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Human action recognition is a challenging task not only because of the factors like changes in intensity, background, etc but also because of the variability in the behavioural patterns among the objects in the image which in turn affects the recognition accuracy. Analyzing all those factors and identifying the action is termed as activity recognition. In this paper, we present an approach of activity...
TV logo recognition plays an important role in video content understanding. So far, Real-time TV logo recognition under complex background based on single frame is still a very challenge task. By analyzing the characteristics of TV logo, a new TV logo recognition algorithm based on multiple feature fusion via hierarchical matching was proposed in this paper. The recognition process consisted of three...
This paper proposed a fusion of color features, shape features and SURF image recognition algorithm. Traditional SURF algorithm take greyscale image as input to extract local extreme value points as characteristics points, ignoring color and shape information. The paper introduced the global color histogram information to make up the lack of color information loss, at the same time introduced shape...
This paper proposes a methodology for recognition of plant species by using a set of statistical features obtained from digital leaf images. As the features are sensitive to geometric transformations of the leaf image, a pre processing step is initially performed to make the features invariant to transformations like translation, rotation and scaling. Images are classified to 32 pre-defined classes...
The purpose of this experimental study is to elucidate how is the expression of illustrations giving a positive impression from the viewpoints of impression evaluation and psychophysiological indices. The experiment is carried out to explore the relationship between impression evaluation and psychophysiological indices obtained from biological signals. We analyze the latency and amplitude of EEG event-related...
Finding a method which allows a computer recognition to be close to human recognition is a goal of many works in the present. We have set this goal too. According to us, we need to find function for simple recognition of shapes in the images as first step of this goal. Result of this method provides input of our system of recognition. System form depends on the result of shape recognition method....
In this study, we propose a novel scene descriptor for visual place recognition. Unlike popular bag-of-words scene descriptors which rely on a library of vector quantized visual features, our proposed descriptor is based on a library of raw image data, such as publicly available photo collections from Google StreetView and Flickr. The library images need not to be associated with spatial information...
This paper addresses leaf images based visual tree search system called OKIRAKU Search. A user photographs an isolated leaf on a white background, and inputs the photographs to the system. The system automatically extracts the leaf region and computes shape features and color features, and searches it in the known species. The system shows the user the top matched species. The point of our approach...
In this paper, Krawtchouk invariant moments are used as features for object recognition. For hand images, the performance of Krawtchouk moments in terms of recognition accuracy, rotational invariance, scale invariance, computational time and feature vector size, has been analysed. A user independent dataset for 21 subjects under varying illumination conditions is created. A comparative analysis with...
In this paper, we propose to use Fourier descriptors (FD) for hand posture recognition in a vision-based approach. FD are widely used for shape representation and pattern recognition, they may also be well-adapted for hand posture recognition. The invariance properties of FD are discussed, and we provide a comparison of the performances with Hu moments. First, experiments are performed on the Triesch...
In this paper, we introduce a novel model for simultaneously segment and recognize object using shape prior information. Given a set of training shapes including many different object classes, the target shape in a test image is represented approximately as a sparse convex combination of the training shapes. The proposed model is optimal in the L2 criterion between the unknown true shape and the convex...
Recent development in depth sensors opens up new challenging task in the field of computer vision research areas, including human-computer interaction, computer games and surveillance systems. This paper addresses shape and motion features approach to observe, track and recognize human silhouettes using a sequence of RGB-D images. Under our proposed activity recognition framework, the required procedure...
Preprocessing and fusion techniques for finger vein recognition are investigated. An experimental study involving a set of preprocessing approaches shows the importance of selecting the appropriate single technique and the usefulness of cascading several different preprocessing methods for subsequent feature extraction of various types. Score level fusion is able to significantly improve recognition...
Sign language recognition (SLR) is considered a multidisciplinary research area engulfing image processing, pattern recognition and artificial intelligence. The major hurdle for a SLR is the occlusions of one hand on another. This results in poor segmentations and hence the feature vector generated result in erroneous classifications of signs resulting in deprived recognition rate. To overcome this...
This paper describes a method to recognize a Quick Response Code (QR) a novel. The QR Code is a two-dimensional code, which is currently used in various fields. According to the salient growth smartphone market, the recognition distance of QR Code is increased but the recognition angle is still limited. To tackle the issue, we propose a QR Code recognition method using ‘cloud-based pre-generated image...
Too much irregularity in shapes of intraclass images poises an intrinsic challenge for recognition of handwritten alpha-numeric characters. We propose, in this paper, a perimetric complexity based handwritten numeral recognition that is inspired from gestalt configural superiority effect. A point-wise correspondence is obtained from the matching of shape context using affine transformation. A set...
A method is presented for authenticating people on the basis of lip movement. It uses the kernel mutual subspace (KMS) method using fusion of canonical angles by kernel Fisher discriminant analysis. Its authentication accuracy is better than that of previously proposed lip-movement authentication methods when the distribution of lip images has a nonlinear structure. The similarity of KMS is canonical...
In general, it is difficult to construct an object recognition system, because such a system has many design variables and often these cannot be designed independently. However, in certain manufacturing tasks, it is not always necessary to design all variables. In this study, we selected a picking task as the target task for the experiment. We restricted the design variables to parameters of the preprocessing...
This research is to propose a fast and highly accurate object recognition method especially for fruit recognition applications to be used in a mobile environment. Conventional techniques are based on one or more of the basic features that characterize an object: color, shape, texture and intensity, causing performance or accuracy limitations in a mobile environment. Thus, this paper presents a combined...
Iris recognition system captures an image of an individual's eye. In addition, the process of segmentation, normalization and feature extraction is followed by the iris of an eye image in the system. Using the algorithms proposed by J. Daugman, Iris recognition system has significantly improved over the last decade, and it has been used in so many practical applications. However, some difficulties...
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