The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, we propose a data-driven foreground object detection technique which can detect foreground objects from a moving camera. We propose to build a data-driven consensus foreground object template (CFOT) and then detect the foreground object region in each frame. The proposed foreground object detection technique is equipped with the following functions: (1) the ability to detect the foreground...
The Local Binary Pattern (LBP) operator is a computationally efficient yet powerful feature for analyzing local texture structures. While the LBP operator has been successfully applied to tasks as diverse as texture classification, texture segmentation, face recognition and facial expression recognition, etc., it has been rarely used in the domain of Visual Object Classes (VOC) recognition mainly...
In this paper we present a scene exploration method for the identification of interest regions in unknown indoor environments and the position estimation of the objects located in those regions. Our method consists of two stages: First, we generate a saliency map of the scene based on the spectral residual of three color channels and interest points are detected in this map. Second, we propose and...
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...
We propose a novel method for the refinement of Maximally Stable Extremal Region (MSER) boundaries to sub-pixel precision by taking into account the intensity function in the 2 × 2 neighborhood of the contour points. The proposed method improves the repeatability and precision of Local Affine Frames (LAFs) constructed on extremal regions. Additionally, we propose a novel method for detection of local...
We propose a method for multi-object segmentation in a projection plane. Our algorithm requires a stereo camera system called Subtraction Stereo, which extracts foreground information with a fixed stereo camera. The main contribution of this paper is how the image sequences that include partial occlusion of the foreground objects can be accurately segmented using mean shift clustering in real-time...
Ghosting artifact in the field of image stitching is a common problem and the elimination of it is not an easy task. In this paper, we propose an intuitive technique according to a stitching line based on a novel energy map which is essentially a combination of gradient map which indicates the presence of structures and prominence map which determines the attractiveness of a region. We consider a...
We propose a bag-of-hierarchical-co-occurrence features method incorporating hierarchical structures for image classification. Local co-occurrences of visual words effectively characterize the spatial alignment of objects' components. The visual words are hierarchically constructed in the feature space, which helps us to extract higher-level words and to avoid quantization error in assigning the words...
This paper presents a novel local feature descriptor, the Local Directional Pattern (LDP), for describing local image feature. A LDP feature is obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Each bit of code sequence is determined by considering a local neighborhood hence becomes robust in noisy...
Over the last several years, a new probabilistic representation for 3-d volumetric modeling has been developed. The main purpose of the model is to detect deviations from the normal appearance and geometry of the scene, i.e. change detection. In this paper, the model is utilized to characterize changes in the scene as vehicles. In the training stage, a compositional part hierarchy is learned to represent...
Generic object recognition by a computer is strongly required in various fields like robot vision and image retrieval in recent years. Conventional methods use Conditional Random Field (CRF) that recognizes the class of each region using the features extracted from the local regions and the class co-occurrence between the adjoining regions. However, there is a problem that the discriminative ability...
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...
Road sign identification in images is an important issue, in particular for vehicle safety applications. It is usually tackled in three stages: detection, recognition and tracking, and evaluated as a whole. To progress towards better algorithms, we focus in this paper on the first stage of the process, namely road sign detection. More specifically, we compare, on the same ground-truth image database,...
This papers presents a weakly supervised method to simultaneously address object localization and recognition problems. Unlike prior work using exhaustive search methods such as sliding windows, we propose to learn category and image-specific visual words in image collections by extracting discriminating feature information via two different types of support vector machines: the standard L2-regularized...
Potential energy theory is a new method to extract target feature and recognize the target in image processing. Potential energy theory, when used for image processing, is faster, more efficient, and more accurate in target feature extraction. The data are easy to extract, and occupy small storage space, what's more, they are simple to be computed with distinct features. Potential energy theory can...
The proposed novel method categorizes candidate boundaries into visually-prominent and non-prominent boundaries, considering local intensity cues of multiple color channels and pixel-prominence-values measured as a function of proximity-influence of other contours. The results of the method with and without incorporating a wavelet transform are compared for images of different characteristics, types...
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...
Loops are an important part of classic programming techniques, but are rarely used in genetic programming. This paper presents a method of using unrestricted, i.e. nesting, loops to evolve programs for image classification tasks. Contrary to many other classification methods where pre-extracted features are typically used, we perform calculations on image regions determined by the loops. Since the...
Feature matching plays an important role in many applications, including 3D reconstruction, object recognition and video understanding. Point matching has made great progress recently, while it has made little progress in the fields of line and curve matching. By computing statistics of point descriptors constructed at each edge points, this paper develops a novel method for extending point descriptors...
We propose a novel method to address object localization in a weakly supervised framework. Unlike prior work using exhaustive search methods such as sliding windows, we advocate the use of visual attention maps which are constructed by class-specific visual words. Based on dense SIFT descriptors, these visual words are selected by support vector machines and feature ranking techniques. Therefore,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.