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The objective of this research is to build an automatic method for recognizing a Thai herb flower based on the Minimum Distance Method. The herb flower images, acquired from a digital camera, are taken in the real environment. We use the characteristics of herb flowers to design our classification algorithms, which consist of the average red, green and blue (RGB) colors, the herb flower size and the...
Three-dimensional (3D) imaging has attracted considerable attention recently due to its increasingly wide range of applications. Consequently, perceived quality is a great important issue to assess the performance of all 3D imaging applications. Perceived distortion and depth of any stereoscopic images are strongly dependent on the local features, such as edge, flat and texture. In this paper, we...
This paper presents a fast method for detecting multi-view cars in real-world scenes. Cars are artificial objects with various appearance changes, but they have relatively consistent characteristics in structure that consist of some basic local elements. Inspired by this, we propose a novel set of image strip features to describe the appearances of those elements. The new features represent various...
We present a novel classification scheme which uses partial object information that is selected adaptively using modified distance transform and represented as moment invariants (Hu moments) to compensate for scale, translation and rotational transformation(s). The moment invariants of different parts of an object are learned using AdaBoost algorithm [1]. The classifier obtained using the proposed...
In this paper, a colour text/graphics segmentation is proposed. Firstly, it takes advantage of colour properties by computing a relevant hybrid colour model. Then an edge detection is performed to construct a binary image composed of contour information. From this contour image, connected components are classified according to a graph representation. Text and graphic diversity is taken into account...
Classification procedure aims at finding regions of the classes in the feature space. There are several algorithms proposed with supervised and unsupervised strategies for classification in literature. This paper goes on to propose a supervised method of classification using information slicing. Information lies in the feature space of the data to be classified. Training stage consists of slicing...
Object recognition and classification in a multi-environment is an important part of machine vision. The goal of this paper is to build a system that classifies the objects of interest plane and helicopter. This paper addresses the issues to classify objects by combining pose and viewpoint invariant to certain extend. The threshold technique with background subtraction is used to segment the background...
In this paper, we present a robust system to accurately detect and localize texts in natural scene images. For text detection, a region-based method utilizing multiple features and cascade AdaBoost classifier is adopted. For text localization, a window grouping method integrating text line competition analysis is used to generate text lines. Then within each text line, local binarization is used to...
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this as a structure learning task and our strategy is to learn and combine basic POM's that make use of complementary image cues. Each POM has algorithms for inference and parameter learning, but: (i) the structure of each POM...
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