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An accurate and reliable image based fruit detection system is critical for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples. Ablation studies are presented to better understand the practical...
For robots that have the capability to interact with the physical environment through their end effectors, understanding the surrounding scenes is not merely a task of image classification or object recognition. To perform actual tasks, it is critical for the robot to have a functional understanding of the visual scene. Here, we address the problem of localization and recognition of functional areas...
The purpose of this study is learning and classification of video activities using video color and motion information. The video activity labeling is important for many applications such as video content modeling, indexing, and quick access to content. In this study video activity recognition is performed by deep learning. In order to learn visual features of video, Convolutional Neural Network (CNN)...
Images on the Internet and in multimedia systems are rising successively. There are different research works on visual information and automatic analysis of images. Image memorability is a new task in computer vision. Actually, the human brain processes simultaneously millions of images and other information from multiple sources. Among these various images and information some of them are more memorable...
A new spatiotemporal saliency detection model is presented in this paper. Instead of previous works which combine the image saliency in the spatial domain with motion cues to build their video saliency model, we propose to apply the pattern mining algorithm. From initial saliency maps computed in spatial and temporal domains, discriminative saliency patterns can be recognized and used to detect pertinent...
Shadows cast by the moving objects may lead to several errors in the process of moving object detection and tracking. Since the shadows are connected to the object and move along with it, false object detection may occur in addition to the problem of false connectivity and loss of background texture. Hence shadow detection is an important preprocessing step for a robust visual surveillance system...
Object categorization is an interesting application in computer vision. To develop an efficient system for this purpose, finding an appropriate classifier in conjunction with a suitable feature is essential. Most classifiers and features have one or more parameters to be tuned through cross validation. In this paper, we examined a number of classifiers with several feature descriptors and advise an...
The technology world for visually impaired people has evolved over the past few years, making their day-to-day life more functional. However, there are still gaps such as in the area of aesthetics and visual image that need to be more explored. Thus, this article describes the first validation in the development of a Web platform in aid of the combination of clothing for blindness people. This project...
Feature fusion methods have been demonstrated to be effective for many computer vision based applications. These methods generally use multiple hand-crafted features. However, in recent days, features extracted through transfer leaning procedures have been proved to be robust than the hand-crafted features in myriad applications, such as object classification and recognition. The transfer learning...
Human's everyday environment is an open environment in which objects with new shapes, colors or textures frequently appear. Enabling robots to deal with such environments and to manipulate those objects raises a difficult challenge: how to recognize an object? How to distinguish it from the background? An approach is proposed here to allow the robot to find this segmentation on its own. It relies...
In this paper, we will propose a method is to establishing 3D images and tracking objects in a plane space. The system in this paper will use automated. At first, grabbing dynamic environment images that from two CCDs, digitalized them by the image-grabber and then using digital image processing techniques to become 3D environment images. Next step is to do objects recognition and locate them respectively...
In this paper, we propose an unsupervised objective measure for quality evaluation of single object segmentation in images. Objectness as an essential attribute of objects is treated as a main feature to measure object segmentation quality. In addition, the prior information about the object quantity is integrated into the proposed measure. Experimental results show that our measure can conform well...
Due to the advancement in multimedia technology, the images and videos play a major role in day today life. How the humans are looking into the image? The computational models of visual attention used in many of the computer vision tasks such as image segmentation, object recognition, image understanding, etc. The proposed method aims to construct visual saliency model with the help of the center...
Single materials have colors which form straight lines in RGB space. However, in severe shadow cases, those lines do not intersect the origin, which is inconsistent with the description of most literature. This paper is concerned with the detection and correction of the offset between the intersection and origin. First, we analyze the reason for forming that offset via an optical imaging model. Second,...
A variety of methods have been proposed for object level saliency detection, which is useful for many content-based computer vision applications. Unlike most previous work that integrate multiple low level cues to compute the saliency map, this paper presents a novel hierarchical optimization model. First, we compute a rough saliency map using HS method, and then, boundary and foreground seeds are...
Vision is vital to decision making, as humans naturally trust their eyes to enhance situation awareness. Yet the modern age has overwhelmed humans with massive amounts of visual information, which is problematic in time sensitive and mission critical situations, such as emergency management and disaster response. More efficient search and retrieval systems address some of these issues, which is why...
While the abundance of visual content available on the Internet, and the easy access to such content by all users allows us to find relevant content quickly, it also poses challenges. For example, if a parent wants to restrict the visual content which their child can see, this content needs to either be automatically tagged as offensive or not, or a computer vision algorithm needs to be trained to...
Since the introduction of deep convolutional neural networks (CNNs), object detection in imagery has witnessed substantial breakthroughs in state-of-the-art performance. The defense community utilizes overhead image sensors that acquire large field-of-view aerial imagery in various bands of the electromagnetic spectrum, which is then exploited for various applications, including the detection and...
Automatic, fast and efficient estimation of some physical characteristics such as mass and volume in agricultural products, improves some postharvest processes such as sorting and storage. This document presents a computer vision system to automatically estimate these characteristics in passion fruit. The visible aspects in digital images: color, texture, size and shape are correlated with the actual...
Recently, mobile devices have become equipped with sophisticated hardware components such as a heterogeneous multi-core SoC that consists of a CPU, GPU, and DSP. This provides opportunities to realize computationally-intensive computer vision applications using General Purpose GPU (GPGPU) programming tools such as Open Graphics Library for Embedded System (OpenGL ES) and Open Computing Language (OpenCL)...
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