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Visual recognition and vision based retrieval of objects from large databases are tasks with a wide spectrum of potential applications. In this paper we propose a novel recognition method from video sequences suitable for retrieval from databases acquired in highly unconstrained conditions e.g. using a mobile consumer-level device such as a phone. On the lowest level, we represent each sequence as...
Ihis paper proposes a new object recognition algorithm using robotic context information for humanoid robots. For more robust object recognition for less textured objects, we combine shape-based interest points and local appearance-based descriptors computed in a neighborhood around each detected interest point. The combination is used as a basic feature for matching, and candidate feature correspondences...
Derived from ecological psychology, the term ‘affordance’ refers to the functional classification of objects. It simply means the set of actions a subject (i.e. humans and anthropomorphic agents) can possibly perform with an object. There are several paradigms in the researches regarding the approaches of affordance detection. These approaches include considering other contexts like the subject, ambient...
In any visual object recognition system, the classification accuracy will likely determine the usefulness of the system as a whole. In many real-world applications, it is also important to be able to recognize a large number of diverse objects for the system to be robust enough to handle the sort of tasks that the human visual system handles on an average day. These objectives are often at odds with...
Object recognition from remote sensing systems is a task of immense interest. With the vast deployment of aerial vehicles and space borne sensors for a wide variety of purposes, it is critical to have robust image processing techniques to analyze massive streams of collected data. Herein, we explore the utility of a feature descriptor learning framework, called improved Evolution-COnstructed (iECO)...
In this study we evaluate the potential of local binary descriptors for automatic sorting in an industrial context. This problem is different from that of retrieval for human handling as we need to identify the one correct class, rather than finding all the similar classes. We have looked at classes of objects that need to be identified by their cover or label, rather than their shape. Challenges...
We present Marvin, a system that can search physical objects using a mobile or wearable device. It integrates HOG-based object recognition, SURF-based localization information, automatic speech recognition, and user feedback information with a probabilistic model to recognize the “object of interest” at high accuracy and at interactive speeds. Once the object of interest is recognized, the information...
In this paper, we present a novel object matching approach using the method considering both the similarity on regions and structure in its feature space. The previous works [1], [2] and [3] show that it's possible to formulate the object matching problem as a linear programming problem. However, it remains an open problem how to better use the feature similarity and structure similarity at a same...
Nowadays leaf image classification is very useful for both botanists and ordinary users since advanced imaging devices such as smart phones make it ever easier to capture leaf images for various tasks such as retrieval and classification. Most of existing approaches mainly utilize global shape features. In this paper, we propose to improve leaf image classification by taking both global features and...
Informative image representations are important in achieving state-of-the-art performance in object recognition tasks. Among feature learning algorithms that are used to develop image representations, restricted Boltzmann machines (RBMs) have good expressive power and build effective representations. However, the difficulty of training RBMs has been a barrier to their wide use. To address this difficulty,...
Recognizing and localizing queried objects in range images plays an important role for robotic manipulation and navigation. Even though it has been steadily studied, it is still a challenging task for scenes with occlusion and clutter
In this paper, we present a new method to recognize object class based on local appearance features and context information. At first, local descriptors of object class appearance are clustered, then part classifiers are trained to select the most distinctive image patches and visual context information around them are extracted to keep the robustness to object occlusion and background clutter. Finally...
This paper proposes a new method to detect doors using context-based object recognition. Particularly, in order to improve the efficiency of object recognition, we utilize robotic context such as the robot's viewpoint and the average height of doorknobs. The robotic context is used to make a region of interest in a captured image which reduces both the computational time and false-postive rate in...
Place and object recognition are two fundamental problems for mobile robot to understand its surroundings. In the field of computer vision it has been acknowledged that context plays an important role in image parsing, but in most of the researches contextual information is only used in one direction and little attention is paid to the relative pose context between objects and local features. We observe,...
In this paper the relative-chord context is proposed for shape description in object recognition. The relative-chord is introduced to obtain the affine-invariant feature. The intervals of parallel chords are normalized by vertical direction-chord. Experimental result based on the Columbia University Coil-100 3D database demonstrates the feasibility of the relative-chord context methodology and also...
We present a new method to learn the model based on object parts extraction and grammar which can be applied to classification and recognition. Our approach is invariant to the scale and rotation of the objects. We use Structural Context feature to detect object parts. It is done comparing SC histograms of the model and image. We extract oriented triplets from centers of detected parts. We define...
We have established a multi-walker recognition/tracking testbed based on low-cost pyroelectrc sensor network (PSN). In order to identify a region of interest (Rol) in the monitoring area for the detection of any interesting mobile targets, we propose to use Bayesian machine learning and binary signal projection to extract the statistical contextual features from real-time, high-dimensional PSN data...
Accurate, efficient and robust location recognition is a fundamental task for any mobile robot. This paper presents a new approach using visual features to efficiently represent a series of locations along a path in an indoor environment. In the training stage, local features which are detected across multiple images from a single tour are combined to represent a real-world landmark, modelled by the...
Autonomous Surveillance is an important term in order to produce pervasive, ubiquitous, homenet, telemetics and other application purposes. However, many surveillance systems are annoyed with some environmental hazards like illumination and others. This paper presents a novel method for non-intrusive biometric vision system for the surveillance having the prior knowledge about environment. As an environment...
Recognizing human actions is of vital interest in video surveillance or ambient assisted living. We consider an action as a sequence of body poses which are themselves a linear combination of body parts. In an offline procedure, nonnegative tensor factorization is used to extract basis images that represent body parts. The weighting coefficients are obtained by filtering a frame with the set of basis...
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