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.
Facial wearable items recognition refers to the judgment of whether the face in a face image wears a facial item, such as an eyeglass or a mask, which belongs to the category of face attribute analysis. In recent years, face attribute analysis mainly focuses on the study of gender, age, expression and other aspects, ignoring the study of facial wearable items recognition. However, this technology...
Deep convolutional neutral networks have achieved great success on image recognition tasks. Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable. We present deep feature flow, a fast and accurate framework for video recognition. It runs the expensive convolutional sub-network only on sparse key frames and...
There are numerous potential applications for the Internet of Things (IOT) at the present stage. In the topic of image processing, the gender recognition usually adopts face information to identify the gender of a person. Therefore, if the input image loses face information, it will result in the wrong identification result. In this paper, we proposed a multiple-attributes (MA) recognition method...
This paper describes a web-based system for page segmentation and text recognition of historical documents. The system is organised following a pipeline of 4 steps : 1) digitisation, 2) preprocessing, 3) textline extraction, and 4) handwritten text recognition based on hidden Markov models. In this study we used to evaluate the system the “Statuti del Doge Tiepolo”, a 14th century manuscript written...
In this paper we propose a new method for scene representation and recognition based on the concept of Region Subspaces. Each image is pre-segmented into semantically meaningful regions and local features are extracted at different scales from each such region. The Region Subspaces are the low-dimensional linear subspaces calculated from the set of local features inside each region. We also define...
The technological advancement and sophistication in cameras and gadgets prompt researchers to have focus on image analysis and text understanding. The deep learning techniques demonstrated well to assess the potential for classifying text from natural scene images as reported in recent years. There are variety of deep learning approaches that prospects the detection and recognition of text, effectively...
Automatic identification and recognition of medicinal plant species in environments such as forests, mountains and dense regions is necessary to know about their existence. In recent years, plant species recognition is carried out based on the shape, geometry and texture of various plant parts such as leaves, stem, flowers etc. Flower based plant species identification systems are widely used. While...
This paper describes an automatic approach for iris segmentation and recognition with focus on twins. The technique entails localizing and segmenting the iris, followed by iris normalization and obtaining distinctive features. Lastly, iris templates are matched to realize one to one and one to many recognition in twins. Further, effect of various template sizes on the accuracy and memory requirement...
The detection and matching of logos have widely attention for the document images retrieval projects. In this paper an automatic effective logo matching has been developed and applied to different Arabic documents. The proposed technique based on matching between logos by using two types of significant features. The first is region based feature which represent the global features of an object. This...
We propose to leverage concept-level representations for complex event recognition in photographs given limited training examples. We introduce a novel framework to discover event concept attributes from the web and use that to extract semantic features from images and classify them into social event categories with few training examples. Discovered concepts include a variety of objects, scenes, actions...
The main objective of presenting this context is to identify and verify the quality of the seed for future fertilization in the field of agriculture. This research paper proposes a novel image processing technique that includes two phases depicts an optimized selection of feature extraction and classification algorithm that enhances the quality, exactness of the seed variety and realization of the...
There are countless plant species available globally. To manage massive content, development of a fast and effective categorization methods has turned into a territory of dynamic research. As trees and plants are very important to ecology, accurate Identification and classification becomes necessary. Classification procedure is carried out through number of sub procedures. An identification or Classification...
To recognize accessible regions successfully and accurately is an important part of mobile robot navigation in outdoor environment. This thesis studies that the modified Mean Shift algorithm is applied for region partition on outdoor images obtained by robot vision. Then all parts of these pictures are recognized through the combination BOW model and the Opponent SIFT feature. And finally the results...
Recognition of dominant planes is an important task used in areas such as robot navigation, augmented reality, 3D reconstruction, among others. There are several approaches for recognizing planar structures, however, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds associated with the camera...
The aim of this paper is to develop a system that involves character recognition of Brahmi, Grantha and Vattezuthu characters from palm manuscripts of historical Tamil ancient documents, analyzed the text and machine translated the present Tamil digital text format. Though many researchers have implemented various algorithms and techniques for character recognition in different languages, ancient...
In this paper, we introduce a novel framework which applies known image features combined with advanced linear image representations for weed recognition. Our proposed weed recognition framework, is based on state-of-the-the art object/image categorization methods exploiting enhanced performance using advanced encoding and machine learning algorithms. The resulting system can be applied in a variety...
In this paper we present a novel descriptor and method for segmentation-based keyword spotting. We introduce Zoning-Aggregated Hypercolumn features as pixel-level cues for document images. Motivated by recent research in machine vision, we use an appropriately pretrained convolutional network as a feature extraction tool. The resulting local cues are subsequently aggregated to form word-level fixed-length...
In this paper, we propose a novel automatic traffic sign detection and recognition method. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost. Segmentation is implemented by the improved Grab cut via the detection information. Classification is defined as a multiclass categorization problem, which is solved by HOG feature and support vector machine...
We define Pathomics as the process of high throughput generation, interrogation, and mining of quantitative features from high-resolution histopathology tissue images. Analysis and mining of large volumes of imaging features has great potential to enhance our understanding of tumors. The basic Pathomics workflow consists of several steps: segmentation of tissue images to delineate the boundaries of...
In order to further improve the recognition rate and computing efficiency of modular 2DPCA in face recognition, an improved modular 2DPCA method based on image segmentation is proposed. Firstly, segmentation of threshold value optimization is utilized to segment face image of training samples into several non-overlapping sub-image spaces so that the pixel number has uniform distribution in each sub-image...
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.