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.
Place recognition is widely used in the loop closure detection in SLAM. The current approach to place recognition is based on RGB images, but there are relatively few place recognition studies using a point cloud. This study presents the place recognition method based on the surface graph. The proposed method clusters the surfaces in the point cloud and recognizes a place through a surface descriptor...
The classification recognition performance is a hot study in the field of remote sensing image. In this paper, texture feature, shape feature, radiation intensity of remote sensing image information were used to initial terrain classification. Then an improved fuzzy c-means algorithm was applied on classification, and it included optimization of determine clustering center, got the number of clustering...
While Optical Character Recognition (OCR) can be considered as a solved problem, text detection and recognition in real scene images is still extremely challenging and remains an open problem. Due to the wide variety of text appearances in real scenes, such as variations in font, size, color, orientation, partial occlusions, different distortions and illumination conditions, current results of both...
Scene recognition applications on mobile devices receive increasing attentions in recent years. Due to mobile users' real-time requirement, an accurate and efficient scene recognition system is urgent for mobile applications. In this paper, we propose a novel discriminative codeword selection method by using the ensemble extreme learning machine (ELM) algorithm for fast and accurate scene recognition...
This research is addresses to determine the dominant species that located in the overlapped clusters produced by the Kohonen Self-Organizing Map (KSOM). Before, KSOM algorithm able to cluster the tropical wood species data set effectively and accurately according to the wood features, which is wood pores sizes. Unfortunately, there are seven overlapped clusters in the clustering result and this is...
In this paper, we present a scene recognition framework, which could process the images and recognize the scene in the images. We demonstrate and evaluate the performance of our system on a dataset of Oxford typical landmarks. In this paper, we put forward a novel method of local k-meriod for building a vocabulary and introduce a novel quantization method of soft-assignment based on the Gaussian mixture...
In this paper, a dollar bill denomination recognition algorithm based on local texture feature is proposed. this paper proposes a local texture feature dollar denomination recognition algorithm, this algorithm first use the between-cluster variance method about the dollar's local image binarization to enhance the effect of differences, and then through the cross algorithm to extract the local texture...
Scene recognition is an important research topic in robotics and computer vision. Even though scene recognition is a problem that has been studied in depth, indoor scene categorization has had a slow progress. Indoor scene recognition is a challenging problem due to the severe high intra-class variability, mainly due to the intrinsic variety of objects that may be present, and inter-class similarities...
This paper proposes a clustering based image segmentation approach for elephant recognition. Appreciable recognition rate was achieved by k-means clustering technique followed by feature extraction and K nearest neighbour (K-NN) classifier. The k-means algorithm employs the concept of fitness and belongingness to provide a more adaptive andbetterclustering process as compared to several conventional...
In this paper, we propose a training strategy for an automatic face recognition system. Our strategy is based on cascade reduction of data dimensionality using LBP and PCA algorithms. This method is able to achieve higher recognition accuracy in comparison with simple LBP or PCA and it is suitable in the case of adding a new user to the face recognition system. We provide a comparative study of our...
Image recognition is one of the fundamental problems in multimedia analysis. Typically in the training database, there will be more than one image for each object, however most existing bag-of-features based approaches treat them independently and completely ignore the feature correspondence relationship among them. As a result, features corresponding to the same physical point may be clustered into...
A method for the off-line recognition of Tamil handwriting characters based on local feature extraction is investigated. In the proposed method each pre-processed character is represented by a set of local SIFT feature vectors. From a large set of SIFT descriptors, the key idea is to create a codebook for each character using K-means clustering algorithm. K-means is an optimisation algorithm but this...
Several methods have been developed in order to recognize a location from an image. Early methods use appearance based matching, but they usually failed to handle occlusion. Recently, some methods using feature based matching have been developed. They are more robust and faster than the appearance based, but processing time and memory usage are still aspects which can be improved further.
We approach the shape recognition domain in this paper. After an introduction in the image shape analysis domain, we describe a shape feature extraction technique using moment-based measures which are invariant to geometric transforms. Then, an automatic unsupervised feature vector classification approach is proposed. It is based on a sequence of hierarchical agglomerative region-growing clustering...
Given a large collection of images, very few of which have labels, how can we guess the labels of the remaining majority, and how can we spot those images that need brand new labels, different from the existing ones? Current automatic labeling techniques usually scale super linearly with the data size, and/or they fail when only a tiny amount of labeled data is provided. In this paper, we propose...
In this paper, an approach for human action recognition is presented based on adaptive bag-of-words features. Bag-of-words techniques employ a codebook to describe a human action. For successful recognition, most action recognition systems currently require the optimal codebook size to be determined, as well as all instances of human actions to be available for computing the features. These requirements...
In this paper two novel methods are proposed for recognizing B-mode ultrasound (US) imaging of common carotid artery (CCA) in longitude. In the single frame method, the US is segmented by k-means fuzzy clustering algorithm. Then a series points are extracted based on geometric features. Thirdly, the feature points are selected according to our new cluster method. Then curve fitting is applied by feature...
Gait is an emerging biometric technology. It enables biometric at a distance. The first step in gait recognition is the silhouette extraction. However, most of the work involves indoor controlled environment or well-exposed outdoor scenes. Furthermore, they are all applied to perspective-like pictures. This paper addresses a method for silhouette extraction on catadioptric images in indoor and uncontrolled...
We propose an automatic moment-based image recognition technique in this paper. The problem to be solved consists of classifying the images from a set, using the content similarity. In the feature extraction stage, we compute a set of feature vectors using area moments. An automatic unsupervised feature vector classification approach is proposed next. It uses a hierarchical agglomerative clustering...
This paper deals with the issue of gradual classification of a multivariate sequence where the number of candidate time-series generators is significantly high. It proposes a prediction scheme that consists of two components: a hierarchical structure which organizes the time-series models and a decision maker tool that assigns and evolves a respective hierarchy of probabilities; the latter expresses...
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.