Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
A novel approach to extract and retrieve furniture items from an image database and online websites which includes multiple furniture items, and then find the similar items from the database. The image could be taken using phone cameras or downloaded from online websites or from shopping malls. A real time application is developed in android phone to find the similar images of furniture taken using...
Object recognition is a versatile capability. Automatic guided tours and augmented reality are just two examples. Humans seem to do it subconsciously — unaware of the extensive processing required for it — while it is a complex task for machines. Methods based on SIFT features have proven to be robust for recognition. However, a prior detection step is required to limit confusion, caused by, e.g.,...
The paper deals with the design and new solutions of application software with the aim to detect and recognize objects sensed by a camera. Objects of the sensed scene were determined and recognized after previous digital processing of data delivered by the camera. To this end the computer vision learning neural-based methods of feature extraction were used. The proposed application software may be...
In this paper, an efficient and automatic method for detection of multiple-objects of interest from images is presented. This method is based on using region similarity measures. The method starts by constructing two knowledge databases in which significant and distinctive textures extracted from both objects of interest and background are respectively represented. The proposed procedure continues...
Object discovery is the task of detecting unknown objects in images. The task is of large interest in many fields of machine vision, ranging from the automatic analysis of web images to interpreting data of a mobile robot or a driver assistant system. Here, we present a new approach for object discovery, based on findings of the human visual system. Proto-objects are detected with a segmentation module,...
Object detection and recognition are fundamental capabilities for a mobile robot. Objects are a powerful representation for a variety of tasks including mobile manipulation and inventory tracking. As a result, object-based world representations have seen a great deal of research interest in the last several years. However, these systems usually assume that object recognition is well-solved: they require...
A novel approach to object recognition based on shape matching of repeatable segments is presented. The motivation is to increase the recognition system robustness in handling problems such as noise corruption at a local level, featureless surfaces, and variations in 3D data sources. Inspired by the detection of repeatable interest points, interest segments were extracted through region growing and...
This paper deals with research in the area of image analysis. Our approach is based on hybrid segmentation and Scale-Invariant Feature Transform (SIFT) method. The main idea is to improve the process of object recognition and their classification into classes by Support Vector Machine (SVM) classifier. The fast and powerful hybrid segmentation algorithm based on Mean Shift and Believe Propagation...
We solve the problem of localizing and tracking household objects using a depth-camera sensor network. We design and implement Kin sight that tracks household objects indirectly -- by tracking human figures, and detecting and recognizing objects from human-object interactions. We devise two novel algorithms: (1) Depth Sweep -- that uses depth information to efficiently extract objects from an image,...
We present compression related methods that can be used within a larger system referred to as a passive assistant. The system receives information from a mobile device, as well as information from an image database such as Google Street View, and employs image processing to provide useful information about a local urban environment to a visually impaired user. The first stage acquires and computes...
Understanding natural images is a difficult task. One method to accomplish that can be, first, segment the image into regions of similar characteristics and then apply some object extraction scheme. Alternatively, extraction of characteristics of the desired objects can be initiated at the beginning. In this paper, we propose a scheme that adopts the former approach. An image is first segmented and...
Identification of vehicles for security reasons has lately attracted much scientific and commercial attention. In certain areas, such as government buildings, army camps or country borders, the vehicles are inspected before allowed to enter. As this inspection needs to be thorough, it is a rather time-consuming process. To address this issue, this paper proposes a combination of distinct computer...
In this paper, we extend our video object recognition system to multiclass object recognition context, dealing with unbalanced data sets and comparing our resuls to state-of-the-art methods. Our approach is based on a Spatio-Temporal data representation, a dedicated kernel design and statistical learning techniques for object recognition. From video tracks made of segmented object regions in the successive...
In this work we address the problem of forest species recognition which is a very challenging task and has several potential applications in the wood industry. The first contribution of this work is a database composed of 22 different species of the Brazilian flora that has been carefully labeled by expert in wood anatomy. In addition, in this work we demonstrate through a series of comprehensive...
In this paper, we propose a new theme-based CRF model and investigate its performance on class based pixel-wise segmentation of images. By including the theme of an image, we also propose a new texture-environment potential to represent texture environment of a pixel, which alone gives satisfactory recognition results. The pixel-wise segmentation accuracy is remarkably improved by introducing texture...
Visual object recognition is one of the most challenging problems in computer vision, due to both inter-class and intra-class variations. The local appearance-based features, especially SIFT, have gained a big success in such a task because of their great discriminative power. In this paper, we propose to adopt two different kinds of feature to characterize different aspects of object. One is the...
This paper addresses the problem of recognizing shadows from monochromatic natural images. Without chromatic information, shadow classification is very challenging because the invariant color cues are unavailable. Natural scenes make this problem even harder because of ambiguity from many near black objects. We propose to use both shadow-variant and shadow-invariant cues from illumination, textural...
We present a framework to recognize objects in images based on their silhouettes. In previous work we developed translation and rotation invariant classification algorithms for textures based on Fourier transforms in the polar space followed by dimensionality reduction. Here we present a new approach to recognizing shapes by following a similar classification step with a "soft" retrieval...
We propose in this paper a new evaluation metric that enables to quantify the quality of an image interpretation result. This metric takes into account the a priori knowledge used by the interpretation algorithm and the ground truth associated with the original image. We combine two metrics that evaluate the localization and recognition results of each detected object. We show that the proposed metric...
We present a novel method for recognizing an object in an image using full pixel matching between a reference image and an input image without advance segmentation of the image. A method called two-dimensional continuous dynamic programming (2DCDP) is adopted to optimally calculate the accumulated local distances of all corresponding pixels in nonlinearly matched areas in an input image and a reference...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.