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In order to overcome the existing fruit recognition method only for single feature recognition which leads the problem of lower recognition rate, this paper proposes a recognition method based on multi-feature and multi-decision. Firstly, we preprocess the fruit image which is to be classified, separateing foreground and background, and then we divide the target area. Secondly, in order to take full...
Scene text recognition has attracted much attention in the research community. Many proposed scene text recognition methods adopt a step-by-step procedure, which includes a text extraction phase and a recognition phase. In this study, in order to eliminate the risk of text extraction error, we try to build a scene text recognition system that does not involve the text extraction phase. In our proposed...
Many research studies demonstrated that recognition based on ear biometrics offers an accuracy which is comparable to face trait, especially in controlled settings. Our proposal is to exploit it to avoid the problem of newborn swap, which is possible and actually happens, most of all in crowded maternity wards of big hospitals. We tested the viability of this solution using a dataset of ear images...
The numismatics community undergoes an important change toward a digital archiving of coin trades. Transactions of historical coins are tracked in many catalogues and the digitization of such content is of great interest for augmenting experts work in estimating a coin value and preventing illegal sales. In order to recognize and index coin pictures in transactions archives, a segmentation step is...
This paper presents a fresh food recognition system that utilizes the feature fusion extracted from food images captured from optical fibers embedded inside a chopping board. We exploit both local and global features including color, SURF and shape for image representation. In addition, we propose cost-based schemes for feature matching and the Borda count method for feature fusion. An experiment...
In this paper a new perspective is provided on the re-ranking problem, which is essential in pattern recognition and computer vision tasks. Items are efficiently organized using the minimal spanning tree (MST) and the orthogonal-MST graph and their similarity is calculated through an appropriate graph traversal method. The graph is augmented consecutively providing alternative paths, however not escaping...
Classic shape context algorithm uses the correspondence points between contour points. Those points represent the outline of a shape characteristic. Select a different position and number of sampling points to produce different effect on the similar shape feature description. Uniform random sampling algorithm can not solve the problem of selective retention for classic shape context with similar characters...
Categorizing free-hand human sketches has profound implications in applications such as human computer interaction and image retrieval. The task is non-trivial due to the iconic nature of sketches, signified by large variances in both appearance and structure when compared with photographs. One of the most fundamental problems is how to effectively describe a sketch image. Many existing descriptors,...
Human action recognition has been one of the most challenging topics in computer vision during the last decade. This paper presents a novel approach for recognizing view independent human actions based on analysis of Fourier transform and Radon transform of self similarity matrix of features obtained from the action. The proposed feature descriptor is extracted from human point cloud over the time...
Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification...
This paper discusses the potentials and challenges of the recognition of individual tree patterns in image data acquired by decimeter-resolution airborne synthetic aperture radar (SAR) systems working in the millimeterwave domain. Due to the different characteristics of conventional optical imagery and side-looking SAR imagery, sophisticated processing strategies have to be developed, which need to...
Within the framework of a smartphone-based application, helping people to identify plant species in the wild, a sub-classifier strategy has been introduced. It aims at recognizing the botanical properties of a leaf, relatively to various global and local shape criteria used in flora books. A decision function is applied on these classified shape categories to produce a final decision on the species...
We propose a novel approach for learning image representation based on qualitative assessments of visual aesthetics. It relies on a multi-node multi-state model that represents image attributes and their relations. The model is learnt from pair wise image preferences provided by annotators. To demonstrate the effectiveness we apply our approach to fashion image rating, i.e., comparative assessment...
A new method for mango detection is presented in this paper. This method is based on preprocessing operators on image which includes converting to gray image, finding edges, calculating distances to edges, opening morphology and converting to binary color image. To take advantage of oval shaped mango fruit, we apply Randomized Hough Transform method to detect potential places for mango fruit in input...
A new method of weed recognition based on the invariant moments was proposed in this paper. Firstly, the area of the soybean leaf was located from the complicated image background. Secondly, the features of soybean leaf were obtained by Hu invariant moments, which are the invariability of the translation, the ratio and the rotation, and have lower computational complexity. Finally, the soybean leaf...
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...
Picking of parts loaded in bulk is an industrial need. Thus bin-picking systems for various objects have ever been studied by various ways. However, it is difficult to recognize coil springs randomly placed in a pile by conventional machine vision techniques because of their shape characteristics. In this paper, we propose a method of recognition and pose estimation of coil springs. This method uses...
Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, a novel leaf image recognition technique via sparse representation is proposed for automatic plant identification. In order to model leaf images, we learn an overcomplete dictionary for sparsely representing...
In this paper, we present a robust approach of automatic detection and recognition of road signs in national roads, starting from the images resulting from a video stream taken by a camera embarked on a vehicle. Our approach is composed of three main phases: the first phase is to extract video stream images containing a circle or a triangle. This extraction is performed respectively by Hough transformation...
This paper presents an approach for automatic recognition of vehicle make from its logo in a front-view image using SIFT descriptor of interior structure and back-propagation neural network. The proposed method focuses on recognition of automobile make by integrating Top-Hat transformation with shape descriptor to locate the logo of an automobile from an image then uses back-propagation neural network...
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