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The acumen and sophistication of consumers have created the increasing expectation for improved quality in food products. This in turn has boosted the need for enhanced quality monitoring [1]. The aim of this project is to develop an efficient and adaptable algorithm to accurately monitor the quality of packaged cheeses. Computer vision was used to distinguish unqualified cheeses from a large number...
The method of extracting characteristic parameters of lip according to lip template was presented. A dynamic clustering algorithm to classify the lip-shape based on the criteria of the least square error sum was proposed, and dynamic state sequence that described the lip movement was obtained by the improved ant colony algorithm. The lip-reading dynamic pattern recognition was performed by DTW algorithm...
The objective of the present study is to develop an automatic tool to identify and classify the different types of cocci bacterial cells in digital microscopic cell images using active contour method. Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Geometric features are used to identify the arrangement...
In many situations, we have to read characters on the object we are inspecting. Typically, the character text is horizontal, in this case, we can recognize the character easily, and the recognition methods are very mature. But, sometimes the characters distribute on the circle or ellipse, and the characters are distortion, which bring us huge trouble to recognize. This paper main about how to read...
This paper presents the results of an all-day-long pedestrian classification system based on an AdaBoost cascade meta-algorithm. The underlying idea is to use a Haar-features-based AdaBoost together with an ad-hoc-features-based AdaBoost system in order to reach a better pedestrian classification. A specific night-time pedestrian classification is developed in order to obtain a system that can be...
One of main tasks in machine vision is able to provide understandable descriptions of objects. A method described here is based on fast Fourier descriptors, which initially incorporated with classic image processing operation can describe shape information in feature space. Fourier descriptor based on chain code and Fourier Transform method is discussed and related invariant property is derived. Experimental...
In this paper, we explore the idea of using only pose, without utilizing any temporal information, for human action recognition. In contrast to the other studies using complex action representations, we propose a simple method, which relies on extracting “key poses” from action sequences. Our contribution is two-fold. Firstly, representing the pose in a frame as a collection of line-pairs, we propose...
In this paper, we have introduced a hierarchical object categorization method with automatic feature selection. A hierarchy obtained by natural similarities and properties is learnt by automatically selected features at different levels. The categorization is a top-down process yielding multiple labels for a test object. We have tested out method and compared the experimental results with that of...
High precision identification of feature points is an important technology and one of the bases of computer vision, image analysis and image processing. In the practical applications, the feature points can not be identified easily in various conditions of light illumination, visual angle, texture, and perspective projection. And in many cases, the size of feature point is small, the context is uncertain...
A new algorithm for apple shape classification using level set and motion estimation was proposed. At first, a standard class shape apple images database was construct by expert, and then the level set representations according to signed distance transforms were used, which are a simple, robust, rich and efficient way to represent shapes; second, the unknown shape class apple was aligned to the standard...
A new method to obtain high quality skeletons of binary shapes is proposed in this paper. First, a small set of salient contour points is computed by Discrete Curve Evolution (DCE). These salient points are the stable endpoints of the skeleton. Second, the skeleton is grown between pairs of the endpoints. Examining every eight-connected point of the current skeleton points, Select the point, that...
This paper presents a fuzzy KNN and Bayesian decision based classification method for determining whether a 3D object belongs to human class. To achieve efficiency and simplicity, the view having maximum area is used to substitute a 3D shape, which is front-side view for human models. View features and structural features are utilized to describe those selected views. For shape feature can not distinguish...
Statistical methods of shape and appearance are powerful tools used in computer vision for near-correct interpretation of images. In this paper, we present a method for classifying facial expressions based on the extracted features of facial components. The face, the window to the inner self of an individual can be analyzed for outright expressions like sadness, happiness, anger, surprise, disgust...
Several fruit recognition techniques are developed based upon color and shape attributes. However, different fruit images may have similar or identical color and shape values. Hence, using color features and shape features analysis methods are still not robust and effective enough to identify and distinguish fruits images. A new fruit recognition system has been proposed, which combines three features...
We propose a novel learning algorithm, called Bagging-Adaboost ensemble algorithm with floating search algorithm post optimization, for object detection that uses local shape-based feature. The feature use the chamfer distance as a shape comparison measure. It can be calculated very quickly using a look-up table. Random sampling boosting algorithm is used to form an object detector. Floating search...
The new automatic classification devices for products based on machine vision techniques value the shape and color parameters so as to suitably assess the latter. These parameters may also be turned to good account by the machine vision techniques that proved to be applicable in several domains. One can especially apply these techniques in the inspection and analysis systems of industrial products...
Researches throughout the past few years, having as a goal the automatic classification of products, via calculus systems implementation, as well as machine vision techniques, and artificial intelligence field methods, have lead to very promising results. Using them allowed for the assessment of some parameters such as shape, colour and the integrity degree of the products analyzed, having much more...
Surface texture classification is an important aspect of computer vision and a well studied problem. In this paper, we greatly increase speed for texture classification while maintaining accuracy. We take inspiration form past work and propose a new method for texture classification which is extremely fast due to the low dimensionality of our feature space. We extract distinctive features at a very...
Object recognition may be a hard computer vision task under severe occlusion circumstances. This problem is efficiently solved in this paper through a new 3D recognition method for free-form objects. The technique uses the Depth Gradient Image Based on Silhouette representation (DGI-BS) and settles the problem of identification-pose under occlusion and noise requirements. DGI-BS synthesizes both surface...
This paper describes a new approach to the use of particle swarm optimisation (PSO) for object classification problems. Instead of using PSO to evolve only a set of good parameter values for another machine learning method for object classification, the new approach developed in this paper can be used as a stand alone method for classification. Two new methods are developed in the new approach. The...
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