahab Alazeb³, Asaad Algarni⁴, Hamdan A. Alzahrani⁵, Jeongmin Park^6,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1563-1580, 2024, DOI:10.32604/cmc.2024.049333 - 18 July 2024

Abstract The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex scenes. Various technologies, such as augmented reality-driven scene integration, robotic navigation, autonomous driving, and guided tour systems, heavily rely on this type of scene comprehension. This paper presents a novel segmentation approach based on the UNet network model, aimed at recognizing multiple objects within an image. The methodology begins with the acquisition and preprocessing of the image, followed by segmentation using the fine-tuned UNet architecture. Afterward, we use an annotation tool to accurately label… More >

Shu-Yin Chiang^*, Ting-Yu Lin

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1757-1773, 2024, DOI:10.32604/cmc.2024.049477 - 15 May 2024

Abstract This research focuses on addressing the challenges associated with image detection in low-light environments, particularly by applying artificial intelligence techniques to machine vision and object recognition systems. The primary goal is to tackle issues related to recognizing objects with low brightness levels. In this study, the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images. The detection scenarios are categorized into normal brightness and low brightness situations. When the system determines a normal brightness environment, normal brightness images are recognized using deep learning methods. In low-brightness situations,… More >

Muhammad Waqas Ahmed¹, Nouf Abdullah Almujally², Abdulwahab Alazeb³, Asaad Algarni⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3315-3331, 2024, DOI:10.32604/cmc.2024.046501 - 15 May 2024

Abstract Advances in machine vision systems have revolutionized applications such as autonomous driving, robotic navigation, and augmented reality. Despite substantial progress, challenges persist, including dynamic backgrounds, occlusion, and limited labeled data. To address these challenges, we introduce a comprehensive methodology to enhance image classification and object detection accuracy. The proposed approach involves the integration of multiple methods in a complementary way. The process commences with the application of Gaussian filters to mitigate the impact of noise interference. These images are then processed for segmentation using Fuzzy C-Means segmentation in parallel with saliency mapping techniques to find… More >

R. Meenakshi¹, R. Ponnusamy^1,*, Saleh Alghamdi², Osama Ibrahim Khalaf³, Youseef Alotaibi⁴

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3473-3495, 2022, DOI:10.32604/cmc.2022.028540 - 16 June 2022

Abstract In 2017, it was estimated that the number of persons of all ages visually affected would be two hundred and eighty-five million, of which thirty-nine million are blind. There are several innovative technical solutions available to facilitate the movement of these people. The next big challenge for technical people is to give cost-effective solutions. One of the challenges for people with visual impairments is navigating safely, recognizing obstacles, and moving freely between locations in unfamiliar environments. A new mobile application solution is developed, and the application can be installed in android mobile. The application will More >

Ajisha Mathias¹, Samiappan Dhanalakshmi^1,*, R. Kumar¹, R. Narayanamoorthi²

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5251-5267, 2022, DOI:10.32604/cmc.2022.021168 - 11 October 2021

Abstract Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in… More >

Huijin Park, Soobin Ou, Jongwoo Lee^*

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 247-258, 2021, DOI:10.32604/iasc.2021.015274 - 12 May 2021

Abstract Blind people are highly exposed to numerous dangers when they walk alone outside as they cannot obtain sufficient information about their surroundings. While proceeding along a crosswalk, acoustic signals are played, though such signals are often faulty or difficult to hear. The bollards can also be dangerous if they are not made with flexible materials or are located improperly. Therefore, since the blind cannot detect proper information about these obstacles while walking, their environment can prove to be dangerous. In this paper, we propose an object recognition system that allows the blind to walk safely… More >

Shilpa Sethi¹, Mamta Kathuria^1,*, Trilok Kaushik²

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 389-409, 2021, DOI:10.32604/cmes.2021.014478 - 19 April 2021

Abstract Effective strategies to control COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy, with the brim-full horizon yet to unfold. In the absence of effective antiviral and limited medical resources, many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources. Wearing mask is among the non-pharmaceutical intervention measures that can be used as barrier to primary route of SARS-CoV2 droplets expelled by presymptomatic or asymptomatic individuals. Regardless of discourse on medical resources and diversities in masks, all countries are mandating coverings over… More >

Jinxing Niu^1,*, Qingsheng Hu¹, Yi Niu¹, Tao Zhang¹, Sunil Kumar Jha²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2221-2229, 2021, DOI:10.32604/cmc.2021.017073 - 13 April 2021

Abstract Object recognition and location has always been one of the research hotspots in machine vision. It is of great value and significance to the development and application of current service robots, industrial automation, unmanned driving and other fields. In order to realize the real-time recognition and location of indoor scene objects, this article proposes an improved YOLOv3 neural network model, which combines densely connected networks and residual networks to construct a new YOLOv3 backbone network, which is applied to the detection and recognition of objects in indoor scenes. In this article, RealSense D415 RGB-D camera… More >

Ying Cui¹, Shi Qiu^2,*, Tong Li³

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 417-428, 2021, DOI:10.32604/iasc.2021.016802 - 01 April 2021

Abstract It is difficult to recognize a target object from foggy images. Aiming at solving this problem, a new algorithm is thereby proposed. Fog concentration estimation is the prerequisite for image defogging. Due to the uncertainty of fog distribution, a fog concentration estimation model is accordingly proposed. Establish the brightness and gradient model in the spatial domain, and establish the FFT model in the frequency domain. Also, a multiple branch network framework is established to realize the qualitative estimation of the fog concentration in images based on comprehensive analysis from spatial and frequency domain levels. In More >

Maria João M. Vasconcelos¹, João Manuel R. S. Tavares¹

CMES-Computer Modeling in Engineering & Sciences, Vol.36, No.3, pp. 213-242, 2008, DOI:10.3970/cmes.2008.036.213

Abstract In this work we developed methods to automatically extract significant points of objects like hand palms and faces represented in images that can be used to build Point Distribution Models automatically. These models are further used to segment the modelled objects in new images, through the use of Active Shape Models or Active Appearance Models. These models showed to be efficient in the segmentation of objects, but had as drawback the fact that the labelling of the landmark points was usually manually made and consequently time consuming. Thus, in this paper we describe some methods More >