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Recent advances in drone visual sensors and integration of complex vision algorithms, facilitate further potential, entirely disrupting in a positive way their applications and capabilities. In particular, real-time object detection, usually the initial necessary step in multiple computer vision and image processing applications, has been gaining momentum in drone- based applications. Whilst heavily...
Despite significant recent progress, the best available computer vision algorithms still lag far behind human capabilities, even for recognizing individual discrete objects under various poses, illuminations, and backgrounds. Here we present a new approach to using object pose information to improve deep network learning. While existing large-scale datasets, e.g. ImageNet, do not have pose information,...
This paper presents an object recognition modular system implementation that allows a humanoid robot to recognize and manipulate objects in a service robotics context. The system consists of three modules: I) Object recognition, II) Object manipulation and III) Humanoid-robot interaction. The Object recognition uses A-KAZE descriptor and Growing Cell Structure (GCS) artificial neural network (ANN)...
In this paper, we generated an activity recognition model using an ANN and trained it using Backpropagation learning. We considered a sandwich making scenario and identified the hand-motion-based activities of reaching, sprinkling, spreading and cutting. The contribution of this paper is twofold: First, given the fact that many image processing steps like feature identification are computation intensive...
This paper addresses the problem of object counting, which is to estimate the number of objects of interest from an input observation. We formalize the problem as a posterior inference of the count by introducing a particular type of Gaussian mixture for the input observation, whose mixture indexes correspond to the count. Unlike existing approaches in image analysis, which typically perform explicit...
With the advent of commodity autonomous mobiles, it is becoming increasingly prevalent to recognize under extreme conditions such as night, erratic illumination conditions. This need has caused the approaches using multi-modal sensors, which could be complementary to each other. The choice for the thermal camera provides a rich source of temperature information, less affected by changing illumination...
Distributed object recognition is a significantly fast-growing research area, mainly motivated by the emergence of high performance cameras and their integration with modern wireless sensor network technologies. In wireless distributed object recognition, the bandwidth is limited and it is desirable to avoid transmitting redundant visual features from multiple cameras to the base station. In this...
In this paper, we demonstrate how automatic grasp selection can be achieved by placing a camera in the palm of a prosthetic hand and training a convolutional neural network on images of objects with corresponding grasp labels. Our labeled dataset is built from common graspable objects curated from the ImageNet dataset and from images captured from our own camera that is placed in the hand. We achieve...
A recognition system for three-dimensional objects is proposed in this paper. This system consists of line laser projectors and a web camera in order to collect depth and curvature information of objects, leading to a compact and cost-effective system compared to light coding-based systems such as Microsoft Kinect. Curvature information of objects can be obtained through calculation of Fourier components...
Today most recognition pipelines are trained at an off-line stage, providing systems with pre-segmented images and predefined objects, or at an on-line stage, which requires a human supervisor to tediously control the learning. Self-Supervised on-line training of recognition pipelines without human intervention is a highly desirable goal, as it allows systems to learn unknown, environment specific...
Monitoring marine object is important for understanding the marine ecosystem and evaluating impacts on different environmental changes. One prerequisite of monitoring is to identify targets of interest. Traditionally, the target objects are recognized by trained scientists through towed nets and human observation, which cause much cost and risk to operators and creatures. In comparison, a noninvasive...
The Internet of Things (IoTs) has triggered rapid advances in sensors, surveillance devices, wearables and body area networks with advanced Human-Computer Interfaces (HCI). One such application area is the adoption of Body Worn Cameras (BWCs) by law enforcement officials. The need to be ‘always-on’ puts heavy constraints on battery usage in these camera front-ends, thus limiting their widespread adoption...
Visual Servoing for a robotic hand is still a difficult problem to solve and is the topic of current research. To recognize an object of interest and calculating its orientation and location without extensive training is another far-fetched problem faced by researchers in this field. In this paper, object recognition based on scale-invariant feature based transform (SIFT) is done, which is used to...
Recently, different smart glasses solutions have been proposed on the market. The rapid development of this wearable technology has led to several research projects related to applications of smart glasses in healthcare. In this paper we propose a general architecture of the system enabling data integration for the recognized person. In the proposed system smart glasses integrates data obtained for...
This paper reports on the results obtained when using Radial Basis Neural Networks to classify different objects using only colour data extracted from images captured under different lighting conditions. Each network is trained with data from a single image and then tested with data from images containing the same collection of objects, but captured under different lighting conditions. The ability...
In this paper we apply light field reconstruction and rendering of object views to the problem of automatic generation of training material for a statistical object recognition system. The advantages of using a light field instead of real images are shown. We evaluate with respect to the error rate of the classifier, whether the reconstructed light field can be applied to the training step. We also...
Conventional supervised object recognition methods have been investigated for many years. Despite their successes, there are still two suffering limitations: (1) various information of an object is represented by artificial features only derived from RGB images, (2) lots of manually labeled data is required by supervised learning. To address those limitations, we propose a new semi-supervised learning...
Visual-to-auditory Sensory Substitution Devices (SSDs) are non-invasive sensory aids that provide visual information to the blind via their functioning senses, such as audition. For years SSDs have been confined to laboratory settings, but we believe the time has come to use them also for their original purpose of real-world practical visual rehabilitation. Here we demonstrate this potential by presenting...
This paper presents a method for the continuous segmentation of dynamic objects using only a vehicle mounted monocular camera without any prior knowledge of the object's appearance. Prior work in online static/dynamic segmentation [1] is extended to identify multiple instances of dynamic objects by introducing an unsupervised motion clustering step. These clusters are then used to update a multi-class...
The ubiquity of smartphones with high quality cameras and fast network connections will spawn many new applications. One of these is visual object recognition, an emerging smartphone feature which could play roles in high-street shopping, price comparisons and similar uses. There are also potential roles for such technology in assistive applications, such as for people who have visual impairment....
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